- Ruhr-Universität Bochum
Scientific output
Publications
Below, you can either scroll through the complete list of our annually published research in peer-reviewed journals or search for a specific author or keyword via the free text search.
- 2026 • 80
Machine Learning-Based Surrogate Modelling for Efficient Inverse Analysis of Micro-Indentation Response to Determine Material Parameters
Sajjad, Sidrah; Knorr, Sebastian; Schellenberg, Dirk; Chudoba, Thomas; Clausner, André; Hartmaier, Alexander
MATERIALS. Volume: 19 (2026) - 2026 • 79
The basis for future personalized therapy approaches – Machine learning-generated 1-year survival rate, metastatic status and therapy-dependent survival in pancreatic cancer patients
Schneider, François; Chen, Haotian; Pelzer, Uwe; Compton, Richard G.; Duwe, Gregor; Dragomir, Mihnea P.; Hilfenhaus, Georg; Vecchione, Loredana; Alig, Annabel; Felsenstein, Matthäus; Lerchbaumer, Markus; Rieke, Damian T.; Bahra, Marcus; Kutsche, Ralf; Tschulik, Kristina; Stintzing, Sebastian; Keilholz, Ulrich; Neumann, Christopher C.M.
EUROPEAN JOURNAL OF CANCER. Volume: 234 (2026) - 2026 • 78
Machine learning driven exploration of hydride superconductors at ambient pressure
Pires, Paulo R.; da Silva, Thalis H.B.; Gao, Kun; Hiorth, Kaja H.; Cerqueira, Tiago F.T.; Cavignac, Théo; De Breuck, Pierre-Paul; Wang, Hai-Chen; Dangić, Đorđe; Fang, Yue-Wen; Sanna, Antonio; Cui, Wenwen; Errea, Ion; Törmä, Päivi; Marques, Miguel A.L.
COMPUTATIONAL MATERIALS TODAY. Volume: 10 (2026) - 2025 • 77
Accelerating point defect photo-emission calculations with machine learning interatomic potentials
Sharma, K. and Loew, A. and Wang, H. and Nilsson, F. A. and Jain, M. and Marques, M. A. L. and Thygesen, K. S.
NPJ COMPUTATIONAL MATERIALS. Volume: 11 (2025) - 2025 • 76
Universal machine learning potentials under pressure
Loew, Antoine; Schmidt, Jonathan; Botti, Silvana; Marques, Miguel A L
JOURNAL OF PHYSICS: MATERIALS. Volume: 9 (2025) - 2025 • 75
Random Sampling Versus Active Learning Algorithms for Machine Learning Potentials of Quantum Liquid Water
Stolte, N. and Daru, J. and Forbert, H. A. and Marx, D. and Behler, J.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION. Volume: 21 (2025) - 2025 • 74
Automated generation of structure datasets for machine learning potentials and alloys
Poul, M. and Huber, L. and Neugebauer, J.
NPJ COMPUTATIONAL MATERIALS. Volume: 11 (2025) - 2025 • 73
Descriptors based on the density of states for efficient machine learning of grain-boundary segregation energies
Dösinger, C. and Hammerschmidt, T. and Peil, O. E. and Scheiber, D. and Romaner, L.
COMPUTATIONAL MATERIALS SCIENCE. Volume: 247 (2025) - 2025 • 72
Universal machine learning interatomic potentials are ready for phonons
Loew, A. and Sun, D. and Wang, H. and Botti, S. and Marques, M. A. L.
NPJ COMPUTATIONAL MATERIALS. Volume: 11 (2025) - 2024 • 71
Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials
Omranpour, A. and Montero De Hijes, P. and Behler, J. and Dellago, C.
JOURNAL OF CHEMICAL PHYSICS. Volume: 160 (2024) - 2024 • 70
Digital color analysis and machine learning for ballpoint pen ink clustering and aging investigation
Golovkina, A. G. and Karpukhin, O. R. and Kravchenko, A. V. and Khairullina, E. M. and Tumkin, I. I. and Kalinichev, A. V.
FORENSIC SCIENCE INTERNATIONAL. Volume: 364 (2024) - 2024 • 69
Accelerating ab initio melting property calculations with machine learning: application to the high entropy alloy TaVCrW
Zhu, L. and Körmann, F. and Chen, Q. and Selleby, M. and Neugebauer, J. and Grabowski, B.
NPJ COMPUTATIONAL MATERIALS. Volume: 10 (2024) - 2024 • 68
From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows
Menon, S. and Lysogorskiy, Y. and Knoll, A. L. M. and Leimeroth, N. and Poul, M. and Qamar, M. and Janssen, J. and Mrovec, M. and Rohrer, J. and Albe, K. and Behler, J. and Drautz, R. and Neugebauer, J.
NPJ COMPUTATIONAL MATERIALS. Volume: 10 (2024) - 2024 • 67
A machine learning constitutive model for plasticity and strain hardening of polycrystalline metals based on data from micromechanical simulations
Shoghi, R. and Hartmaier, A.
MACHINE LEARNING: SCIENCE AND TECHNOLOGY. Volume: 5 (2024) - 2024 • 66
Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion
Shoghi, R. and Morand, L. and Helm, D. and Hartmaier, A.
COMPUTATIONAL MECHANICS. Volume: 74 (2024) - 2024 • 65
Ensuring Part Quality for Material Extrusion by Developing a Methodology for Use-Case-Specific Parameter Set Determination Using Machine Learning Models
Schmidt, C. and Griesbaum, R. and Sehrt, J. T. and Finsterwalder, F.
JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING. Volume: 8 (2024) - 2024 • 64
Machine Learning-Enabled Tomographic Imaging of Chemical Short-Range Atomic Ordering
Li, Y. and Colnaghi, T. and Gong, Y. and Zhang, H. and Yu, Y. and Wei, Y. and Gan, B. and Song, M. and Marek, A. and Rampp, M. and Zhang, S. and Pei, Z. and Wuttig, M. and Ghosh, S. and Körmann, F. and Neugebauer, J. and Wang, Z. and Gault, B.
ADVANCED MATERIALS. Volume: 36 (2024) - 2024 • 63
Machine learning enhanced evaluation of semiconductor quantum dots
Corcione, E. and Jakob, F. and Wagner, L. and Joos, R. and Bisquerra, A. and Schmidt, M. and Wieck, A. D. and Ludwig, A. and Jetter, M. and Portalupi, S. L. and Michler, P. and Tarín, C.
SCIENTIFIC REPORTS. Volume: 14 (2024) - 2024 • 62
Accelerating Fourth-Generation Machine Learning Potentials Using Quasi-Linear Scaling Particle Mesh Charge Equilibration
Gubler, M. and Finkler, J. A. and Schäfer, M. R. and Behler, J. and Goedecker, S.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION. Volume: (2024) - 2024 • 61
Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces
Zare, M. and Sahsah, D. and Saleheen, M. and Behler, J. and Heyden, A.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION. Volume: (2024) - 2023 • 60
Symmetry-based computational search for novel binary and ternary 2D materials
Wang, H. and Schmidt, J. and Marques, M. A. L. and Wirtz, L. and Romero, A. H.
2D MATERIALS. Volume: 10 (2023) - 2023 • 59
Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding
Ko, T.W. and Finkler, J.A. and Goedecker, S. and Behler, J.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION. Volume: 19 (2023) - 2023 • 58
Site occupancies in a chemically complex σ-phase from the high-entropy Cr–Mn–Fe–Co–Ni system
Joubert, J. and Kalchev, Y. and Fantin, A. and Crivello, J. and Zehl, R. and Elkaim, E. and Laplanche, G.
ACTA MATERIALIA. Volume: 259 (2023) - 2023 • 57
Machine learning transferable atomic forces for large systems from underconverged molecular fragments
Herbold, M. and Behler, J.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS. Volume: 25 (2023) - 2023 • 56
Influence of Surface Roughness on Material Classification for Reflective THz-TDS Measurements
Gassel, S. T. and Hofmann, M. R. and Brenner, C.
INTERNATIONAL CONFERENCE ON INFRARED, MILLIMETER, AND TERAHERTZ WAVES, IRMMW-THZ. Volume: (2023) - 2023 • 55
Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials
Schmidt, J. and Hoffmann, N. and Wang, H. and Borlido, P. and Carriço, P. J. M. A. and Cerqueira, T. F. T. and Botti, S. and Marques, M. A. L.
ADVANCED MATERIALS. Volume: 35 (2023) - 2023 • 54
Machine learning guided high-throughput search of non-oxide garnets
Schmidt, J. and Wang, H. and Schmidt, G. and Marques, M. A. L.
NPJ COMPUTATIONAL MATERIALS. Volume: 9 (2023) - 2023 • 53
How to train a neural network potential
Tokita, A. M. and Behler, J.
JOURNAL OF CHEMICAL PHYSICS. Volume: 159 (2023) - 2023 • 52
Systematic atomic structure datasets for machine learning potentials: Application to defects in magnesium
Poul, M. and Huber, L. and Bitzek, E. and Neugebauer, J.
PHYSICAL REVIEW B. Volume: 107 (2023) - 2023 • 51
Atomic cluster expansion for Pt–Rh catalysts: From ab initio to the simulation of nanoclusters in few steps
Liang, Y. and Mrovec, M. and Lysogorskiy, Y. and Vega-Paredes, M. and Scheu, C. and Drautz, R.
JOURNAL OF MATERIALS RESEARCH. Volume: 38 (2023) - 2023 • 50
Hydrogen atom scattering at the Al2O3(0001) surface: a combined experimental and theoretical study
Liebetrau, M. and Dorenkamp, Y. and Bünermann, O. and Behler, J.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS. Volume: 26 (2023) - 2022 • 49
Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data
Zhou, X. and Wei, Y. and Kühbach, M. and Zhao, H. and Vogel, F. and Darvishi Kamachali, R. and Thompson, G.B. and Raabe, D. and Gault, B.
ACTA MATERIALIA. Volume: 226 (2022) - 2022 • 48
Machine learning for molecular simulations of crystal nucleation and growth
Sarupria, S. and Hall, S.W. and Rogal, J.
MRS BULLETIN. Volume: (2022) - 2022 • 47
Emergence of Machine Learning Techniques in Ultrasonic Guided Wave-based Structural Health Monitoring: A Narrative Review
Sattarifar, A. and Nestorović, T.
INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT. Volume: 13 (2022) - 2022 • 46
Neural Network Potentials: A Concise Overview of Methods
Kocer, E. and Ko, T.W. and Behler, J.
ANNUAL REVIEW OF PHYSICAL CHEMISTRY. Volume: 73 (2022) - 2022 • 45
Machine learning–enabled high-entropy alloy discovery
Rao, Z. and Tung, P.-Y. and Xie, R. and Wei, Y. and Zhang, H. and Ferrari, A. and Klaver, T.P.C. and Körmann, F. and Sukumar, P.T. and da Silva, A.K. and Chen, Y. and Li, Z. and Ponge, D. and Neugebauer, J. and Gutfleisch, O. and Bauer, S. and Raabe, D.
SCIENCE. Volume: 378 (2022) - 2022 • 44
Coupled Cluster Molecular Dynamics of Condensed Phase Systems Enabled by Machine Learning Potentials: Liquid Water Benchmark
Daru, J. and Forbert, H. and Behler, J. and Marx, D.
PHYSICAL REVIEW LETTERS. Volume: 129 (2022) - 2022 • 43
Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopy
Bachmann, B.-I. and Müller, M. and Britz, D. and Durmaz, A.R. and Ackermann, M. and Shchyglo, O. and Staudt, T. and Mücklich, F.
FRONTIERS IN MATERIALS. Volume: 9 (2022) - 2022 • 42
Short-range order and phase stability of CrCoNi explored with machine learning potentials
Ghosh, S. and Sotskov, V. and Shapeev, A.V. and Neugebauer, J. and Körmann, F.
PHYSICAL REVIEW MATERIALS. Volume: 6 (2022) - 2022 • 41
A Hessian-based assessment of atomic forces for training machine learning interatomic potentials
Herbold, M. and Behler, J.
JOURNAL OF CHEMICAL PHYSICS. Volume: 156 (2022) - 2022 • 40
Optimal Data-Generation Strategy for Machine Learning Yield Functions in Anisotropic Plasticity
Shoghi, R. and Hartmaier, A.
FRONTIERS IN MATERIALS. Volume: 9 (2022) - 2022 • 39
Chapter 11: Pathways in Classification Space: Machine Learning as a Route to Predicting Kinetics of Structural Transitions in Atomic Crystals
Rogal, J. and Tuckerman, M.E.
RSC THEORETICAL AND COMPUTATIONAL CHEMISTRY SERIES. Volume: 2022-January (2022) - 2022 • 38
Roadmap on Machine learning in electronic structure
Kulik, H.J. and Hammerschmidt, T. and Schmidt, J. and Botti, S. and Marques, M.A.L. and Boley, M. and Scheffler, M. and Todorović, M. and Rinke, P. and Oses, C. and Smolyanyuk, A. and Curtarolo, S. and Tkatchenko, A. and Bartók, A.P. and Manzhos, S. and Ihara, M. and Carrington, T. and Behler, J. and Isayev, O. and Veit, M. and Grisafi, A. and Nigam, J. and Ceriotti, M. and Schütt, K.T. and Westermayr, J. and Gastegger, M. and Maurer, R.J. and Kalita, B. and Burke, K. and Nagai, R. and Akashi, R. and Sugino, O. and Hermann, J. and Noé, F. and Pilati, S. and Draxl, C. and Kuban, M. and Rigamonti, S. and Scheidgen, M. and Esters, M. and Hicks, D. and Toher, C. and Balachandran, P.V. and Tamblyn, I. and Whitelam, S. and Bellinger, C. and Ghiringhelli, L.M.
ELECTRONIC STRUCTURE. Volume: 4 (2022) - 2021 • 37
Machine-learning-enhanced time-of-flight mass spectrometry analysis
Wei, Y. and Varanasi, R.S. and Schwarz, T. and Gomell, L. and Zhao, H. and Larson, D.J. and Sun, B. and Liu, G. and Chen, H. and Raabe, D. and Gault, B.
PATTERNS. Volume: 2 (2021) - 2021 • 36
Ab initio based models for temperature-dependent magnetochemical interplay in bcc Fe-Mn alloys
Schneider, A. and Fu, C.-C. and Waseda, O. and Barreteau, C. and Hickel, T.
PHYSICAL REVIEW B. Volume: 103 (2021) - 2021 • 35
A bin and hash method for analyzing reference data and descriptors in machine learning potentials
Paleico, M.L. and Behler, J.
MACHINE LEARNING: SCIENCE AND TECHNOLOGY. Volume: 2 (2021) - 2021 • 34
Online Geological Anomaly Detection Using Machine Learning in Mechanized Tunneling
Cao, B.-T. and Saadallah, A. and Egorov, A. and Freitag, S. and Meschke, G. and Morik, K.
LECTURE NOTES IN CIVIL ENGINEERING. Volume: 125 (2021) - 2021 • 33
Segmentation of Static and Dynamic Atomic-Resolution Microscopy Data Sets with Unsupervised Machine Learning Using Local Symmetry Descriptors
Wang, N. and Freysoldt, C. and Zhang, S. and Liebscher, C.H. and Neugebauer, J.
MICROSCOPY AND MICROANALYSIS. Volume: (2021) - 2021 • 32
Reduction of surface morphology influence on THz reflection time domain spectroscopy for material classification by using multiple observation angles
Becke, L. and Gerling, A. and Hofmann, M.R. and Brenner, C.
PROCEEDINGS OF SPIE - THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING. Volume: 11685 (2021) - 2021 • 31
A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
Ko, T.W. and Finkler, J.A. and Goedecker, S. and Behler, J.
NATURE COMMUNICATIONS. Volume: 12 (2021) - 2021 • 30
General-Purpose Machine Learning Potentials Capturing Nonlocal Charge Transfer
Ko, T.W. and Finkler, J.A. and Goedecker, S. and Behler, J.
ACCOUNTS OF CHEMICAL RESEARCH. Volume: 54 (2021) - 2021 • 29
A MULTISCALE VISION—ILLUSTRATIVE APPLICATIONS FROM BIOLOGY TO ENGINEERING
Schlick, T. and Portillo-Ledesma, S. and Blaszczyk, M. and Dalessandro, L. and Ghosh, S. and Hackl, K. and Harnish, C. and Kotha, S. and Livescu, D. and Masud, A. and Matouš, K. and Moyeda, A. and Oskay, C. and Fish, J.
INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING. Volume: 19 (2021) - 2021 • 28
Local Latin hypercube refinement for multi-objective design uncertainty optimization[Formula presented]
Bogoclu, C. and Roos, D. and Nestorović, T.
APPLIED SOFT COMPUTING. Volume: 112 (2021) - 2021 • 27
An assessment of the structural resolution of various fingerprints commonly used in machine learning
Parsaeifard, B. and Sankar De, D. and Christensen, A.S. and Faber, F.A. and Kocer, E. and De, S. and Behler, J. and von Lilienfeld, O.A. and Goedecker, S.
MACHINE LEARNING: SCIENCE AND TECHNOLOGY. Volume: 2 (2021) - 2021 • 26
Insights into lithium manganese oxide-water interfaces using machine learning potentials
Eckhoff, M. and Behler, J.
JOURNAL OF CHEMICAL PHYSICS. Volume: 155 (2021) - 2021 • 25
High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions
Eckhoff, M. and Behler, J.
NPJ COMPUTATIONAL MATERIALS. Volume: 7 (2021) - 2021 • 24
Four Generations of High-Dimensional Neural Network Potentials
Behler, J.
CHEMICAL REVIEWS. Volume: 121 (2021) - 2021 • 23
Machine learning potentials for extended systems: a perspective
Behler, J. and Csányi, G.
EUROPEAN PHYSICAL JOURNAL B. Volume: 94 (2021) - 2021 • 22
Automated image analysis for quantification of materials microstructure evolution
Ahmed, M. and Horst, O.M. and Obaied, A. and Steinbach, I. and Roslyakova, I.
MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING. Volume: 29 (2021) - 2021 • 21
Applying machine learning to optical coherence tomography images for automated tissue classification in brain metastases
Möller, J. and Bartsch, A. and Lenz, M. and Tischoff, I. and Krug, R. and Welp, H. and Hofmann, M.R. and Schmieder, K. and Miller, D.
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY. Volume: (2021) - 2021 • 20
Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials
Gubaev, K. and Ikeda, Y. and Tasnádi, F. and Neugebauer, J. and Shapeev, A.V. and Grabowski, B. and Körmann, F.
PHYSICAL REVIEW MATERIALS. Volume: 5 (2021) - 2021 • 19
Mechanism of amorphous phase stabilization in ultrathin films of monoatomic phase change material
Dragoni, D. and Behler, J. and Bernasconi, M.
NANOSCALE. Volume: 13 (2021) - 2020 • 18
Machine learning for metallurgy II. A neural-network potential for magnesium
Stricker, M. and Yin, B. and Mak, E. and Curtin, W.A.
PHYSICAL REVIEW MATERIALS. Volume: 4 (2020) - 2020 • 17
Insights into Water Permeation through hBN Nanocapillaries by Ab Initio Machine Learning Molecular Dynamics Simulations
Ghorbanfekr, H. and Behler, J. and Peeters, F.M.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS. Volume: 11 (2020) - 2020 • 16
Performance and Cost Assessment of Machine Learning Interatomic Potentials
Zuo, Y. and Chen, C. and Li, X. and Deng, Z. and Chen, Y. and Behler, J. and Csányi, G. and Shapeev, A.V. and Thompson, A.P. and Wood, M.A. and Ong, S.P.
JOURNAL OF PHYSICAL CHEMISTRY A. Volume: 124 (2020) - 2020 • 15
Data-oriented constitutive modeling of plasticity in metals
Hartmaier, A.
MATERIALS. Volume: 13 (2020) - 2019 • 14
A machine learning approach for automated fine-tuning of semiconductor spin qubits
Teske, J.D. and Humpohl, S.S. and Otten, R. and Bethke, P. and Cerfontaine, P. and Dedden, J. and Ludwig, Ar. and Wieck, A.D. and Bluhm, H.
APPLIED PHYSICS LETTERS. Volume: 114 (2019) - 2019 • 13
High-Dimensional Neural Network Potentials for Atomistic Simulations
Hellström, M. and Behler, J.
ACS SYMPOSIUM SERIES. Volume: 1326 (2019) - 2019 • 12
Non-Destructive Testing of 3D-printed Samples based on Machine Learning
Elsaadouny, M. and Barowski, J. and Rolfes, I.
IMWS-AMP 2019 - 2019 IEEE MTT-S INTERNATIONAL MICROWAVE WORKSHOP SERIES ON ADVANCED MATERIALS AND PROCESSES FOR RF AND THZ APPLICATIONS. Volume: (2019) - 2019 • 11
Ab initio thermodynamics of liquid and solid water
Cheng, B. and Engel, E.A. and Behler, J. and Dellago, C. and Ceriotti, M.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA. Volume: 116 (2019) - 2019 • 10
Structure and Dynamics of the Liquid-Water/Zinc-Oxide Interface from Machine Learning Potential Simulations
Quaranta, V. and Behler, J. and Hellström, M.
JOURNAL OF PHYSICAL CHEMISTRY C. Volume: 123 (2019) - 2019 • 9
Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations
Reimann, D. and Nidadavolu, K. and ul Hassan, H. and Vajragupta, N. and Glasmachers, T. and Junker, P. and Hartmaier, A.
FRONTIERS IN MATERIALS. Volume: 6 (2019) - 2019 • 8
Intelligent optimization and machine learning algorithms for structural anomaly detection using seismic signals
Trapp, M. and Bogoclu, C. and Nestorović, T. and Roos, D.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING. Volume: 133 (2019) - 2018 • 7
Machine-learning-based atom probe crystallographic analysis
Wei, Y. and Gault, B. and Varanasi, R.S. and Raabe, D. and Herbig, M. and Breen, A.J.
ULTRAMICROSCOPY. Volume: 194 (2018) - 2018 • 6
Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
Imbalzano, G. and Anelli, A. and Giofré, D. and Klees, S. and Behler, J. and Ceriotti, M.
JOURNAL OF CHEMICAL PHYSICS. Volume: 148 (2018) - 2018 • 5
A machine learning approach to model solute grain boundary segregation
Huber, L. and Hadian, R. and Grabowski, B. and Neugebauer, J.
NPJ COMPUTATIONAL MATERIALS. Volume: 4 (2018) - 2018 • 4
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
Nguyen, T.T. and Székely, E. and Imbalzano, G. and Behler, J. and Csányi, G. and Ceriotti, M. and Götz, A.W. and Paesani, F.
JOURNAL OF CHEMICAL PHYSICS. Volume: 148 (2018) - 2017 • 3
Machine learning molecular dynamics for the simulation of infrared spectra
Gastegger, M. and Behler, J. and Marquetand, P.
CHEMICAL SCIENCE. Volume: 8 (2017) - 2016 • 2
Erratum: “Perspective: Machine learning potentials for atomistic simulations” (The Journal of Chemical Physics (2016) 145 (170901) DOI: 10.1063/1.4966192)
Behler, J.
JOURNAL OF CHEMICAL PHYSICS. Volume: 145 (2016) - 2016 • 1
Perspective: Machine learning potentials for atomistic simulations
Behler, J.
JOURNAL OF CHEMICAL PHYSICS. Volume: 145 (2016)