How To Optimize Materials and Devices <i>via</i> Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics

TLDR

Most discoveries in materials science have been made empirically through one‑variable‑at‑a‑time experimentation, yet material systems are complex and interdependent, so altering a single variable can produce multiple unforeseen effects, as seen in the many components and processing conditions of organic photovoltaics. Design of Experiments, when combined with machine learning, allows simultaneous testing and optimization of multiple variables, accelerating discovery and conserving laboratory resources. The integration of DoE with machine learning expands the optimization landscape from narrow, serial experimentation to a comprehensive, multi‑directional view.

Abstract

Most discoveries in materials science have been made empirically, typically through one-variable-at-a-time (Edisonian) experimentation. The characteristics of materials-based systems are, however, neither simple nor uncorrelated. In a device such as an organic photovoltaic, for example, the level of complexity is high due to the sheer number of components and processing conditions, and thus, changing one variable can have multiple unforeseen effects due to their interconnectivity. Design of Experiments (DoE) is ideally suited for such multivariable analyses: by planning one's experiments as per the principles of DoE, one can test and optimize several variables simultaneously, thus accelerating the process of discovery and optimization while saving time and precious laboratory resources. When combined with machine learning, the consideration of one's data in this manner provides a different perspective for optimization and discovery, akin to climbing out of a narrow valley of serial (one-variable-at-a-time) experimentation, to a mountain ridge with a 360° view in all directions.