MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals

arXiv:2604.13897 · cs.LG, physics.comp-ph · Submitted 2026-04-15 · Read on arXiv

cs.LG, physics.comp-ph

Submitted: 2026-04-15

Updated: 2026-07-24

Terminology

• Foundation Model — 2× in this paper · explained in 3 episodes
• Machine-Learned Interatomic Potentials — 2× in this paper
• Foundation Models — 1× in this paper · explained in 8 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks