Global properties of the energy landscape: a testing and training arena for machine learned potentials
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Cărare, Vlad, Thiemann, Fabian L., Morrow, Joe, Wales, David J., Pyzer-Knapp, Edward O., Dicks, Luke |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Introduction to machine learning potentials for atomistic simulations
par: Thiemann, Fabian L., et autres
Publié: (2024)
par: Thiemann, Fabian L., et autres
Publié: (2024)
Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents
par: Williams, Nicholas J., et autres
Publié: (2024)
par: Williams, Nicholas J., et autres
Publié: (2024)
Random Spin Committee Approach For Smooth Interatomic Potentials
par: Cărare, Vlad, et autres
Publié: (2024)
par: Cărare, Vlad, et autres
Publié: (2024)
Refining embeddings with fill-tuning: data-efficient generalised performance improvements for materials foundation models
par: Wilson, Matthew P., et autres
Publié: (2025)
par: Wilson, Matthew P., et autres
Publié: (2025)
Providing Machine Learning Potentials with High Quality Uncertainty Estimates
par: Sumer, Zeynep, et autres
Publié: (2025)
par: Sumer, Zeynep, et autres
Publié: (2025)
Understanding and improving transferability in machine-learned activation energy predictors
par: Gilkes, Joe, et autres
Publié: (2025)
par: Gilkes, Joe, et autres
Publié: (2025)
Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
par: Ge, Fuchun, et autres
Publié: (2024)
par: Ge, Fuchun, et autres
Publié: (2024)
ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials
par: David, Rolf, et autres
Publié: (2024)
par: David, Rolf, et autres
Publié: (2024)
High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo
par: Goswami, Shubhang, et autres
Publié: (2024)
par: Goswami, Shubhang, et autres
Publié: (2024)
Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
par: Matin, Sakib, et autres
Publié: (2025)
par: Matin, Sakib, et autres
Publié: (2025)
Density diversity in training data governs thermodynamic transferability of machine learning interatomic potentials
par: Kim, Minwoo, et autres
Publié: (2026)
par: Kim, Minwoo, et autres
Publié: (2026)
Predicting solvation free energies with an implicit solvent machine learning potential
par: Röcken, Sebastien, et autres
Publié: (2024)
par: Röcken, Sebastien, et autres
Publié: (2024)
Teachers that teach the irrelevant: Pre-training machine learned interaction potentials with classical force fields for robust molecular dynamics simulations
par: Yuan, Eric C. -Y., et autres
Publié: (2025)
par: Yuan, Eric C. -Y., et autres
Publié: (2025)
Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for $Δ$-machine learning
par: Krug, Simon León, et autres
Publié: (2024)
par: Krug, Simon León, et autres
Publié: (2024)
Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs
par: Warford, Thomas, et autres
Publié: (2026)
par: Warford, Thomas, et autres
Publié: (2026)
Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water
par: Stolte, Nore, et autres
Publié: (2024)
par: Stolte, Nore, et autres
Publié: (2024)
Basic stability tests of machine learning potentials for molecular simulations in computational drug discovery
par: Ranasinghe, Kavindri, et autres
Publié: (2025)
par: Ranasinghe, Kavindri, et autres
Publié: (2025)
Knowing when to trust machine-learned interatomic potentials
par: Mehdi, Shams, et autres
Publié: (2026)
par: Mehdi, Shams, et autres
Publié: (2026)
Pushing the limits of unconstrained machine-learned interatomic potentials
par: Bigi, Filippo, et autres
Publié: (2026)
par: Bigi, Filippo, et autres
Publié: (2026)
Understanding multi-fidelity training of machine-learned force-fields
par: Gardner, John L. A., et autres
Publié: (2025)
par: Gardner, John L. A., et autres
Publié: (2025)
Monomeric machine learning potential for general covalent molecules: linear alkanes as an example
par: Li, Xinze, et autres
Publié: (2026)
par: Li, Xinze, et autres
Publié: (2026)
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
par: Stark, Wojciech G., et autres
Publié: (2024)
par: Stark, Wojciech G., et autres
Publié: (2024)
Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials
par: Vorotnikov, Igor, et autres
Publié: (2025)
par: Vorotnikov, Igor, et autres
Publié: (2025)
Structural bias in three-dimensional autoregressive generative machine learning of organic molecules
par: Koczor-Benda, Zsuzsanna, et autres
Publié: (2025)
par: Koczor-Benda, Zsuzsanna, et autres
Publié: (2025)
Force training neural network potential energy surface models
par: Christian Devereux, et autres
Publié: (2024)
par: Christian Devereux, et autres
Publié: (2024)
Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces
par: de Kam, Lucas B. T., et autres
Publié: (2026)
par: de Kam, Lucas B. T., et autres
Publié: (2026)
Thawed Gaussian wavepacket dynamics with $Δ$-machine learned potentials
par: Gherib, Rami, et autres
Publié: (2024)
par: Gherib, Rami, et autres
Publié: (2024)
Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
par: Majumdar, Sauradeep, et autres
Publié: (2026)
par: Majumdar, Sauradeep, et autres
Publié: (2026)
Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations
par: Jinnouchi, Ryosuke, et autres
Publié: (2024)
par: Jinnouchi, Ryosuke, et autres
Publié: (2024)
Overcoming sampling limitations using machine-learned interatomic potentials: the case of water-in-salt electrolytes
par: Brugnoli, Luca, et autres
Publié: (2026)
par: Brugnoli, Luca, et autres
Publié: (2026)
Integer linear programming for unsupervised training set selection in molecular machine learning
par: Haeberle, Matthieu, et autres
Publié: (2024)
par: Haeberle, Matthieu, et autres
Publié: (2024)
Accurate nuclear quantum statistics on machine-learned classical effective potentials
par: Zaporozhets, Iryna, et autres
Publié: (2024)
par: Zaporozhets, Iryna, et autres
Publié: (2024)
MLQD: A package for machine learning-based quantum dissipative dynamics
par: Ullah, Arif, et autres
Publié: (2023)
par: Ullah, Arif, et autres
Publié: (2023)
Benchmarking machine-learned interatomic potentials for molecular infrared spectroscopy
par: Bhatia, Nitik, et autres
Publié: (2026)
par: Bhatia, Nitik, et autres
Publié: (2026)
Extending machine learning model for implicit solvation to free energy calculations
par: Dey, Rishabh, et autres
Publié: (2025)
par: Dey, Rishabh, et autres
Publié: (2025)
Adaptive energy reference for machine-learning models of the electronic density of states
par: How, Wei Bin, et autres
Publié: (2024)
par: How, Wei Bin, et autres
Publié: (2024)
Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
par: Bhatia, Nitik, et autres
Publié: (2025)
par: Bhatia, Nitik, et autres
Publié: (2025)
Development of machine-learned interatomic potentials to predict structure, transport, and reactivity in platinum-based fuel cells
par: Fazel, Kamron, et autres
Publié: (2025)
par: Fazel, Kamron, et autres
Publié: (2025)
Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions
par: Muuga, Kadri, et autres
Publié: (2026)
par: Muuga, Kadri, et autres
Publié: (2026)
Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
par: Kellner, Matthias, et autres
Publié: (2026)
par: Kellner, Matthias, et autres
Publié: (2026)
Documents similaires
-
Introduction to machine learning potentials for atomistic simulations
par: Thiemann, Fabian L., et autres
Publié: (2024) -
Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents
par: Williams, Nicholas J., et autres
Publié: (2024) -
Random Spin Committee Approach For Smooth Interatomic Potentials
par: Cărare, Vlad, et autres
Publié: (2024) -
Refining embeddings with fill-tuning: data-efficient generalised performance improvements for materials foundation models
par: Wilson, Matthew P., et autres
Publié: (2025) -
Providing Machine Learning Potentials with High Quality Uncertainty Estimates
par: Sumer, Zeynep, et autres
Publié: (2025)