Differentiable thermodynamic modeling

TL;DR

This paper introduces a differentiable programming framework for thermodynamic modeling, enabling gradient-based optimization and integration with deep learning to improve predictions and guide material design.

Contribution

It presents a novel differentiable thermodynamic modeling approach that unifies thermodynamics and deep learning, demonstrated on the Cu-Rh system.

Findings

Successful application to Cu-Rh system

Enhanced prediction power expected for complex materials

Framework enables gradient-based optimization of thermodynamic models

Abstract

A new framework of thermodynamic modeling is proposed by introducing the concept of differentiable programming, where all the thermodynamic observables including both thermochemical quantities and phase equilibria can be differentiated with respect to the underlying model parameters, thus allowing the models learned by gradient-based optimization. It is shown that thermodynamic modeling and deep learning can be seamlessly integrated and unified within this framework. A preliminary successful application is demonstrated for the Cu-Rh system. It is expected that thermodynamic modeling in a deep learning style can increase prediction power of models, and provide more effective guidance for design, synthesis and optimization of multi-component materials with complex chemistry via learning various types of data.