Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields
cs.LG, cond-mat.mtrl-sci, physics.chem-ph, q-bio.BM
Submitted: 2025-03-11
Updated: 2025-05-29
Project page: https://tkreiman.github.io/projects/mlff_distribution_shifts
Terminology
Sources
- A foundation model for atomistic materials chemistry
- Graph Neural Networks Use Graphs When They Shouldn't
- Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
- Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
- Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
- GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets
- On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
- Implicit Bias of Gradient Descent on Linear Convolutional Networks
- Test-Time Adaptation via Self-Training with Nearest Neighbor Information
- EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
- Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials
- From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
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