Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials

Kyle Noordhoek and Christopher J. Bartel

Nanoscale, 2024, 16, 6365
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Surface phase diagrams from nested sampling

Mingrui Yang, Livia B. Pártay and Robert B. Wexler

Phys. Chem. Chem. Phys., 2024, 26, 13862
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Exploring inhomogeneous surfaces: Ti-rich SrTiO3(110) reconstructions via active learning

Ralf Wanzenböck, Esther Heid, Michele Riva, Giada Franceschi, Alexander M. Imre, Jesús Carrete, Ulrike Diebold and Georg K. H. Madsen

Digital Discovery, 2024, 3, 2137
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Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials—A Review

Kaiwei Wan, Jianxin He and Xinghua Shi

Advanced Materials, 2024, 36
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Digging Its Own Site: Linear Coordination Stabilizes a Pt1/Fe2O3 Single-Atom Catalyst

Ali Rafsanjani-Abbasi, Florian Buchner, Faith J. Lewis, Lena Puntscher, Florian Kraushofer, Panukorn Sombut, Moritz Eder, Jiří Pavelec, Erik Rheinfrank, Giada Franceschi, Viktor Birschitzky, Michele Riva, Cesare Franchini, Michael Schmid, Ulrike Diebold, Matthias Meier, Georg K. H. Madsen and Gareth S. Parkinson

ACS Nano, 2024, 18, 26920
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Clinamen2: Functional-style evolutionary optimization in Python for atomistic structure searches

Ralf Wanzenböck, Florian Buchner, Péter Kovács, Georg K.H. Madsen and Jesús Carrete

Computer Physics Communications, 2024, 297, 109065
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Band Alignment of Oxides by Learnable Structural-Descriptor-Aided Neural Network and Transfer Learning

Shin Kiyohara, Yoyo Hinuma and Fumiyasu Oba

J. Am. Chem. Soc., 2024, 146, 9697
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GPAW: An open Python package for electronic structure calculations

Jens Jørgen Mortensen, Ask Hjorth Larsen, Mikael Kuisma, Aleksei V. Ivanov, Alireza Taghizadeh, Andrew Peterson, Anubhab Haldar, Asmus Ougaard Dohn, Christian Schäfer, Elvar Örn Jónsson, Eric D. Hermes, Fredrik Andreas Nilsson, Georg Kastlunger, Gianluca Levi, Hannes Jónsson, Hannu Häkkinen, Jakub Fojt, Jiban Kangsabanik, Joachim Sødequist, Jouko Lehtomäki, Julian Heske, Jussi Enkovaara, Kirsten Trøstrup Winther, Marcin Dulak, Marko M. Melander, Martin Ovesen, Martti Louhivuori, Michael Walter, Morten Gjerding, Olga Lopez-Acevedo, Paul Erhart, Robert Warmbier, Rolf Würdemann, Sami Kaappa, Simone Latini, Tara Maria Boland, Thomas Bligaard, Thorbjørn Skovhus, Toma Susi, Tristan Maxson, Tuomas Rossi, Xi Chen, Yorick Leonard A. Schmerwitz, Jakob Schiøtz, Thomas Olsen, Karsten Wedel Jacobsen and Kristian Sommer Thygesen

The Journal of Chemical Physics, 2024, 160
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Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning

Jesús Carrete, Hadrián Montes-Campos, Ralf Wanzenböck, Esther Heid and Georg K. H. Madsen

The Journal of Chemical Physics, 2023, 158
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Toward data-driven predictive modeling of electrocatalyst stability and surface reconstruction

Jiayu Peng

The Journal of Chemical Physics, 2025, 163
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Inverse catalysts: tuning the composition and structure of oxide clusters through the metal support

Luuk H. E. Kempen and Mie Andersen

npj Comput Mater, 2025, 11
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Neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia

Sebastian Bichelmaier, Jesús Carrete, Ralf Wanzenböck, Florian Buchner and Georg K. H. Madsen

Phys. Rev. B, 2023, 107
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Machine learning speeds up search for surface structure

Mie Andersen

Nat Comput Sci, 2023, 3, 1009
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Cascading symmetry constraint during machine learning-enabled structural search for sulfur-induced Cu(111)-(43×43) surface reconstruction

Florian Brix, Mads-Peter Verner Christiansen and Bjørk Hammer

The Journal of Chemical Physics, 2024, 160
DOI: 10.1063/5.0201421

Application of machine learning interatomic potentials in heterogeneous catalysis

Gbolagade Olajide, Khagendra Baral, Sophia Ezendu, Ademola Soyemi and Tibor Szilvási

Journal of Catalysis, 2025, 448, 116202
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ViPErLEED package I: Calculation of I(V) curves and structural optimization

Florian Kraushofer, Alexander M. Imre, Giada Franceschi, Tilman Kißlinger, Erik Rheinfrank, Michael Schmid, Ulrike Diebold, Lutz Hammer and Michele Riva

Phys. Rev. Research, 2025, 7
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Spatially Resolved Uncertainties for Machine Learning Potentials

Esther Heid, Johannes Schörghuber, Ralf Wanzenböck and Georg K. H. Madsen

J. Chem. Inf. Model., 2024, 64, 6377
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Structural Disorder by Octahedral Tilting in Inorganic Halide Perovskites: New Insight with Bayesian Optimization

Jingrui Li, Fang Pan, Guo‐Xu Zhang, Zenghui Liu, Hua Dong, Dawei Wang, Zhuangde Jiang, Wei Ren, Zuo‐Guang Ye, Milica Todorović and Patrick Rinke

Small Structures, 2024, 5
DOI: 10.1002/sstr.202400268