Cover Image

Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Fuchs, Paul, Zavadlav, Julija
Format:	Preprint
Published:	2025
Subjects:	Computational Physics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
by: Fuchs, Paul, et al.
Published: (2024)

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

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

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

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

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

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

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

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

Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling
by: Costa, Nuno, et al.
Published: (2025)

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

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements
by: Malik, Shreshth A., et al.
Published: (2025)

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

MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals
by: Lahouari, Adam, et al.
Published: (2026)

Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations
by: Lin, Yuchao, et al.
Published: (2025)

Physics-Informed Machine Learning for Two-Phase Moving-Interface and Stefan Problems
by: Chang, Che-Chia, et al.
Published: (2025)

AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
by: Mirarchi, Antonio, et al.
Published: (2024)

Boltzmann Classifier: A Thermodynamic-Inspired Approach to Supervised Learning
by: Amin, Muhamed, et al.
Published: (2025)

On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants
by: Avilov, Fedor S., et al.
Published: (2024)

Optimized Machine Learning Methods for Studying the Thermodynamic Behavior of Complex Spin Systems
by: Kapitan, Dmitrii, et al.
Published: (2025)

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
by: Nascimento, Gabriel de Miranda, et al.
Published: (2026)

Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
by: Shah, Karan, et al.
Published: (2025)

Differentiable Thermodynamic Phase-Equilibria for Machine Learning
by: Hicham, Karim K. Ben, et al.
Published: (2026)

Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition
by: Jacobsen, Christian, et al.
Published: (2024)

Climate-Invariant Machine Learning
by: Beucler, Tom, et al.
Published: (2021)

Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
by: Shah, Karan, et al.
Published: (2024)

${\it Asparagus}$: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces
by: Töpfer, Kai, et al.
Published: (2024)

Machine Learning for neutron source distributions
by: Robledo, Jose Ignacio, et al.
Published: (2026)

Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
by: Luo, Erpai, et al.
Published: (2025)

MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling
by: Kraß, Hendrik, et al.
Published: (2025)

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

Thermodynamics-informed super-resolution of scarce temporal dynamics data
by: Bermejo-Barbanoj, Carlos, et al.
Published: (2024)

Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Study
by: Mehdizadeh, Ardavan, et al.
Published: (2025)

Machine-Learned Atomic Cluster Expansion Potentials for Fast and Quantum-Accurate Thermal Simulations of Wurtzite AlN
by: Yang, Guang, et al.
Published: (2023)

Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows
by: Li, Wenwen, et al.
Published: (2026)

Understanding Machine Learning Paradigms through the Lens of Statistical Thermodynamics: A tutorial
by: Star, et al.
Published: (2024)

Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
by: Fu, Xiang, et al.
Published: (2025)

Adaptive Sensing of Continuous Physical Systems for Machine Learning
by: Köster, Felix, et al.
Published: (2026)