Uncertainty quantification of spectral predictions using deep neural networks

Sneha Verma, Nik Khadijah Nik Aznan, Kathryn Garside and Thomas J. Penfold

Chem. Commun., 2023, 59, 7100
DOI: 10.1039/D3CC01988H

AI in computational chemistry through the lens of a decade-long journey

Pavlo O. Dral

Chem. Commun., 2024, 60, 3240
DOI: 10.1039/D4CC00010B

Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023

Igor Poltavsky, Anton Charkin-Gorbulin, Mirela Puleva, Grégory Fonseca, Ilyes Batatia, Nicholas J. Browning, Stefan Chmiela, Mengnan Cui, J. Thorben Frank, Stefan Heinen, Bing Huang, Silvan Käser, Adil Kabylda, Danish Khan, Carolin Müller, Alastair J. A. Price, Kai Riedmiller, Kai Töpfer, Tsz Wai Ko, Markus Meuwly, Matthias Rupp, Gábor Csányi, O. Anatole von Lilienfeld, Johannes T. Margraf, Klaus-Robert Müller and Alexandre Tkatchenko

Chem. Sci., 2025, 16, 3720
DOI: 10.1039/D4SC06529H

Neural network potentials for chemistry: concepts, applications and prospects

Silvan Käser, Luis Itza Vazquez-Salazar, Markus Meuwly and Kai Töpfer

Digital Discovery, 2023, 2, 28
DOI: 10.1039/D2DD00102K

Augmenting Chemical Databases for Atomistic Machine Learning by Sampling Conformational Space

Luis Itza Vazquez-Salazar and Markus Meuwly

J. Chem. Inf. Model., 2025, 65, 8563
DOI: 10.1021/acs.jcim.5c00752

Uncertainty quantification by direct propagation of shallow ensembles

Matthias Kellner and Michele Ceriotti

Mach. Learn.: Sci. Technol., 2024, 5, 035006
DOI: 10.1088/2632-2153/ad594a

Uncertainty Quantification for In Silico Chemistry

Tom Frömbgen, Elizaveta Surzhikova, Jürgen Dölz, Jonny Proppe, Barbara Kirchner and Christoph R. Jacob

Chem. Rev., 2026
DOI: 10.1021/acs.chemrev.5c00931

Design, assessment, and application of machine learning potential energy surfaces

Valerii Andreichev, Sena Aydin, Kai Töpfer, Markus Meuwly and Luis Itza Vazquez-Salazar

Mach. Learn.: Sci. Technol., 2026, 7, 021001
DOI: 10.1088/2632-2153/ae47b9

PhysNet meets CHARMM: A framework for routine machine learning/molecular mechanics simulations

Kaisheng Song, Silvan Käser, Kai Töpfer, Luis Itza Vazquez-Salazar and Markus Meuwly

The Journal of Chemical Physics, 2023, 159
DOI: 10.1063/5.0155992

Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems

Arpita Poddar, Bhrigu Chakraborty and Pratim Kumar Chattaraj

ACS Phys. Chem Au, 2026
DOI: 10.1021/acsphyschemau.6c00002

Analysis of machine learning prediction reliability based on sampling distance evaluation with feature decorrelation

Evan Askanazi and Ilya Grinberg

Mach. Learn.: Sci. Technol., 2024, 5, 025030
DOI: 10.1088/2632-2153/ad4231

Comparing the Latent Features of Universal Machine‐Learning Interatomic Potentials

Sofiia Chorna, Davide Tisi, Cesare Malosso, Wei Bin How, Michele Ceriotti and Sanggyu Chong

Advanced Intelligent Systems, 2026
DOI: 10.1002/aisy.202501497

Data-Quality-Navigated Machine Learning Strategy with Chemical Intuition to Improve Generalization

Songran Yang, Ming Sun, Chaojie Shi, Yiran Liu, Yanzhi Guo, Yijing Liu, Zhiyun Lu, Yan Huang and Xuemei Pu

J. Chem. Theory Comput., 2024, 20, 10633
DOI: 10.1021/acs.jctc.4c00969

Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches

Bidhan Chandra Garain, Max Pinheiro, Matheus de Oliveira Bispo and Mario Barbatti

Chemistry A European J, 2026
DOI: 10.1002/chem.202503299

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets

Maria H. Rasmussen, Chenru Duan, Heather J. Kulik and Jan H. Jensen

J Cheminform, 2023, 15
DOI: 10.1186/s13321-023-00790-0

Asparagus: A toolkit for autonomous, user-guided construction of machine-learned potential energy surfaces

Kai Töpfer, Luis Itza Vazquez-Salazar and Markus Meuwly

Computer Physics Communications, 2025, 308, 109446
DOI: 10.1016/j.cpc.2024.109446

A Bayesian deep learning approach for rheological properties prediction of asphalt binders considering uncertainty of output

Bongjun Ji, Sushmit Sharma Bhattarai, Il-Ho Na and Hyunhwan Kim

Construction and Building Materials, 2023, 408, 133671
DOI: 10.1016/j.conbuildmat.2023.133671

PiNN: Equivariant Neural Network Suite for Modeling Electrochemical Systems

Jichen Li, Lisanne Knijff, Zhan-Yun Zhang, Linnéa Andersson and Chao Zhang

J. Chem. Theory Comput., 2025, 21, 1382
DOI: 10.1021/acs.jctc.4c01570

Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction

Yan Liu, Yiming Mo and Youwei Cheng

J. Chem. Inf. Model., 2024, 64, 8131
DOI: 10.1021/acs.jcim.4c01358

Calibration in machine learning uncertainty quantification: Beyond consistency to target adaptivity

Pascal Pernot

APL Machine Learning, 2023, 1
DOI: 10.1063/5.0174943

Outlier-detection for reactive machine learned potential energy surfaces

Luis Itza Vazquez-Salazar, Silvan Käser and Markus Meuwly

npj Comput Mater, 2025, 11
DOI: 10.1038/s41524-024-01473-6

Effects of aleatoric and epistemic errors in reference data on the learnability and quality of NN-based potential energy surfaces

Sugata Goswami, Silvan Käser, Raymond J. Bemish and Markus Meuwly

Artificial Intelligence Chemistry, 2024, 2, 100033
DOI: 10.1016/j.aichem.2023.100033

Recent Advances in Data‐Driven Design of Dual‐Atom Catalysts

Xinqi Chen, Haiyang Cheng, Ran Shi, Tong Zhou, Tianwei He and Qingju Liu

Adv Funct Materials, 2026, 36
DOI: 10.1002/adfm.202510647