MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials

ArXi:2603.02002v3 Announce Type: replace-cross Foundation MLIPs nstrate 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.