MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials
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 \tex...
| Authors | Yuanchang Zhou, Siyu Hu, Xiangyu Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 March 2026 |
| Last updated at source | 6 March 2026 |
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