Cover Image

chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Fuchs, Paul, Thaler, Stephan, Röcken, Sebastien, Zavadlav, Julija
Format:	Preprint
Published:	2024
Subjects:	Chemical Physics Machine Learning Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
by: Fuchs, Paul, et al.
Published: (2025)

Accurate machine learning force fields via experimental and simulation data fusion
by: Röcken, Sebastien, et al.
Published: (2023)

Predicting solvation free energies with an implicit solvent machine learning potential
by: Röcken, Sebastien, et al.
Published: (2024)

Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration
by: Fuchs, Paul, et al.
Published: (2025)

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
by: Chen, Weilong, et al.
Published: (2025)

Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
by: Sanocki, Michal, et al.
Published: (2025)

Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions
by: Fuchs, Paul, et al.
Published: (2025)

Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
by: Röcken, Sebastien, et al.
Published: (2025)

Mapping Still Matters: Coarse-Graining with Machine Learning Potentials
by: Görlich, Franz, et al.
Published: (2025)

Coarse-Grained Boltzmann Generators
by: Chen, Weilong, et al.
Published: (2026)

JaxSGMC: Modular stochastic gradient MCMC in JAX
by: Thaler, Stephan, et al.
Published: (2025)

Implicit Delta Learning of High Fidelity Neural Network Potentials
by: Thaler, Stephan, et al.
Published: (2024)

Physics-Informed Weakly Supervised Learning for Interatomic Potentials
by: Takamoto, Makoto, et al.
Published: (2024)

Learning Equivariant Non-Local Electron Density Functionals
by: Gao, Nicholas, et al.
Published: (2024)

Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
by: Liu, Yuzhi, et al.
Published: (2026)

OpenQDC: Open Quantum Data Commons
by: Gabellini, Cristian, et al.
Published: (2024)

GemNet: Universal Directional Graph Neural Networks for Molecules
by: Gasteiger, Johannes, et al.
Published: (2021)

Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations
by: Störmer, Ian, et al.
Published: (2026)

Shoot from the HIP: Hessian Interatomic Potentials without derivatives
by: Burger, Andreas, et al.
Published: (2025)

Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems
by: Scherbela, Michael, et al.
Published: (2025)

On Representing Electronic Wave Functions with Sign Equivariant Neural Networks
by: Gao, Nicholas, et al.
Published: (2024)

Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations
by: Gao, Nicholas, et al.
Published: (2024)

Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes
by: Simeon, Guillem, et al.
Published: (2024)

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials
by: Zariquiey, Francesc Sabanés, et al.
Published: (2025)

A Posteriori Evaluation of a Physics-Constrained Neural Ordinary Differential Equations Approach Coupled with CFD Solver for Modeling Stiff Chemical Kinetics
by: Kumar, Tadbhagya, et al.
Published: (2023)

Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory
by: Chen, Siqi, et al.
Published: (2026)

Geometric Deep Learning for Molecular Crystal Structure Prediction
by: Kilgour, Michael, et al.
Published: (2023)

Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State
by: Gao, Nicholas, et al.
Published: (2026)

Lightweight Geometric Deep Learning for Molecular Modelling in Catalyst Discovery
by: Geitner, Patrick
Published: (2024)

Deep Learning Foundation Models from Classical Molecular Descriptors
by: Burns, Jackson W., et al.
Published: (2025)

Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
by: Plainer, Michael, et al.
Published: (2025)

Molecular Learning Dynamics
by: Gusev, Yaroslav, et al.
Published: (2025)

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
by: Pelaez, Raul P., et al.
Published: (2024)

Self-consistent Validation for Machine Learning Electronic Structure
by: Hu, Gengyuan, et al.
Published: (2024)

Grappa -- A Machine Learned Molecular Mechanics Force Field
by: Seute, Leif, et al.
Published: (2024)

Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies
by: Vinod, Vivin, et al.
Published: (2024)

Multi-Fidelity Machine Learning for Excited State Energies of Molecules
by: Vinod, Vivin, et al.
Published: (2023)

Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning
by: Pelletier, Vivienne, et al.
Published: (2026)

Machine-Learning Interatomic Potentials for Long-Range Systems
by: Ji, Yajie, et al.
Published: (2025)

Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations
by: Rydzewski, Jakub, et al.
Published: (2022)