Self-consistent Coulomb interactions for machine learning interatomic potentials

Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies

Computing solvation free energies of small molecules with experimental accuracy

Toward transferable empirical valence bonds: Making classical force fields reactive.

Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces

Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials

Accurate Crystal Structure Prediction of New 2D Hybrid Organic Inorganic Perovskites

Efficiency, accuracy, and transferability of machine learning potentials: Application to dislocations and cracks in iron

Zero Shot Molecular Generation via Similarity Kernels

Equivariant Matrix Function Neural Networks