MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials

TL;DR

MatRIS introduces an attention-based invariant machine learning interatomic potential that achieves high accuracy and scalability, matching or surpassing equivariant models while reducing computational costs in materials science applications.

Contribution

The paper presents MatRIS, a novel invariant MLIP with a linear complexity attention mechanism, enabling scalable and accurate modeling of atomic interactions.

Findings

MatRIS achieves comparable accuracy to state-of-the-art equivariant models.

MatRIS demonstrates lower training costs while maintaining high performance.

The model scales linearly with the number of atoms, enabling large-scale applications.

Abstract

Foundation MLIPs demonstrate broad applicability across diverse material systems and have emerged as a powerful and transformative paradigm in chemical and computational materials science. Equivariant MLIPs achieve state-of-the-art accuracy in a wide range of benchmarks by incorporating equivariant inductive bias. However, the reliance on tensor products and high-degree representations makes them computationally costly. This raises a fundamental question: as quantum mechanical-based datasets continue to expand, can we develop a more compact model to thoroughly exploit high-dimensional atomic interactions? In this work, we present MatRIS (\textbf{Mat}erials \textbf{R}epresentation and \textbf{I}nteraction \textbf{S}imulation), an invariant MLIP that introduces attention-based modeling of three-body interactions. MatRIS leverages a novel separable attention mechanism with linear complexity $O(N)$, enabling both scalability and expressiveness. MatRIS delivers accuracy comparable to that of leading equivariant models on a wide range of popular benchmarks (Matbench-Discovery, MatPES, MDR phonon, Molecular dataset, etc). Taking Matbench-Discovery as an example, MatRIS achieves an F1 score of up to 0.847 and attains comparable accuracy at a lower training cost. The work indicates that our carefully designed invariant models can match or exceed the accuracy of equivariant models at a fraction of the cost, shedding light on the development of accurate and efficient MLIPs.