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
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Enhancing high-dimensional neural network potential accuracy in OLED systems via element relabeling

Yonghwan Yun, Dongmin Park, Junyoung Choi, Dong Shin Choi and Yousung Jung

Digital Discovery, 2026
DOI: 10.1039/D5DD00579E

Effects of van der Waals Interaction on N2 Adsorption on Carbon Nanotubes: Proposal for New Force Field Parameters

Carlos Alberto Martins Junior, Henrique Musseli Cezar, Daniela Andrade Damasceno and Caetano Rodrigues Miranda

ACS Omega, 2026, 11, 18080
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Machine learning global atomic representations with Euclidean fast attention

J. Thorben Frank, Stefan Chmiela, Klaus-Robert Müller and Oliver T. Unke

Nat Mach Intell, 2026, 8, 388
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Lessons from the trenches on evaluating machine learning systems in materials science

Nawaf Alampara, Mara Schilling-Wilhelmi and Kevin Maik Jablonka

Computational Materials Science, 2025, 259, 114041
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A Perspective on Computer Simulation of Liquid Electrolytes for Energy Storage

Adriano Pierini and Enrico Bodo

ACS Electrochem., 2025, 1, 1231
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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
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QMeCha: Quantum Monte Carlo package for fermions in embedding environments

Matteo Barborini, Jorge Charry, Matej Ditte, Andronikos Leventis, Georgios Kafanas and Alexandre Tkatchenko

The Journal of Chemical Physics, 2026, 164
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Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Tsz Wai Ko, Bowen Deng, Marcel Nassar, Luis Barroso-Luque, Runze Liu, Ji Qi, Atul C. Thakur, Adesh Rohan Mishra, Elliott Liu, Gerbrand Ceder, Santiago Miret and Shyue Ping Ong

npj Comput Mater, 2025, 11
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Fine-Tuning Unifies Foundational Machine-Learned Interatomic Potential Architectures at ab initio Accuracy

Jonas Hänseroth, Aaron Flötotto, Muhammad Nawaz Qaisrani and Christian Dreßler

J. Phys. Chem. Lett., 2026, 17, 3152
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Unprecedented robustness of physics-informed atomic energy models at and beyond room temperature

Bienfait Kabuyaya Isamura, Olivia Aten, Mohamadhosein Nosratjoo and Paul Lode Albert Popelier

Commun Chem, 2026, 9
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On the design space between molecular mechanics and machine learning force fields

Yuanqing Wang, Kenichiro Takaba, Michael S. Chen, Marcus Wieder, Yuzhi Xu, Tong Zhu, John Z. H. Zhang, Arnav Nagle, Kuang Yu, Xinyan Wang, Daniel J. Cole, Joshua A. Rackers, Kyunghyun Cho, Joe G. Greener, Peter Eastman, Stefano Martiniani and Mark E. Tuckerman

Applied Physics Reviews, 2025, 12
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Short-Range Δ-Machine Learning: A Cost-Efficient Strategy to Transfer Chemical Accuracy to Condensed Phase Systems

Bence Balázs Mészáros, András Szabó and János Daru

J. Chem. Theory Comput., 2025, 21, 5372
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Are Foundational Atomistic Models Reliable for Finite-Temperature Molecular Dynamics?

Denan Li, Jiyuan Yang, Xiangkai Chen, Lintao Yu and Shi Liu

J. Phys. Chem. C, 2025, 129, 21538
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aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials

Tobias Henkes, Shubham Sharma, Alexandre Tkatchenko, Mariana Rossi and Igor Poltavsky

J. Chem. Inf. Model., 2026
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Machine learning force fields: recent advances and technical challenges in cross-disciplinary applications

Junbao Hu, Lifeng Xu and Jian Jiang

Sci. Sin.-Chim, 2025, 55, 1734
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Current Advances in Genome Modeling Across Length Scales

Eric R. Schultz, Jay L. Kaplan, Yiheng Wu, Soren Kyhl, Rebecca Willett and Juan J. de Pablo

WIREs Comput Mol Sci, 2025, 15
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Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides

Erik Andris, Ján Michael Kormaník, Tadeáš Kalvoda, Jan Řezáč and Lubomír Rulíšek

J. Chem. Inf. Model., 2026
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AI driven electrochemical interface design: Development and economic evaluation of low-cost electrode materials based on the 'structure-activity consumption' model

Tingfa Zhou, Peng Gui, Chao Hu, Weihua Xiao, Dong Liu and Zhiping Wang

Journal of Environmental Chemical Engineering, 2025, 13, 120039
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Machine-learning interatomic potentials from a users perspective: a comparison of accuracy, speed and data efficiency

Niklas Leimeroth, Linus C Erhard, Karsten Albe and Jochen Rohrer

Modelling Simul. Mater. Sci. Eng., 2025, 33, 065012
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