ArticleNature communications2021
Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- MOFF2: A Transferable Coarse-Grained Protein Force Field for Predictive Condensate Simulations.bioRxiv : the preprint server for biology · 2026Article
- Generalization of long-range machine learning potentials in complex chemical spaces.Digital discovery · 2026Article
- Article
- NEAT-DNA: A Chemically Accurate, Sequence-Dependent Coarse-Grained Model for Large-Scale DNA Simulations.Journal of chemical theory and computation · 2026Article
- Fitting coarse-grained models to macroscopic experimental data via automatic differentiation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Learning data-efficient coarse-grained molecular dynamics from forces and noise.Nature communications · 2026Article
- Foundation models for atomistic simulation of chemistry and materials.Nature reviews. Chemistry · 2026Review
- Mapping Still Matters: Coarse-Graining with Machine Learning Potentials.Journal of chemical information and modeling · 2026Article
- Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives.ChemPlusChem · 2025Article
- NEAT-DNA: A Chemically Accurate, Sequence-Dependent Coarse-Grained Model for Large-Scale DNA Simulations.bioRxiv : the preprint server for biology · 2025Article
- chemtrain-deploy: A Parallel and Scalable Framework for Machine Learning Potentials in Million-Atom MD Simulations.Journal of chemical theory and computation · 2025Article
- Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements.Journal of chemical information and modeling · 2025Article
- Reversible molecular simulation for training classical and machine-learning force fields.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Chromatin structures from integrated AI and polymer physics model.PLoS computational biology · 2025Article
- Refining potential energy surface through dynamical properties via differentiable molecular simulation.Nature communications · 2025Article
- Fine-tuning molecular mechanics force fields to experimental free energy measurements.bioRxiv : the preprint server for biology · 2025Article
- Learning non-local molecular interactions via equivariant local representations and charge equilibration.npj computational materials · 2025Article
- Data Generation for Machine Learning Interatomic Potentials and Beyond.Chemical reviews · 2024Review
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN potentials directly from experimental data have received less attention, typically facing numerical and computational challenges when backpropagating through MD simulations. We present the Differentiable Trajectory Reweighting (DiffTRe) method, which bypasses differentiation through the MD simulation for time-independent observables. Leveraging thermodynamic perturbation theory, we avoid exploding gradients and achieve around 2 orders of magnitude speed-up in gradient computation for top-down learning. We show effectiveness of DiffTRe in learning NN potentials for an atomistic model of diamond and a coarse-grained model of water based on diverse experimental observables including thermodynamic, structural and mechanical properties. Importantly, DiffTRe also generalizes bottom-up structural coarse-graining methods such as iterative Boltzmann inversion to arbitrary potentials. The presented method constitutes an important milestone towards enriching NN potentials with experimental data, particularly when accurate bottom-up data is unavailable.
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