From flat to stepped: active learning frameworks for investigating local structure at copper–water interfaces

Johannes Schörghuber, Nina Bučková, Esther Heid and Georg K. H. Madsen

Phys. Chem. Chem. Phys., 2025, 27, 9169
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Insights into the structure and dynamics of K+ ions at the muscovite–water interface from machine learning potential simulations

Abhinav S. Raman and Annabella Selloni

The Journal of Chemical Physics, 2024, 160
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Metal–Organic Frameworks through the Lens of Artificial Intelligence: A Comprehensive Review

Kevizali Neikha and Amrit Puzari

Langmuir, 2024, 40, 21957
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Unveiling the face-dependent ice growth kinetics: Insights from molecular dynamics on the basal and prism surfaces

Jihong Shi, Maxwell Fulford, Matteo Salvalaglio and Carla Molteni

The Journal of Chemical Physics, 2025, 162
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Unconventional ice nucleation pathway induced by irregular silver iodide surfaces

Yaochen Yu, Mingyi Chen, Yuefeng Lei and Haiyang Niu

Commun Phys, 2025, 8
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An ab-initio deep neural network potential for accurate large-scale simulations of the muscovite mica-water interface

Abhinav S. Raman and Annabella Selloni

Molecular Physics, 2025, 123
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Deep potential molecular dynamics simulations of low-temperature plasma-surface interactions

Andreas Kounis-Melas, Joseph R. Vella, Athanassios Z. Panagiotopoulos and David B. Graves

Journal of Vacuum Science & Technology A, 2025, 43
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Effects of quenched disorder on the kinetics and pathways of phase transition in a soft colloidal system

Gadha Ramesh, Mantu Santra and Rakesh S. Singh

The Journal of Chemical Physics, 2025, 162
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Ab initio machine-learning simulation of calcium carbonate from aqueous solutions to the solid state

Pablo M. Piaggi, Julian D. Gale and Paolo Raiteri

Proc. Natl. Acad. Sci. U.S.A., 2025, 122
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Machine Learning Potentials for Heterogeneous Catalysis

Amir Omranpour, Jan Elsner, K. Nikolas Lausch and Jörg Behler

ACS Catal., 2025, 15, 1616
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Consistent density functional theory-based description of ion hydration through density-corrected many-body representations

Etienne Palos, Alessandro Caruso and Francesco Paesani

The Journal of Chemical Physics, 2023, 159
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Machine learning-accelerated path integral molecular dynamics simulations of reactive organic electrolytes

Michael S. Chen, Alan Robledo, Christian Schäfer, Katherine Y. Han, Cecilia Clementi and Mark E. Tuckerman

The Journal of Chemical Physics, 2025, 163
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Deciphering the complexities of crystalline state(s) with molecular simulations

Caroline Desgranges and Jerome Delhommelle

Commun Chem, 2025, 8
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Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials

Amir Omranpour, Pablo Montero De Hijes, Jörg Behler and Christoph Dellago

The Journal of Chemical Physics, 2024, 160
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Neural Network Atomistic Potential for Pyrophyllite Clay Simulations

Chloe Sanz, Abdul-Rahman Allouche, Colin Bousige and Pierre Mignon

J. Phys. Chem. A, 2025, 129, 3567
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Investigation of Data Set Portability on Various Machine-Learned Interaction Potentials for Pyrophyllite Clay

Chloe Sanz, Colin Bousige and Pierre Mignon

J. Phys. Chem. A, 2026, 130, 581
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Structure and thermodynamics of defects in Na-feldspar from a neural network potential

Alexander Gorfer, Rainer Abart and Christoph Dellago

Phys. Rev. Materials, 2024, 8
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Long timescale molecular dynamics simulations of carboxylic acid-modified anatase TiO2(101)-water interfaces using ab-initio deep neural network potentials

Abhinav S. Raman and Annabella Selloni

Surface Science, 2024, 750, 122595
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Force field dependence of ion transport on mica surfaces

Benqiang Wang

J. Phys.: Conf. Ser., 2026, 3197, 012018
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Understanding the heterogeneous nucleation of ice on silver iodide using deep potential molecular dynamics

Yaochen Yu and Haiyang Niu

The Journal of Chemical Physics, 2025, 163
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Machine Learning Driven Advances in Molecular Dynamics of Bulk and Interfacial Aqueous Systems

Ruiyu Wang, Vanessa J. Meraz and Pratyush Tiwary

Chem. Rev., 2026, 126, 3730
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Transferable Water Potentials Using Equivariant Neural Networks

Tristan Maxson and Tibor Szilvási

J. Phys. Chem. Lett., 2024, 15, 3740
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Machine learning for mineralogy: From atoms to earth system

Zhuohao Zhao, Hongyang Chen, Xiaohui Ji, Xiancai Lu and Wei Li

Earth-Science Reviews, 2026, 278, 105514
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A close look at the hydration layer and at the premelting layer of K-feldspar

B. V. Ramírez, L. Lupi and P. Gallo

Molecular Physics, 2024, 122
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How Water Binds to Microcline Feldspar (001)

Giada Franceschi, Andrea Conti, Luca Lezuo, Rainer Abart, Florian Mittendorfer, Michael Schmid and Ulrike Diebold

J. Phys. Chem. Lett., 2024, 15, 15
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Using machine learning with atomistic surface and local water features to predict heterogeneous ice nucleation

Abhishek Soni and G. N. Patey

The Journal of Chemical Physics, 2024, 160
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Clay Mineral‐Based Photocatalysis

Sen Lin, Shiyong Sun, Haoming Tang, Xiang Li and Jun Liu

Advanced Sustainable Systems, 2025, 9
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Structure and Dynamics of CO2 at the Air–Water Interface from Classical and Neural Network Potentials

Nitesh Kumar and Vyacheslav S. Bryantsev

J. Phys. Chem. Lett., 2025, 16, 5619
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Computational Modeling of Solid–Liquid Interfaces: From Ab Initio Methods to Machine-Learning Potentials

Abhinav S. Raman

J Indian Inst Sci, 2025
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Evaluating the use of a machine learning simulator for structure–property prediction: A case study on disordered elastic networks

Salman N. Salman, Sergey A. Shteingolts, Ron Levie and Dan Mendels

The Journal of Chemical Physics, 2025, 163
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Early stages of self-healing at tungsten grain boundaries from ab initio machine learning simulations

Jorge Suárez-Recio, Pablo M. Piaggi, Francisco J. Domínguez-Gutiérrez, Raquel Gonzalez-Arrabal and Roberto Iglesias

Commun Mater, 2025, 6
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Atomistic simulations of nucleation and growth of CaCO3 with the influence of inhibitors: A review

Yue Li, Hongbo Zeng and Hao Zhang

Materials Genome Engineering Advances, 2023, 1
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