Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy

Novel optoelectronic materials have the potential to revolutionize the ongoing green transition by both providing more efficient photovoltaic (PV) devices and lowering energy consumption of devices like LEDs and sensors. The lead candidate materials for these applications are both organic semiconductors and more recently perovskites. This Perspective illustrates how novel machine learning techniques can help explore these materials, from speeding up ab initio calculations toward experimental guidance. Furthermore, based on existing work, perspectives around machine-learned molecular dynamics potentials, physically informed neural networks, and generative methods are outlined.

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This Perspective discusses the potential of novel machine learning techniques in exploring optoelectronic materials, and their application in accelerating calculations and providing experimental guidance. It also outlines the prospects of machine-learned molecular dynamics potentials, physically informed neural networks, and generative methods based on existing work.