This period centers on entropy as a unifying quantitative framework bridging materials science, information theory, and fundamental physics. Contemporary themes highlight how thermodynamics and kinetics guide high-entropy alloy design through enthalpy-entropy balancing, microstructure engineering, and sluggish diffusion, with elemental tuning (notably aluminum-containing multi-component systems) shaping phase formation and strength-ductility outcomes. The approach emphasizes cross-disciplinary entropy concepts as a common language for disorder, evolution of microstructure, and entropy production across time and temperature.
High-entropy alloys (HEAs) are increasingly designed by thermodynamics, balancing enthalpy and entropy to predict phase stability and potential decomposition pathways across compositions.
High-entropy alloys (HEAs) leverage microstructure engineering—dual-phase architectures, twinning-induced plasticity, precipitation hardening, and severe plastic deformation—to overcome strength-ductility trade-offs across systems.
Kinetic control, including sluggish diffusion, phase decomposition, and thermomechanical processing, governs HEA stability and performance, linking time, temperature, and microstructure evolution.
Elemental tuning, especially Al-containing multi-component systems like AlCoCrCuFeNi, reveals how composition spaces shape phase formation and mechanical performance, through specific Al effects and alloying trends.
Entropy serves as a unifying idea across disciplines, from mathematical entropy and geometry to quantum information and black-hole thermodynamics, highlighting entropy production, entanglement entropy, and quantum entropy as foundational concepts.
Influential works
On Measures of Entropy and Information (1961) introduced formal definitions of entropy and information, establishing the mathematical basis for information theory and statistical mechanics; it provided axiomatic grounding and influenced coding, data compression, and thermodynamic perspectives on uncertainty.
Black Holes and Entropy (1973) proposed the Bekenstein-Hawking entropy, tying a black hole’s event horizon area to entropy and signaling a deep link between gravity, thermodynamics, and quantum theory; immediate impact: sparked black hole thermodynamics; long-term: catalyzed holography and quantum gravity program.
Ergodic theory of chaos and strange attractors (1985) connected entropy concepts with dynamical systems, highlighting how chaotic behavior yields positive metric entropy (Kolmogorov–Sinai) and complex attractors; immediate impact: formal tools for chaos; long-term: influenced complexity theory and time-series analysis.
Divergence measures based on the Shannon entropy (1991) introduces families of information divergences derived from entropy, enabling robust comparisons between distributions; immediate impact: statistics and signal processing; long-term: underpins modern machine learning, information geometry, and robust statistics.
Permutation Entropy: A Natural Complexity Measure for Time Series (2002) proposes a simple, robust ordinal-pattern based entropy measure for time series, enabling detection of complexity and regime shifts; immediate appeal: computational efficiency; long-term: widely adopted in physics, biology, finance.