Cover Image

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Nam, Juno, Peng, Jiayu, Gómez-Bombarelli, Rafael
Format:	Preprint
Published:	2024
Subjects:	Materials Science Machine Learning Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Transferable Learning of Reaction Pathways from Geometric Priors
by: Nam, Juno, et al.
Published: (2025)

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

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

Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
by: Peng, Jiayu, et al.
Published: (2024)

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

Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation
by: Yang, Soojung, et al.
Published: (2024)

Enhancing Diffusion-Based Sampling with Molecular Collective Variables
by: Nam, Juno, et al.
Published: (2025)

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

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

PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
by: Mazitov, Arslan, et al.
Published: (2025)

Learning from the electronic structure of molecules across the periodic table
by: Kaniselvan, Manasa, et al.
Published: (2025)

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)

Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
by: Liu, Junlan, 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)

Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search
by: Subramanian, Akshay, et al.
Published: (2024)

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

Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules
by: Segal, Nofit, et al.
Published: (2025)

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

Reducing cross-sample prediction churn in scientific machine learning
by: Prastalo, Gordan, et al.
Published: (2026)

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

Multimodal machine learning with large language embedding model for polymer property prediction
by: Zhang, Tianren, et al.
Published: (2025)

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

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

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

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

Latent Ewald summation for machine learning of long-range interactions
by: Cheng, Bingqing
Published: (2024)

Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $\mathrm{CO_2}$ hydrogenation energy barriers
by: Kempen, Luuk H. E., et al.
Published: (2025)

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

MBD-ML: Many-body dispersion from machine learning for molecules and materials
by: Moerman, Evgeny, et al.
Published: (2026)

Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials
by: Nam, Juno, et al.
Published: (2024)

Grad DFT: a software library for machine learning enhanced density functional theory
by: Casares, Pablo A. M., et al.
Published: (2023)

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)

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

Understanding active learning of molecular docking and its applications
by: Kim, Jeonghyeon, et al.
Published: (2024)

Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches
by: Peng, Jiayu, et al.
Published: (2026)

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

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

Machine learning Hubbard parameters with equivariant neural networks
by: Uhrin, Martin, et al.
Published: (2024)

Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials
by: Mahmoud, Chiheb Ben, et al.
Published: (2025)