Table of Contents
Overview
Definition and Scope
Computational chemistry is defined as the branch of chemistry that employs computer-based methods to solve quantum mechanics equations, thereby addressing various problems in chemistry, including the prediction of molecular structure, properties, and reactivity patterns.[5.1] This field has evolved significantly, emerging as a natural extension of theoretical chemistry, particularly due to advancements in computer technology.[1.1] The scope of computational chemistry encompasses a variety of methodologies, including ab initio, semi-empirical, and density functional theory (DFT) methods, which are essential for understanding reaction mechanisms.[4.1] The development of computational chemistry can be categorized into three distinct eras, each characterized by varying degrees of reliance on theoretical insights and computational power.[2.1] Historically, the foundational work in computational quantum chemistry can be traced back to the theoretical calculations performed by Walter Heitler and Fritz London in 1927, which were pivotal in shaping the field.[3.1] As computational chemistry continues to advance, it plays a crucial role in applications such as green chemistry, where it aids in designing synthesis strategies that prioritize atom economy and energy efficiency.[4.1]Importance in Modern Chemistry
Computational chemistry is pivotal in modern chemistry, enabling precise analysis of complex chemical systems. The field has been transformed by advancements in computational power and algorithms, enhancing molecular modeling and simulations [7.1]. For example, density functional theory (DFT) remains a cornerstone, with innovations like Accelerated DFT significantly speeding up simulations [6.1]. These advancements allow chemists to optimize processes and design new molecules and materials efficiently. Machine learning (ML) has further revolutionized the field by improving predictions of thermodynamic and kinetic properties, such as activation energies and Gibbs free energies, thus accelerating materials discovery and optimizing reaction conditions in both academic and industrial settings [8.1]. The integration of advanced software tools and accurate force fields has facilitated progress in theoretical and computational chemistry, enabling detailed investigations of biological systems at the molecular level [10.1]. Moreover, computational chemistry is crucial for developing sustainable chemical processes. It provides a framework for predicting the reactivities of chemical reactions, highlighting the synergy between computational methods and experimental approaches [12.1]. This integration has led to advancements in sustainable chemistry, guiding experimental research towards environmentally friendly practices [15.1]. The principles of green chemistry, advocating for the design of benign substances and processes, are increasingly supported by computational methodologies [16.1]. Historically, computational chemistry has evolved significantly since its early 20th-century inception, with foundational contributions from pioneers like Walter Heitler and Fritz London [22.1]. The field matured through powerful algorithms and theoretical methods, establishing itself as a principal area of chemistry by the late 20th century [24.1]. The emergence of machine learning and quantum chemistry methods represents a paradigm shift in solving the Schrödinger equation, enhancing our understanding of chemical systems [26.1]. As computational paradigms continue to evolve, they present opportunities and challenges that will shape the future of chemical research [27.1].In this section:
History
Early Developments
The early developments in computational chemistry were significantly influenced by the pioneering work of Walter Heitler and Fritz London in the early 20th century. Their introduction of the Heitler-London theory provided a foundational framework for understanding chemical bonding through quantum mechanics. This theory constructs a total molecular wavefunction from the constituent atoms, which has remained relevant in chemistry even after the advent of more advanced quantum theories. One of the major advantages of the molecular orbital (MO) theory, which evolved from their work, was its computational feasibility and scalability, allowing chemists to tackle complex molecular systems more effectively.[45.1] The 1980s marked a period of remarkable growth in computational chemistry, largely driven by the introduction of software such as Gaussian. Initially released in 1970 by John Pople and his research group, Gaussian became a crucial tool for computational chemists, facilitating molecular modeling and electronic structure calculations. However, the adoption of such computational tools was not without challenges. Chemists faced significant hurdles related to software modifications, which often required the full attention of domain experts to address low-level translation and integration tasks. These challenges impacted the sustainability, maintenance, adaptability, and extensibility of early software investments.[59.1] Moreover, the complexity of quantum mechanics posed practical challenges in simulating quantum systems on classical computers. Despite the introduction of various approximation methods, researchers struggled to fully address the intricacies of quantum behavior. The advent of quantum computing has since opened new pathways for overcoming these challenges, although practical implementations remain in their infancy and have yet to surpass classical computers for useful computations.[63.1]Emergence as a Discipline
The emergence of computational chemistry as a distinct discipline is rooted in the historical development of theoretical chemistry and the rapid advancements in computer technology. The field can be traced back to significant milestones in theoretical chemistry, which laid the groundwork for computational methods. Notably, the first theoretical calculations in chemistry were conducted by Walter Heitler and Fritz London in 1927, marking a pivotal moment in the integration of quantum mechanics into chemical analysis.[3.1] The evolution of computational chemistry is often categorized into three distinct eras, each characterized by varying degrees of reliance on theoretical insights, experimental data, and computing power. This classification highlights the interplay between theoretical advancements and the capabilities of computational resources throughout the field's history.[2.1] The 1980s, in particular, witnessed remarkable growth in computational chemistry, largely driven by the development of sophisticated software tools such as Gaussian, which was created by Walter Kohn and John Pople, both of whom received Nobel Prizes for their contributions.[72.1] The integration of quantum chemistry principles into computational software has enabled researchers to predict the properties of complex chemical systems effectively. This integration has been facilitated by advances in theory, applied mathematics, and computer science, which have collectively propelled the field forward.[50.1] Furthermore, the emergence of new computational paradigms, particularly those associated with exascale technologies, presents both opportunities and challenges for the future of computational chemistry.[50.1]In this section:
Theoretical Foundations
Quantum Mechanics in Computational Chemistry
Quantum mechanics serves as a foundational pillar in the field of computational chemistry, significantly influencing the development of theoretical and computational methods. The origins of computational chemistry can be traced back to the early theoretical calculations conducted by Walter Heitler and Fritz London in 1927, which were pivotal in establishing the principles of quantum mechanics in chemical contexts.[3.1] The Schrödinger equation, a central element in quantum mechanics, remains crucial in computational chemistry, guiding the development of methods aimed at solving complex chemical problems.[96.1] The theoretical framework of computational chemistry is primarily built upon molecular quantum mechanics and classical and quantum statistical mechanics.[91.1] This framework allows for the accurate computation of the electronic structure of multi-atom molecules and nanomolecular assemblies, thereby enhancing our understanding of chemical systems.[91.1] As computational techniques have evolved, the integration of quantum mechanical simulations has become increasingly sophisticated, enabling researchers to predict chemical characteristics and reactions with high precision.[98.1] The historical progression of computational chemistry can be categorized into three distinct eras, each characterized by varying degrees of reliance on theoretical insights and computational power.[2.1] This evolution reflects the growing importance of quantum mechanics in addressing complex chemical questions and facilitating advancements in drug design and materials research.[98.1] As computational methods continue to advance, the role of quantum mechanics in computational chemistry is expected to expand further, particularly with the advent of technologies such as quantum computing and machine learning, which promise to revolutionize the field.[98.1]Methods And Techniques
Density Functional Theory (DFT)
Density Functional Theory (DFT) is a prominent computational method within quantum chemistry, specifically designed for predicting the electronic structure and properties of molecules and materials. It has gained recognition for its ability to provide accurate results while maintaining computational efficiency, making it a favored choice among researchers in the field.[140.1] DFT operates on the principle that the properties of a many-electron system can be determined by the electron density rather than the many-body wave function, which simplifies calculations significantly. The development of DFT has been influenced by the need to address the complexities associated with electron correlation effects, which are critical for accurately describing the electronic structure of molecules.[140.1] By focusing on the electron density, DFT allows for a more manageable approach to solving the Schrödinger equation, which is central to computational chemistry.[138.1] This method has proven particularly effective in various applications, including the study of molecular interactions, reaction mechanisms, and material properties. DFT's evolution has been marked by the introduction of various approximations and functionals that enhance its accuracy and applicability. These advancements have enabled researchers to explore complex chemical systems with unprecedented precision, thereby expanding the scope of computational chemistry.[140.1] As a result, DFT continues to be a vital tool for chemists seeking to understand and predict the behavior of molecular systems in both theoretical and practical contexts.Applications
Drug Design and Medicinal Chemistry
Computational chemistry plays a pivotal role in drug design and medicinal chemistry by enhancing the efficiency and effectiveness of the drug development process. One of the primary applications is in drug discovery, where computational methods accelerate the identification of promising drug candidates by predicting their interactions with biological targets. This predictive capability allows researchers to focus on the most viable candidates early in the research process, thereby streamlining the overall development timeline and reducing costs associated with drug design.[169.1] The integration of artificial intelligence (AI) and machine learning (ML) into computational chemistry has further transformed drug design. These technologies facilitate virtual screening, optimize lead compounds, and predict molecular interactions with greater accuracy and speed.[181.1] For instance, AI algorithms have been instrumental in molecular docking studies, which have led to the discovery of novel antifungal agents by predicting their binding modes with fungal enzymes.[179.1] Moreover, AI's ability to analyze complex datasets has significantly improved the efficiency of drug characterization, target discovery, and validation processes.[182.1] In addition to enhancing the drug discovery process, computational chemistry contributes to various aspects of drug design, including target selection and lead optimization. Methodological advancements in computational techniques have been crucial in this regard, allowing for a more systematic approach to drug development.[178.1] Furthermore, the use of computational models and simulations enables researchers to conduct rapid screening and testing of new materials, which is essential for the timely development of effective medications.[169.1]Materials Science and Engineering
Computational chemistry plays a pivotal role in materials science and engineering, particularly in the development of next-generation energy storage materials. The integration of computational techniques with experimental methods has significantly enhanced the discovery and optimization of materials for energy applications. For instance, computational material design (CMD) utilizes quantum mechanical simulations, density functional theory, and machine learning to correlate electronic structural attributes with the physical and chemical properties of materials, driving advancements in sustainable energy catalysis and storage.[184.1] The Materials Genome Initiative (MGI) exemplifies the collaborative potential of computational chemistry and experimental techniques. By facilitating instantaneous communication of code, experimental results, and simulation outputs among researchers, the MGI aims to accelerate the pace of discovery in materials science, positioning computational science alongside synthesis, analytical theory, and experimentation as essential components of an integrated approach to material development.[185.1] This collaborative framework is crucial for addressing the challenges associated with improving the efficiency of electrochemical energy storage systems, where atomic-scale modeling can predict and optimize the performance of new materials.[190.1] Atomic and molecular modeling techniques have evolved significantly, enabling researchers to understand and predict material properties across various applications, including battery chemistry.[191.1] In the context of energy storage, specific properties such as ion intercalation dynamics, crystallographic defects, and electrochemical degradation mechanisms are critical areas of focus. Research efforts have included the construction of both physics-based and data-driven models to study these phenomena, often in collaboration with experimental groups to enhance mechanistic understanding and optimize electrode microstructures.[193.1] Moreover, advancements in computational methods have allowed for the exploration of high-voltage cathode materials, such as LiNixMnyCo1−x−yO2 and LiCoO2, which are essential for developing more stable and efficient batteries. However, a comprehensive understanding of the cathode-electrolyte interphases (CEIs) is necessary to improve the stability of these materials under operational conditions.[192.1] The combination of computational insights and experimental characterization techniques, including X-ray photoelectron spectroscopy and electron microscopy, is vital for elucidating the mechanisms governing material performance in energy storage applications.[192.1]In this section:
Recent Advancements
Machine Learning in Computational Chemistry
Recent advancements in machine learning (ML) have significantly transformed the field of computational chemistry, particularly in drug discovery and materials science. The integration of AI-driven techniques has enhanced predictive modeling, allowing for more accurate simulations of chemical processes and the properties of new compounds. For instance, machine learning has improved the accuracy of predicting thermodynamic and kinetic properties, such as activation energies and Gibbs free energies, which accelerates materials discovery and optimizes reaction conditions in both academic and industrial applications.[220.1] Moreover, the application of ML techniques in computational chemistry has streamlined the discovery and optimization of new materials. By utilizing vast datasets and complex mathematical models, researchers can now predict the viability of previously unreported chemistries and guide all stages of the materials discovery workflow, from quantum-chemical calculations to materials synthesis.[223.1] This shift from traditional manual and human-intensive methods to automated and iterative processes driven by AI has enabled a more efficient and rapid exploration of chemical spaces.[224.1] The role of AI in computational chemistry is further underscored by its ability to analyze large volumes of data, thereby minimizing human intervention and accelerating scientific progress.[225.1] As AI technologies continue to mature, they are expected to work in concert with traditional computational methods, creating heterogeneous workflows that enhance the overall efficiency of drug development and materials discovery.[224.1] This collaborative approach is poised to break through long-standing bottlenecks in research and development, heralding a new era in computational chemistry where the synergy of various technologies yields results greater than the sum of their parts.[224.1]Innovations in Molecular Simulations
Recent advancements in molecular simulations have significantly enhanced the capabilities of computational chemistry, particularly in the context of drug discovery. Molecular dynamics (MD) simulations have emerged as a fundamental tool for capturing the dynamic aspects of protein structure, function, and ligand interactions with remarkable detail. These simulations allow researchers to analyze inter-molecular and intra-molecular interactions that influence the stability of biomolecules and molecular complexes, thereby providing insights into the conformational diversity of ligand binding pockets.[214.1] The integration of molecular docking and MD simulations has proven to be particularly effective in predicting binding modes, binding affinities, and the stability of various protein-ligand systems. Recent advancements in algorithms and computational power have made MD simulations a critical component in investigating bio-molecular assemblies, enhancing the accuracy and efficiency of predicting drug-target interactions compared to traditional methods.[214.1] Furthermore, the development of deep learning-based scoring functions has further refined molecular docking techniques, allowing for improved predictions of ligand orientation and binding efficiency.[215.1] Additionally, the application of computational quantum chemistry, particularly through methods such as Density Functional Theory (DFT), has provided researchers with powerful tools to predict electronic structures and properties of molecules. This has enabled a more profound understanding of molecular properties and reactions, which is essential for rational drug design.[205.1] The combination of these advanced computational methods has revolutionized the drug discovery process, allowing for the rapid identification and optimization of new therapeutic candidates while significantly reducing the time and costs associated with traditional experimental approaches.[229.1]In this section:
Challenges And Future Directions
Computational Limitations
Computational chemistry faces several significant limitations that hinder its full potential in molecular science. One of the primary challenges is the complexity of molecular systems, which complicates the development of precise approximations necessary for accurate modeling. This complexity is compounded by the need for standard computational methods that can be universally applied across various types of molecular systems.[246.1] Moreover, the design of enzymes for specific reactions remains a formidable task, as current computational techniques struggle to accurately predict the behavior of these complex biological catalysts.[246.1] Additionally, researchers are often constrained by the availability of computational resources, which can limit the scope and scale of their studies.[246.1] Recent advancements in computational techniques, particularly those integrating artificial intelligence (AI) and machine learning (ML), have shown promise in addressing some of these limitations. These technologies enable chemists to analyze vast datasets more efficiently, optimize chemical processes, and design new molecules with greater speed and accuracy.[252.1] However, the integration of AI and ML into computational chemistry also presents its own set of challenges, particularly in terms of scalability and the need for models that can reliably predict chemical properties based on quantum mechanics.[258.1] Furthermore, the emergence of quantum computing offers a transformative potential for overcoming traditional computational limitations. Hybrid quantum-classical algorithms, which combine the strengths of both quantum and classical computing, are being explored to tackle complex problems in computational chemistry. These methods aim to address the high computational costs associated with quantum chemistry calculations, although they still face significant challenges in implementation and efficiency.[262.1]Integration with Experimental Chemistry
The integration of computational chemistry with experimental chemistry is increasingly recognized as essential for advancing chemical research and discovery. Theoretical and computational chemistry provides critical insights into molecular structures, characteristics, and reactivities, which are vital for understanding chemical systems and applying them to drug design and materials research.[244.1] This integration allows researchers to leverage quantum mechanical simulations, molecular dynamics, and machine learning applications to predict chemical characteristics and reactions with high precision.[244.1] Recent advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the field of computational chemistry, enabling chemists to analyze vast amounts of data, optimize chemical processes, and design new molecules and materials with remarkable speed and accuracy.[256.1] The incorporation of AI-driven instrumentation is expected to further enhance the discovery process by facilitating the exploration of chemical space based on quantum chemical calculations.[248.1] As the number of structures to be screened increases, computational calculations become impractical due to high costs; thus, AI techniques can learn patterns from existing data to streamline this process.[255.1] Moreover, the future of computational chemistry will be characterized by the integration of cutting-edge technologies such as quantum computing and advanced simulation techniques, which will play a critical role in deciphering the complexities of chemical systems.[244.1] However, challenges remain, particularly in ensuring the accuracy of predictions made by machine learning models. Issues such as the loss of accuracy when reconstructing properties from machine learning potentials (MLPs) and the difficulties in learning electrostatics and dispersion interactions must be addressed.[257.1] Standardization in computational methods is also crucial for enhancing collaboration and reproducibility in research across different disciplines. By actively participating in standardization efforts, research projects can achieve greater visibility and trust, ultimately leading to a more significant impact on the integration of computational and experimental chemistry.[266.1] The quality of input data and adherence to established standards will set an upper limit on the value of the outputs generated from computational chemistry, emphasizing the need for well-defined tolerances for accuracy and precision.[267.1]In this section:
References
[1] The Nobel history of computational chemistry. A personal perspective ... — A historical account of the development of computational chemistry must by necessity include a summary of the major milestones in the history of theoretical chemistry. As noted previously, computational chemistry was a natural outgrowth of theoretical chemistry because of the rapid development of computers.
[2] Brief history of computational chemistry: Three distinct eras and the ... — Brief history of computational chemistry: Three distinct eras and the relative importance of theoretical insights and computing power in advancing the field [PeerJ Preprints] "PeerJ Preprints" is a venue for early communication or feedback before peer review. Brief history of computational chemistry: Three distinct eras and the relative importance of theoretical insights and computing power in advancing the field 10.7287/peerj.preprints.365v4 For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited. Using temporal relationships between methods as guide, placing the various methods along a time line clusters the methods into three distinct eras, each defined by their relative reliance on theory, approximations, experiment data and computing power for problem solution. From PeerJ Content Alert Emails
[3] Computational chemistry - chemeurope.com — History. Building on the founding discoveries and theories in the history of quantum mechanics, the first theoretical calculations in chemistry were those of Walter Heitler and Fritz London in 1927.The books that were influential in the early development of computational quantum chemistry include: Linus Pauling and E. Bright Wilson's 1935 Introduction to Quantum Mechanics - with
[4] Computational Chemistry - an overview | ScienceDirect Topics — Computational Chemistry - an overview | ScienceDirect Topics Computational Chemistry From: Theoretical and Computational Chemistry, 2005 Computational Chemistry for Photosensitizer Design and Investigation of DNA Damage Computational chemistry is a basic tool for understanding reaction mechanisms and is therefore very important in green chemistry, in which the design of a strategy for the synthesis of a chemical compound is subject to requirements such as atom economy and energy efficiency. Computational chemistry has made a significant contribution to the understanding of the mechanism of asymmetric hydrogenation by homogeneous transition metal catalysis, and its main contributions are discussed in this chapter. There are different methods suitable in computational chemistry, such as ab initio, semiemprical, and density functional theory (DFT) methods. Computational and Theoretical Chemistry
[5] PDF — Computational (not analytical) chemistry is the branch of chemistry that uses computers to solve quantum mechanics equations to solve problems in chemistry such as predicting the structure, properties, and patterns of reactivity of molecules.
[6] Revolution of Artificial Intelligence in Computational Chemistry ... — The field of computational chemistry is one of many sectors that artificial intelligence (AI) has revolutionized in recent years. Chemists are now more equipped to analyze enormous volumes of data, optimize chemical processes, and design new molecules and materials with high speed and accuracy because of advancements in machine-learning (ML) approaches, hardware platforms, and algorithms. This
[7] Acceleration without Disruption: DFT Software as a Service — Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power and theoretical methods. This paper introduces a novel, cloud-native application, Accelerated DFT, which offers an order of magnitude acceleration in DFT simulations. By integrating state-of-the-art cloud
[8] Advancements in Machine Learning Predicting Activation and Gibbs Free ... — Machine learning has revolutionized computational chemistry by improving the accuracy of predicting thermodynamic and kinetic properties like activation energies and Gibbs free energies, accelerating materials discovery and optimizing reaction conditions in both academic and industrial applications.
[10] Frontiers | From System Modeling to System Analysis: The Impact of ... — The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail.
[12] Computation and Experiment: A Powerful Combination to Understand and ... — The selected examples showcase the ability of computational chemistry to rationalize and also predict reactivities of broad significance. A particular emphasis is placed on the synergistic interplay of computations and experiments.
[15] Computational chemistry and green chemistry: Familiarizing chemistry ... — The principles of green chemistry entail the design of substances and processes that are inherently benign to human health and to the environment (benign-by-design concept). Computational chemistry constitutes a major resource for the design of molecules having desired properties.
[16] Green chemistry and computational chemistry: A wealth of promising ... — Green chemistry is defined as the design of chemical products and processes to reduce or eliminate the use and generation of hazardous substances (Anastas et al., 1996). Its principles (Anastas and Warner, 1998) envisage the design of substances and production processes that are benign (not harmful) for human health and the environment.
[22] Computational Chemistry Introduction - DiPhyx Stories — Computational chemistry has evolved significantly since the early 20th century. Early developments by pioneers such as Walter Heitler and Fritz London using valence bond theory laid the groundwork for the field. Over the decades, the advent of digital computing has enabled increasingly complex quantum mechanical calculations, beginning with
[24] The Nobel history of computational chemistry. A personal perspective ... — Initially, the capabilities of computational chemistry were very modest, but by the end of the 20th century computational chemistry was established as one of the principal areas of chemistry. The evolution of computational chemistry resulted from a combination of advances in theoretical methods, the development of powerful algorithms and
[26] Combining Machine Learning and Computational Chemistry for Predictive ... — Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working
[27] A Perspective on Sustainable Computational Chemistry Software ... — The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a
[45] From Hartree-Fock and Heitler-London to chemical orbitals — For chemistry the theoretical representation of the forces connecting atoms in molecules was and is a central problem. The Atomic Orbital and the Molecular Orbital are basic building blocks in the Heitler-London (HL) and in the Linear Combination of Atomic Orbitals-Molecular Orbital (LCAO-MO) methods, which have lead to the construction of modern Valence Bond and Hartree-Fock methods
[50] A Perspective on Sustainable Computational Chemistry Software ... — The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a
[59] Gaussian (software) - Wikipedia — Gaussian / ˈ ɡ aʊ s. i. ə n / is a general purpose computational chemistry software package initially released in 1970 by John Pople and his research group at Carnegie Mellon University as Gaussian 70. It has been continuously updated since then. The name originates from Pople's use of Gaussian orbitals to speed up molecular electronic structure calculations as opposed to
[63] Quantum Chemistry in the Age of Quantum Computing — Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging and complex
[72] Computational chemistry software that's the cat's whiskers - Research ... — Professor Pople was instrumental in the development of one of the most respected and widely used, general purpose computational chemistry software packages — Gaussian. Gaussian has undergone continuous development for more than 40 years, culminating in the latest release of Gaussian 16 and the accompanying helper application, GaussView 6.
[91] Chemical theory and computation - PMC — The theoretical pillars of theoretical chemistry are molecular quantum mechanics and classical and quantum statistical mechanics. Computational quantum chemistry has developed to the point where the electronic structure of many-atom molecules and nanomolecular assemblies can be readily and accurately computed.
[96] Brief history of computational chemistry: Three distinct eras and the ... — Schrodinger equation occupies central place in computational chemistry, where the focus was, and will continue to be, the development of methods for its solution.
[98] Frontier of Computational and Theoretical Chemistry — Theoretical and computational chemistry provide critical insights into molecular structures, characteristics, and reactivities, allowing us to better understand chemical systems and apply them to drug design and materials research (Lin, 2010; Morales-Navarro et al., 2019). The incorporation of quantum mechanical simulations, molecular dynamics, and machine learning applications into computational chemistry has greatly enhanced the science, allowing researchers to predict chemical characteristics and reactions with high precision. These technologies provide a view into the future of chemistry research, in which quantum computers and advanced simulation techniques will play a critical role in deciphering the intricacies of chemical systems. Finally, the future of theoretical and computational chemistry will be defined by the integration of cutting-edge technologies like as artificial intelligence, quantum computing, and machine learning to address difficult chemical problems and improve prediction skills.
[138] PDF — Computational Quantum Chemistry: Focuses specifically on equations and approximations derived from the postulates of quantum mechanics. Solve the Schrödinger equation for molecular systems. Ab Initio Quantum Chemistry: Uses methods that do not include any empirical parameters or experimental data.
[140] PDF — Abstract: Computational quantum chemistry has emerged as a powerful tool for understanding molecular properties and reactions, enabling researchers to explore complex chemical systems with unprecedented accuracy and efficiency. Keywords: computational chemistry, quantum chemistry, electronic structure theory, density functional theory, ab initio methods, applications, future directions 1. 3.2 Density Functional Theory (DFT) Density Functional Theory (DFT) is a powerful computational method in quantum chemistry for predicting the electronic structure and properties of molecules and materials. Journal of Systems Engineering and Electronics (ISSN NO: 1671-1793) Volume 34 ISSUE 5 2024 PAGE NO: 209 5.2 Treatment of Electron Correlation Effects The treatment of electron correlation effects is a critical aspect of computational quantum chemistry, as electron-electron interactions significantly influence the electronic structure and properties of molecules.
[169] Computational chemistry applications - Schrödinger — Computational chemistry applications - Schrödinger An in-depth exploration of computational chemistry applications to solve real-life biological science, materials, and engineering problems. Many industries are using computational chemistry methods and molecular modeling to drive innovations in pharmaceutical drugs, packaging materials, batteries, and more. Computational Chemistry Accelerates Drug Design Computational chemistry models and simulations decrease the development timeline and costs by allowing for fast screening, design and testing of new materials. Another exciting application of computational chemistry approaches is the use of atomic-scale materials modeling in the design of new battery and energy storage solutions. At Schrödinger, our physics-based computational platform allows companies worldwide to harness the capabilities of computational chemistry methods and apply these to their R&D programs quickly and with ease.
[178] Computational Chemistry in the Pharmaceutical Industry: From Childhood ... — Computational chemistry within the pharmaceutical industry has grown into a field that proactively contributes to many aspects of drug design, including target selection and lead identification and optimization. While methodological advancements have been key to this development, organizational developments have been crucial to our success as well.
[179] PDF — Example 2: Molecular docking facilitated the discovery of novel antifungal agents by 3246 NeuroQuantology| December 2022 | Volume 20 | Issue 20 |Page 3245-3250|doi: 10.48047/NQ.2022.20.20.NQ109321 Trilochan Ram Sahu et al/APPLICATIONS OF COMPUTATIONAL CHEMISTRY IN DRUG DESIGN: A REVIEW eISSN1303-5150 www.neuroquantology.com predicting their binding modes with fungal enzymes (Ferreira et al., 2015). AI and Machine Learning in Drug Design Dynamics Simulations in Protein-Ligand Interactions GROMACS Mechanistic understanding of drug resistance mechanisms Beta-Lactamase AMBER Exploration of ligand binding dynamics and allosteric modulation GPCR Desmond Prediction of binding affinity and selectivity of kinase inhibitors Enzyme Kinase 3248 NeuroQuantology| December 2022 | Volume 20 | Issue 20 |Page 3245-3250|doi: 10.48047/NQ.2022.20.20.NQ109321 Trilochan Ram Sahu et al/APPLICATIONS OF COMPUTATIONAL CHEMISTRY IN DRUG DESIGN: A REVIEW eISSN1303-5150 www.neuroquantology.com AI and machine learning algorithms are revolutionizing drug design by accelerating virtual screening, predicting molecular interactions, and optimizing lead compounds (Schneider et al., 2020).
[181] Artificial Intelligence (AI) Applications in Drug Discovery and Drug ... — AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. By the early 2000s, AI began to gain traction with the introduction of machine learning algorithms capable of analyzing complex datasets, which helped streamline the drug discovery process by predicting molecular interactions and optimizing drug formulations.
[182] The Role of AI in Drug Discovery: Challenges, Opportunities, and ... — Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. AI-based approaches, on the other hand, have the ability to improve the efficiency and accuracy of drug discovery processes and can lead to the development of more effective medications. By combining the predictive power of AI with the expertise and experience of human researchers , it is possible to optimize the drug discovery process and accelerate the development of new medications . Recent developments in AI, including the use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges and limitations of AI in the context of drug discovery.
[184] The success of computational material design for sustainable energy ... — The success of computational material design for sustainable energy catalysis - ScienceDirect Search ScienceDirect The success of computational material design for sustainable energy catalysis Author links open overlay panelSamira Siahrostami (Conceptualization Data curation Funding acquisition Investigation Project administration Supervision Writing – original draft Writing – review & editing) a, Nicholas Murray (Writing – review & editing) a Open access Computational material design (CMD) employs quantum mechanical simulations, density functional theory, and machine learning techniques to correlate electronic structural attributes with physical and chemical properties of materials. This contribution provides an overview of CMD’s success in driving materials discovery for catalysis in the context of sustainable energy applications. Next article in issue computational material design © 2024 The Author(s) No articles found. For all open access content, the relevant licensing terms apply.
[185] The Materials Genome Initiative, the interplay of experiment, theory ... — By allowing code, experimental results, simulation output, hypotheses, and human expertise to be communicated instantaneously and freely among researchers, the proposed Materials Genome Initiative (MGI) has the potential to achieve an inflection point in the pace of discovery within materials science and engineering that may be comparable to that achieved by earlier, once-in-a-generation infrastructure developments listed above. The improved ability to simulate not just materials structure but also collective properties has placed computational science alongside synthesis, analytical theory, and experiment as the four essential legs of an integrated approach towards deploying new materials.
[190] Fundamental Challenges for Modeling Electrochemical Energy Storage ... — There is a strong need to improve the efficiency of electrochemical energy storage, but progress is hampered by significant technological and scientific challenges. This review describes the potential contribution of atomic-scale modeling to the development of more efficient batteries, with a particular focus on first-principles electronic structure calculations. Numerical and theoretical
[191] Atomic-Scale Modeling for Materials and Chemistry — Atomic and molecular modeling techniques have developed over the past 75 years into a vibrant field of computational science, used to understand and predict materials properties and phenomena in academic, industrial, and government labs. ... It is not surprising then that important problems in many fields—battery chemistry, drug design
[192] Dynamic evolution of cathode-electrolyte interphase in lithium metal ... — Li/Li+, showcasing their capabilities with high-voltage cathode materials such as LiNixMnyCo1−x−yO2 (NMC) and LiCoO2.17,18,19 However, despite these significant advances, a clear mechanistic understanding is urgently needed to further improve the stabilities of ether-based electrolytes in practical HVLMBs. Cathode-electrolyte interphases (CEIs) play pivotal roles in governing the high-voltage stability of ether electrolytes.20 To elucidate the underlying mechanisms, comprehensive characterization techniques have been employed to investigate the components and structures of CEIs, including X-ray photoelectron spectroscopy (XPS),21 electron microscopy (EM),4,22 time-of-flight secondary ion mass spectrometry (TOF-SIMS),23,24,25 and atomic force microscopy (AFM),26 etc.
[193] Computational Energy Storage Materials - mpie.de — The research activities include the construction of both physics-based and big data-driven models and their application in the studies of ion intercalation dynamics, lithium electrode heterogeneity, crystallographic defect generation, local stress responses, inter- and intra-granular crack formation, lithium dendrite growth, surface lattice reconstruction, bulk phase transition, and other electro-chemical and structural degradation mechanisms occurring during realistic battery operation. The group also actively collaborates with experimental and ab-initio research groups, both in terms of mechanistic understanding of experimental results, assisting the microstructure design of high-performance electrodes, and by directly constructing the geometrical and physical models from experimental and atomic simulation data, such as electron microscopy techniques, operando diffraction analysis, GITT experiments, solid-state nuclear magnetic resonance spectroscopy, and nano-indentation experiments.
[205] PDF — Abstract: Computational quantum chemistry has emerged as a powerful tool for understanding molecular properties and reactions, enabling researchers to explore complex chemical systems with unprecedented accuracy and efficiency. Keywords: computational chemistry, quantum chemistry, electronic structure theory, density functional theory, ab initio methods, applications, future directions 1. 3.2 Density Functional Theory (DFT) Density Functional Theory (DFT) is a powerful computational method in quantum chemistry for predicting the electronic structure and properties of molecules and materials. Journal of Systems Engineering and Electronics (ISSN NO: 1671-1793) Volume 34 ISSUE 5 2024 PAGE NO: 209 5.2 Treatment of Electron Correlation Effects The treatment of electron correlation effects is a critical aspect of computational quantum chemistry, as electron-electron interactions significantly influence the electronic structure and properties of molecules.
[214] Molecular docking and dynamics simulations of novel drug targets — Molecular dynamics (MD) simulation stands as the fundamental computational tool for capturing dynamic aspects of protein structure, function and ligand interactions and dynamics with utmost detail. 21 Inter-molecular and intra-molecular interactions that influences the stability of biomolecules and molecular complex can be analyzed using
[215] Use of molecular docking computational tools in drug discovery — Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings.
[220] Advancements in Machine Learning Predicting Activation and Gibbs Free ... — Machine learning has revolutionized computational chemistry by improving the accuracy of predicting thermodynamic and kinetic properties like activation energies and Gibbs free energies, accelerating materials discovery and optimizing reaction conditions in both academic and industrial applications.
[223] Machine Learning - Driven Materials Discovery: Unlocking Next ... — The rapid advancement of machine learning and artificial intelligence (AI)-driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerating scientific progress. This review provides a comprehensive overview of smart, machine learning (ML)-driven approaches, emphasizing their role in predicting material
[224] Accelerating materials discovery using artificial intelligence, high ... — In materials discovery, traditional manual, serial, and human-intensive work is being augmented by automated, parallel, and iterative processes driven by Artificial Intelligence (AI), simulation and experimental automation. In this perspective, we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle. We show, using the example of the development of a novel chemically amplified photoresist, how these technologies’ impacts are amplified when they are used in concert with each other as powerful, heterogeneous workflows. Fortunately, our tools for performing such discovery cycles are transforming—with data, artificial intelligence and hybrid cloud being used in new ways to break through long-standing bottlenecks1,2. Now, with the maturation of AI and robotic technology, alongside the further scaling of high-performance computing and hybrid cloud technologies, we are entering a new paradigm where the key is not any one individual technology, but instead how heterogeneous capabilities work together to achieve results greater than the sum of their parts.
[225] Artificial intelligence in drug development: reshaping the therapeutic ... — Artificial intelligence (AI) is receiving increasing attention from major pharmaceutical and biotechnology companies worldwide as an engine for new drug development. With three main elements: vast datasets, complex mathematical models, and advanced computational algorithms, AI is a breakthrough in drug discovery and development, bringing new power to the R&D (research and development) of new
[229] PDF — This review provides a comprehensive overview of the key computational methods and tools used in drug discovery, including molecular docking, molecular dynamics simulations, quantum mechanics, and machine learning. Keywords: Computational chemistry, drug discovery, molecular docking, molecular dynamics, quantum mechanics, machine learning, molecular modelling, virtual screening Introduction The pharmaceutical industry has traditionally relied on experimental approaches for drug discovery, a process that is often labor-intensive, time-consuming, and costly. Computational methods in drug discovery Molecular docking is a key computational technique used to predict the preferred orientation of a small molecule (ligand) when bound to a target protein (receptor). Conclusion Computational chemistry has revolutionized modern drug discovery, providing powerful tools and techniques to predict and analyze molecular interactions.
[244] Frontier of Computational and Theoretical Chemistry — Theoretical and computational chemistry provide critical insights into molecular structures, characteristics, and reactivities, allowing us to better understand chemical systems and apply them to drug design and materials research (Lin, 2010; Morales-Navarro et al., 2019). The incorporation of quantum mechanical simulations, molecular dynamics, and machine learning applications into computational chemistry has greatly enhanced the science, allowing researchers to predict chemical characteristics and reactions with high precision. These technologies provide a view into the future of chemistry research, in which quantum computers and advanced simulation techniques will play a critical role in deciphering the intricacies of chemical systems. Finally, the future of theoretical and computational chemistry will be defined by the integration of cutting-edge technologies like as artificial intelligence, quantum computing, and machine learning to address difficult chemical problems and improve prediction skills.
[246] Challenges and Future Prospects in the Field of Computational Chemistry — Abstract This chapter explores the challenges and opportunities of computational chemistry, which has revolutionized molecular science. Although computational chemistry has made significant progress, it continues to struggle with molecular system complexity, precise approximations, enzyme design for specific reactions, a standard computational method, and computer resource constraints.
[248] Unifying machine learning and quantum chemistry with a deep neural ... — Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate
[252] Revolution of Artificial Intelligence in Computational Chemistry ... — The field of computational chemistry is one of many sectors that artificial intelligence (AI) has revolutionized in recent years. Chemists are now more equipped to analyze enormous volumes of data, optimize chemical processes, and design new molecules and materials with high speed and accuracy because of advancements in machine-learning (ML) approaches, hardware platforms, and algorithms. This
[255] Artificial intelligence and automation to power the future of chemistry ... — Unlocking chemistry's future: Artificial intelligence-driven instrumentation revolutionizes discovery. Download: Download high-res image (140KB) ... As the number of structures to be screened increases, computational calculations become impractical due to the high cost. AI techniques, such as machine learning (ML), can learn patterns from
[256] Revolution of Artificial Intelligence in Computational Chemistry ... — The field of computational chemistry is one of many sectors that artificial intelligence (AI) has revolutionized in recent years. Chemists are now more equipped to analyze enormous volumes of data, optimize chemical processes, and design new molecules and materials with high speed and accuracy because of advancements in machine-learning (ML) approaches, hardware platforms, and algorithms. This
[257] AI in computational chemistry through the lens of a decade-long journey ... — This inevitably leads to the loss of accuracy when the properties of the whole system are reconstructed from such MLPs. Lot of progress is made in this respect although many challenges remain too.119 Dispersion interactions can be either added explicitly via, e.g., D4 corrections, similarly to the typical practice in DFT and as is done in ANI-1x-D4 and ANI-2x-D4 methods121 (also implemented in MLatom),10 or they can be attempted to be implicitly learned by MLP from the data (as ANI-1ccx does to some extend).63,119 Both approaches are approximations and can be insufficient.119 Electrostatics is also a challenge to learn, and different approximations were suggested too, i.e., some of them rely on learning charges (e.g., to reproduce the dipole moments from the reference QM calculations)102 and others on self-consistent or message-passing framework iteratively refining the charge distribution until the lower-energy solution is found.119 While many approaches rely on learning point charges,62 an alternative was suggested based on maximally localized Wannier centers.122 One of the interesting directions for exploring large systems is the incorporation of ML into QM/MM and ONIOM schemes.119
[258] AI And Machine Learning Revolutionize Computational Chemistry, From ... — AI And Machine Learning Revolutionize Computational Chemistry, From Quantum Chemistry To Machine Learning And Back Quantum Computing What can we learn about Quantum Computing Companies from technology history? AI and Machine Learning Revolutionize Computational Chemistry, From Quantum Chemistry to machine learning and back Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being integrated into computational chemistry, offering solutions to scalability and accelerating the exploration of chemical space. The future of computational chemistry lies in the successful integration of AI and ML, and the development of models that can accurately predict chemical properties and behaviors based on quantum mechanics. Artificial Intelligence Chemical Properties Computational Chemistry In Silico Experiments Machine Learning quantum mechanics reproducibility scalability Schrödinger Equation Transferability Quantum Computing News
[262] Evaluating a quantum-classical quantum Monte Carlo algorithm with ... — Solving the electronic structure problem of molecules and solids to high accuracy is a major challenge in quantum chemistry and condensed matter physics. The rapid emergence and development of quantum computers offer a promising route to systematically tackle this problem. Recent work by [Huggins et al., Nature (London) 603, 416 (2022)] proposed a hybrid quantum-classical quantum Monte Carlo
[266] The need for standardisation in life science research - an approach to ... — Although many standards have been developed in recent years, the insufficient implementation of and compliance to existing standards, e.g. within the life science sector, lead to a disruption of the innovation pipeline - often happening at the interface between academic research and industry - simply because of bad quality and missing reproducibility/reusability of the data. We believe that this White Paper demonstrates the global need to promote standards in the life sciences research in response to a major challenge of implementing open science principles in the academic workflow, especially with respect to the reproducibility and reliability of research data. In the opinion paper “The need for standardisation in life science research - an approach to excellence and trust”, the authors suggest and discuss measures to ensure high quality and reusability of data in life science research.
[267] A Guide to Molecular Standardization - Depth-First — Computational chemistry and cheminformatics can be thought of at a basic level as fields that transform molecular graphs and associated data into predictions and insights. The quality of the input sets an upper limit on the value of the output. ... Well-defined tolerances for accuracy and precision play a role, as does adherence to standard