End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design

Numerical simulations have revolutionized material design.However,although simulations excel at mapping an input material to its output property,their direct application to inverse design has traditionally been limited by their high computing cost and lack of differentiability.Here,taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm,we introduce a computational inverse design framework that addresses these challenges,by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end differentiation.Thanks to its differentiability,the simulation is used to directly train a deep generative model,which outputs an optimal porous matrix based on an arbitrary input sorption isotherm curve.Importantly,this inverse design pipeline leverages the power of tensor processing units(TPU)—an emerging family of dedicated chips,which,although they are specialized in deep learning,are flexible enough for intensive scientific simulations.This approach holds promise to accelerate inverse materials design.

Predicting glass structure by physics-informed machine learning

Machine learning(ML)is emerging as a powerful tool to predict the properties of materials,including glasses.Informing ML models with knowledge of how glass composition affects short-range atomic structure has the potential to enhance the ability of composition-property models to extrapolate accurately outside of their training sets.Here,we introduce an approach wherein statistical mechanics informs a ML model that can predict the non-linear composition-structure relations in oxide glasses.This combined model offers an improved prediction compared to models relying solely on statistical physics or machine learning individually.Specifically,we show that the combined model accurately both interpolates and extrapolates the structure of Na_(2)O–SiO_(2)glasses.Importantly,the model is able to extrapolate predictions outside its training set,which is evidenced by the fact that it is able to predict the structure of a glass series that was kept fully hidden from the model during its training.