Cover Image

Prediction rigidities for data-driven chemistry

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chong, Sanggyu, Bigi, Filippo, Grasselli, Federico, Loche, Philip, Kellner, Matthias, Ceriotti, Michele
Format:	Preprint
Published:	2024
Subjects:	Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
by: Chong, Sanggyu, et al.
Published: (2025)

FlashMD: long-stride, universal prediction of molecular dynamics
by: Bigi, Filippo, et al.
Published: (2025)

A prediction rigidity formalism for low-cost uncertainties in trained neural networks
by: Bigi, Filippo, et al.
Published: (2024)

A universal machine learning model for the electronic density of states
by: How, Wei Bin, et al.
Published: (2025)

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

Robustness of Local Predictions in Atomistic Machine Learning Models
by: Chong, Sanggyu, et al.
Published: (2023)

Adaptive energy reference for machine-learning models of the electronic density of states
by: How, Wei Bin, et al.
Published: (2024)

Uncertainty quantification by direct propagation of shallow ensembles
by: Kellner, Matthias, et al.
Published: (2024)

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

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

Learning the action for long-time-step simulations of molecular dynamics
by: Bigi, Filippo, et al.
Published: (2025)

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

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

Representing spherical tensors with scalar-based machine-learning models
by: Domina, Michelangelo, et al.
Published: (2025)

How to Train a Shallow Ensemble
by: Schäfer, Moritz, et al.
Published: (2026)

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

Learning Long-Range Representations with Equivariant Messages
by: Rumiantsev, Egor, et al.
Published: (2025)

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

Simultaneous Learning of Static and Dynamic Charges
by: Stärk, Philipp, et al.
Published: (2026)

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

Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
by: Kellner, Matthias, et al.
Published: (2026)

Quantum-corrected NMR crystallography at scale
by: Kellner, Matthias, et al.
Published: (2026)

A deep learning model for chemical shieldings in molecular organic solids including anisotropy
by: Kellner, Matthias, et al.
Published: (2025)

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

Atom-Density Representations for Machine Learning
by: Willatt, Michael J., et al.
Published: (2018)

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

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
by: Musil, Felix, et al.
Published: (2018)

Water cavitation results from the kinetic competition of bulk, surface and surface-defect nucleation events
by: Loche, Philip, et al.
Published: (2024)

Probing the effects of broken symmetries in machine learning
by: Langer, Marcel F., et al.
Published: (2024)

Mechanistic study of mixed lithium halides solid state electrolytes
by: Tisi, Davide, et al.
Published: (2025)

Path Integral Methods in Atomistic Modelling: An Introduction
by: Ceriotti, Michele, et al.
Published: (2026)

Smooth, exact rotational symmetrization for deep learning on point clouds
by: Pozdnyakov, Sergey N., et al.
Published: (2023)

A Transferable Machine-Learning Model of the Electron Density
by: Grisafi, Andrea, et al.
Published: (2018)

Completeness of Atomic Structure Representations
by: Nigam, Jigyasa, et al.
Published: (2023)

Accurate molecular polarizabilities with coupled-cluster theory and machine learning
by: Wilkins, David M., et al.
Published: (2018)

A data-driven sparse learning approach to reduce chemical reaction mechanisms
by: Fang, Shen, et al.
Published: (2024)

Modeling the structural and thermal properties of loaded metal-organic frameworks. An interplay of quantum and anharmonic fluctuations
by: Kapil, Venkat, et al.
Published: (2019)

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

Ab initio thermodynamics of liquid and solid water
by: Cheng, Bingqing, et al.
Published: (2018)

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