Copertina

Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Molina-Taborda, Ana, Cossio, Pilar, Lopez-Acevedo, Olga, Gabrié, Marylou
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Chemical Physics Computational Physics Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo
di: Schätzle, Zeno, et al.
Pubblicazione: (2025)

Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
di: Matin, Sakib, et al.
Pubblicazione: (2025)

Transferable Boltzmann Generators
di: Klein, Leon, et al.
Pubblicazione: (2024)

Cartesian atomic cluster expansion for machine learning interatomic potentials
di: Cheng, Bingqing
Pubblicazione: (2024)

Machine learning for accuracy in density functional approximations
di: Voss, Johannes
Pubblicazione: (2023)

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
di: Kim, Dongjin, et al.
Pubblicazione: (2025)

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

Symmetry-invariant quantum machine learning force fields
di: Le, Isabel Nha Minh, et al.
Pubblicazione: (2023)

Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
di: Holzenkamp, Matthias, et al.
Pubblicazione: (2024)

Improving the evaluation of samplers on multi-modal targets
di: Grenioux, Louis, et al.
Pubblicazione: (2025)

Machine learning interatomic potential can infer electrical response
di: Zhong, Peichen, et al.
Pubblicazione: (2025)

Free energy Estimation on Any State Space
di: He, Jiajun, et al.
Pubblicazione: (2026)

FEAT: Free energy Estimators with Adaptive Transport
di: He, Jiajun, et al.
Pubblicazione: (2025)

Learning local and semi-local density functionals from exact exchange-correlation potentials and energies
di: Kanungo, Bikash, et al.
Pubblicazione: (2024)

High-performance training and inference for deep equivariant interatomic potentials
di: Tan, Chuin Wei, et al.
Pubblicazione: (2025)

Long-range electrostatics for machine learning interatomic potentials is easier than we thought
di: Kim, Dongjin, et al.
Pubblicazione: (2025)

On the design space between molecular mechanics and machine learning force fields
di: Wang, Yuanqing, et al.
Pubblicazione: (2024)

Knowing when to trust machine-learned interatomic potentials
di: Mehdi, Shams, et al.
Pubblicazione: (2026)

Pushing the limits of unconstrained machine-learned interatomic potentials
di: Bigi, Filippo, et al.
Pubblicazione: (2026)

Are neural scaling laws leading quantum chemistry astray?
di: Lee, Siwoo, et al.
Pubblicazione: (2025)

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

Flow Perturbation to Accelerate Unbiased Sampling of Boltzmann distribution
di: Peng, Xin, et al.
Pubblicazione: (2024)

PAL -- Parallel active learning for machine-learned potentials
di: Zhou, Chen, et al.
Pubblicazione: (2024)

Extending machine learning model for implicit solvation to free energy calculations
di: Dey, Rishabh, et al.
Pubblicazione: (2025)

Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
di: Zaverkin, Viktor, et al.
Pubblicazione: (2025)

Riemannian Stochastic Interpolants for Amorphous Particle Systems
di: Grenioux, Louis, et al.
Pubblicazione: (2025)

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
di: Szabó, P. Bernát, et al.
Pubblicazione: (2026)

Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials
di: Tiefenbacher, Maximilian X., et al.
Pubblicazione: (2025)

Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
di: Majumdar, Sauradeep, et al.
Pubblicazione: (2026)

Scalable learning of potentials to predict time-dependent Hartree-Fock dynamics
di: Bhat, Harish S., et al.
Pubblicazione: (2024)

All-in-one foundational models learning across quantum chemical levels
di: Chen, Yuxinxin, et al.
Pubblicazione: (2024)

How unconstrained machine-learning models learn physical symmetries
di: Domina, Michelangelo, et al.
Pubblicazione: (2026)

Data-driven construction of machine-learning-based interatomic potentials for gas-surface scattering dynamics: the case of NO on graphite
di: Del Fré, Samuel, et al.
Pubblicazione: (2026)

Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials
di: Ullberg, R. Seaton, et al.
Pubblicazione: (2026)

Pre-training of Molecular GNNs via Conditional Boltzmann Generator
di: Koge, Daiki, et al.
Pubblicazione: (2023)

Coarse-Grained Boltzmann Generators
di: Chen, Weilong, et al.
Pubblicazione: (2026)

Considerations in the use of ML interaction potentials for free energy calculations
di: Mendible, Orlando A., et al.
Pubblicazione: (2024)

Machine learning frontier orbital energies of nanodiamonds
di: Kirschbaum, Thorren, et al.
Pubblicazione: (2022)

Polarizable atomic multipoles for learning long-range electrostatics
di: Kim, Dongjin, et al.
Pubblicazione: (2026)