Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties

TL;DR

This paper introduces MPNICE, an invariant message passing MLFF architecture that efficiently predicts long-range interactions and atomic charges, enabling accurate and faster simulations of liquids and materials across diverse systems.

Contribution

The paper presents MPNICE, a novel MLFF architecture that incorporates long-range interactions and charge predictions, improving accuracy and inference speed for atomistic simulations.

Findings

Achieves 5-20x faster inference than comparable models.

Accurately predicts properties of liquids, solids, and organometallic complexes.

Demonstrates good generalization across diverse chemical systems.

Abstract

Machine learning force fields (MLFFs) have emerged as a sophisticated tool for cost-efficient atomistic simulations approaching DFT accuracy, with recent message passing MLFFs able to cover the entire periodic table. We present an invariant message passing MLFF architecture (MPNICE) which iteratively predicts atomic partial charges, including long-range interactions, enabling the prediction of charge-dependent properties while achieving 5-20x faster inference versus models with comparable accuracy. We train direct and delta-learned MPNICE models for organic systems, and benchmark against experimental properties of liquid and solid systems. We also benchmark the energetics of finite systems, contributing a new set of torsion scans with charged species and a new set of DLPNO-CCSD(T) references for the TorsionNet500 benchmark. We additionally train and benchmark MPNICE models for bulk inorganic crystals, focusing on structural ranking and mechanical properties. Finally, we explore multi-task models for both inorganic and organic systems, which exhibit slightly decreased performance on domain-specific tasks but surprising generalization, stably predicting the gas phase structure of $\simeq500$ Pt/Ir organometallic complexes despite never training to organometallic complexes of any kind.