Copertina

Constructing accurate and efficient general-purpose atomistic machine learning model with transferable accuracy for quantum chemistry

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Yicheng, Yan, Wenjie, Wang, Zhanfeng, Wu, Jianming, Xu, Xin
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Chemical Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
di: Liu, Junlan, et al.
Pubblicazione: (2024)

Uncertainty in the era of machine learning for atomistic modeling
di: Grasselli, Federico, et al.
Pubblicazione: (2025)

Fast and flexible long-range models for atomistic machine learning
di: Loche, Philip, et al.
Pubblicazione: (2024)

Introduction to machine learning potentials for atomistic simulations
di: Thiemann, Fabian L., et al.
Pubblicazione: (2024)

Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
di: Bigi, Filippo, et al.
Pubblicazione: (2025)

Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces
di: de Kam, Lucas B. T., et al.
Pubblicazione: (2026)

A foundation model for atomistic materials chemistry
di: Batatia, Ilyes, et al.
Pubblicazione: (2023)

An accurate and efficient framework for modelling the surface chemistry of ionic materials
di: Shi, Benjamin X., et al.
Pubblicazione: (2024)

The dark side of the forces: assessing non-conservative force models for atomistic machine learning
di: Bigi, Filippo, et al.
Pubblicazione: (2024)

Benchmarking foundation potentials against quantum chemistry methods for predicting molecular redox potentials
di: Chen, Yicheng, et al.
Pubblicazione: (2025)

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

High-quality, high-information datasets for universal atomistic machine learning
di: Malosso, Cesare, et al.
Pubblicazione: (2026)

Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
di: Suman, Divya, et al.
Pubblicazione: (2025)

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
di: Mazitov, Arslan, et al.
Pubblicazione: (2025)

Augmenting chemical databases for atomistic machine learning by sampling conformational space
di: Vazquez-Salazar, Luis Itza, et al.
Pubblicazione: (2025)

Constructing and explaining machine learning models for chemistry: example of the exploration and design of boron-based Lewis acids
di: Fenogli, Juliette, et al.
Pubblicazione: (2025)

Understanding and improving transferability in machine-learned activation energy predictors
di: Gilkes, Joe, et al.
Pubblicazione: (2025)

DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations
di: Li, Yang, et al.
Pubblicazione: (2026)

Active learning and explicit electrostatics enable accurate modeling of electrolytes
di: Chalykh, Olga, et al.
Pubblicazione: (2025)

Short-range $Δ$-Machine Learning: A cost-efficient strategy to transfer chemical accuracy to condensed phase systems
di: Mészáros, Bence Balázs, et al.
Pubblicazione: (2025)

qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations
di: Fan, Zheyong, et al.
Pubblicazione: (2026)

Physics-informed machine learning for combustion: A review
di: Wu, Jiahao, et al.
Pubblicazione: (2025)

Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
di: Schwalbe-Koda, Daniel, et al.
Pubblicazione: (2024)

Machine learning electronic structure and atomistic properties from the external potential
di: Nigam, Jigyasa, et al.
Pubblicazione: (2026)

More converged, less accurate? Reassessing standard choices for ab initio water using machine learning potentials
di: Beck, Hubert, et al.
Pubblicazione: (2026)

Systematic incorporation of nuclear quantum effects into atomistic simulations by smoothed trajectory analysis
di: Madarász, Ádám, et al.
Pubblicazione: (2026)

Hamiltonian transformation for accurate and efficient band structure interpolation
di: Wu, Kai, et al.
Pubblicazione: (2022)

Canonized then Minimized RMSD for Three-Dimensional Structures
di: Li, Jie, et al.
Pubblicazione: (2024)

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
di: Dral, Pavlo O., et al.
Pubblicazione: (2023)

Monomeric machine learning potential for general covalent molecules: linear alkanes as an example
di: Li, Xinze, et al.
Pubblicazione: (2026)

Multi-head committees enable direct uncertainty prediction for atomistic foundation models
di: Beck, Hubert, et al.
Pubblicazione: (2025)

How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
di: Kempen, Luuk H. E., et al.
Pubblicazione: (2025)

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
di: Bhatia, Nitik, et al.
Pubblicazione: (2025)

Constraint-aware functional cloning for stable and transferable machine-learned density functional theory
di: Navarro-Rodríguez, Sara, et al.
Pubblicazione: (2026)

Reconstructing Pristine Molecular Orbitals from Scanning Tunneling Microscopy Images via Artificial Intelligence Approaches
di: Zhu, Yu, et al.
Pubblicazione: (2025)

i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations
di: Litman, Yair, et al.
Pubblicazione: (2024)

Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
di: Falk, John, et al.
Pubblicazione: (2023)

A general formalism for machine-learning models based on multipolar-spherical harmonics
di: Domina, Michelangelo, et al.
Pubblicazione: (2025)

Bayesian learning for accurate and robust biomolecular force fields
di: Kostal, Vojtech, et al.
Pubblicazione: (2025)

MLQD: A package for machine learning-based quantum dissipative dynamics
di: Ullah, Arif, et al.
Pubblicazione: (2023)