Uncertainty quantification for predictions of atomistic neural networks-Reference-Cited by-同舟云学术

Uncertainty quantification for predictions of atomistic neural networks

Published:2022 Issue:44 Volume:13 Page:13068-13084
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Vazquez-Salazar Luis Itza¹^ORCID,Boittier Eric D.¹,Meuwly Markus¹²^ORCID

Affiliation:

1. Department of Chemistry, University of Basel, Basel, Switzerland

2. Department of Chemistry, Brown University, USA

Abstract

A model for uncertainty quantification for atomistic neural networks is introduced. Results from different chemical contexts and the evaluation, meaning and chemical interpretation of uncertainty are explored.

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Universität Basel

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SC/D2SC04056E

Reference75 articles.

1. Machine Learning for Chemical Reactions

2. Double proton transfer in hydrated formic acid dimer: Interplay of spatial symmetry and solvent-generated force on reactivity

3. Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning

4. Neural Network Potential Energy Surfaces for Small Molecules and Reactions

5. Permutationally Invariant, Reproducing Kernel-Based Potential Energy Surfaces for Polyatomic Molecules: From Formaldehyde to Acetone

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Uncertainty quantification by direct propagation of shallow ensembles;Machine Learning: Science and Technology;2024-07-05

2. Machine Learning Regions of Reliability based on Sampling Distance Evaluation with Feature Decorrelation for Tabular Time Datasets;2024-06-20

3. Effects of aleatoric and epistemic errors in reference data on the learnability and quality of NN-based potential energy surfaces;Artificial Intelligence Chemistry;2024-06

4. Analysis of machine learning prediction reliability based on sampling distance evaluation with feature decorrelation;Machine Learning: Science and Technology;2024-05-03

5. AI in computational chemistry through the lens of a decade-long journey;Chemical Communications;2024