Papers
1–2 of 2Research Paper·Feb 4, 2026·B2BIndustrials
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulatio...
6.0 viability
Research Paper·Mar 2, 2026
MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interaction 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...
5.0 viability