A fully differentiable framework for training proxy Exchange Correlation Functionals for periodic systems

2026-02-17

AI Summary

This work addresses the high computational cost of traditional density functional theory (DFT) in periodic systems, which hinders its application to large-scale materials. The authors propose the first end-to-end differentiable DFT framework tailored for periodic boundary conditions, employing a neural network as a learnable surrogate for the exchange-correlation (XC) functional and enabling gradient propagation through the entire self-consistent field procedure. Implemented in PyTorch, the framework offers a standardized API, integrates seamlessly with DeepChem, and is compatible with mainstream deep learning tools. Preliminary benchmarks demonstrate relative energy prediction errors of 5–10% compared to established platforms such as GPAW and PySCF, substantially enhancing the efficiency and flexibility of synergistic DFT–machine learning modeling.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Density Functional Theory (DFT) is widely used for first-principles simulations in chemistry and materials science, but its computational cost remains a key limitation for large systems. Motivated by recent advances in ML-based exchange-correlation (XC) functionals, this paper introduces a differentiable framework that integrates machine learning models into density functional theory (DFT) for solids and other periodic systems. The framework defines a clean API for neural network models that can act as drop in replacements for conventional exchange-correlation (XC) functionals and enables gradients to flow through the full self-consistent DFT workflow. The framework is implemented in Python using a PyTorch backend, making it fully differentiable and easy to use with standard deep learning tools. We integrate the implementation with the DeepChem library to promote the reuse of established models and to lower the barrier for experimentation. In initial benchmarks against established electronic structure packages (GPAW and PySCF), our models achieve relative errors on the order of 5-10%.

Problem

• Density Functional Theory
• Exchange-Correlation Functionals
• Periodic Systems
• Machine Learning
• Computational Cost

Innovation

• differentiable DFT
• machine learning XC functional
• periodic systems
• end-to-end gradient
• neural network DFT