MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials

• URL: http://arxiv.org/abs/2603.02002v2
• Date: Thu, 05 Mar 2026 16:45:08 GMT
• Title: MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials
• Authors: Yuanchang Zhou, Siyu Hu, Xiangyu Zhang, Hongyu Wang, Guangming Tan, Weile Jia,
• Abstract summary: MatRIS is an invariant MLIP that introduces attention-based modeling of three-body interactions.<n>MatRIS delivers accuracy comparable to that of leading equivariant models on a wide range of popular benchmarks.
• Score: 11.867736304906508
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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