2026
MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interaction Potentials
ICLR 2026poster
Universal MLIPs (uMLIPs) demonstrate broad applicability across diverse material systems and have emerged as a powerful and transformative paradigm in chemical and computational materials science. Equivariant uMLIPs achieve state-of-the-art accuracy in a wide range of benchmarks by incorporating equ…