Cover Image

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bhatia, Nitik, Rinke, Patrick, Krejci, Ondrej
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Benchmarking machine-learned interatomic potentials for molecular infrared spectroscopy
by: Bhatia, Nitik, et al.
Published: (2026)

MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy
by: Bhatia, Nitik, et al.
Published: (2025)

Machine Learning Accelerated Descriptor Design for Catalyst Discovery in CO$_2$ to Methanol Conversion
by: Pisal, Prajwal, et al.
Published: (2024)

Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
by: Pisal, Prajwal, et al.
Published: (2026)

An interpretable molecular descriptor for machine learning predictions in atmospheric science
by: Lind, Linus, et al.
Published: (2025)

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

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

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

Development of machine-learned interatomic potentials to predict structure, transport, and reactivity in platinum-based fuel cells
by: Fazel, Kamron, et al.
Published: (2025)

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

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

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

ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials
by: David, Rolf, et al.
Published: (2024)

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

Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials
by: Vorotnikov, Igor, et al.
Published: (2025)

Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
by: Stark, Wojciech G., et al.
Published: (2024)

Overcoming sampling limitations using machine-learned interatomic potentials: the case of water-in-salt electrolytes
by: Brugnoli, Luca, et al.
Published: (2026)

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

Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
by: Messerly, Mitchell, et al.
Published: (2025)

Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)

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

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

Density diversity in training data governs thermodynamic transferability of machine learning interatomic potentials
by: Kim, Minwoo, et al.
Published: (2026)

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

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

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
by: Nam, Juno, et al.
Published: (2024)

Accurate nuclear quantum statistics on machine-learned classical effective potentials
by: Zaporozhets, Iryna, et al.
Published: (2024)

High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo
by: Goswami, Shubhang, et al.
Published: (2024)

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials
by: Farris, Riccardo, et al.
Published: (2025)

Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential
by: Ibragimova, Rina, et al.
Published: (2024)

Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water
by: Stolte, Nore, et al.
Published: (2024)

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

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

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

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

A charge-density machine-learning workflow for computing the infrared spectrum of molecules
by: Hazra, Suman, et al.
Published: (2025)

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

Understanding multi-fidelity training of machine-learned force-fields
by: Gardner, John L. A., et al.
Published: (2025)

Automated all-functionals infrared and Raman spectra
by: Bastonero, Lorenzo, et al.
Published: (2023)

Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework
by: Delesma, Francisco A., et al.
Published: (2023)