rMD-17

The 'rMD-17' dataset is a benchmark that contains molecular dynamics simulations used to evaluate the performance of machine learning interatomic potentials in predicting material properties.

Papers using rMD-17 (12)

Prototype-Guided Latent Alignment for Data-Efficient Fine-Tuning of Molecular Foundation Models2026Towards Improved Quantum Machine Learning for Molecular Force Fields2025 · 1 citesPreserving Continuous Symmetry in Discrete Spaces: Geometric-Aware Quantization for SO(3)-Equivariant GNNs2026Machine Learning Hamiltonians are Accurate Energy-Force Predictors2026Quantized SO(3)-Equivariant Graph Neural Networks for Efficient Molecular Property Prediction2026Fast Evaluation of Unbiased Atomic Forces in ab initio Variational Monte Carlo via the Lagrangian Technique2025ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics2025A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attention2025INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics2025From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction2023 · 22 citesNo Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials2024FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields2024