Generative and Reinforcement Learning assisted Material Design

In recent years, machine learning (ML) models based on supervised learning has shown tremendous success in materials property prediction such as band gap, elastic modules, thermo-electric properties that has accelerated the discovery of new materials. However, applicability of these supervised learning-based ML models is limited, and they cannot be used for complex tasks such as inverse design of materials structure, where the input to the ML model is desired property and the output is structure of the material. Deep leaning models based on reinforcement learning and deep generative model can be used for inverse design of materials. In particular, in this talk we will discuss about (1) designing MoS₂ kirigami structure with desired stretchability, (2) computational synthesis of layered materials using reinforcement learning and (3) a generative model based on graph convolution to design polymer structure with desired dielectric properties.