| GPU accelerated computational homogenization based on a variational approach in a reduced basis framework F Fritzen, M Hodapp, M Leuschner Computer Methods in Applied Mechanics and Engineering 278, 186-217, 2014 | 91 | 2014 |
| The finite element square reduced (FE2R) method with GPU acceleration: towards three‐dimensional two‐scale simulations F Fritzen, M Hodapp International Journal for Numerical Methods in Engineering 107 (10), 853-881, 2016 | 74 | 2016 |
| The coupled atomistic/discrete-dislocation method in 3d part I: Concept and algorithms G Anciaux, T Junge, M Hodapp, J Cho, JF Molinari, WA Curtin Journal of the Mechanics and Physics of Solids 118, 152-171, 2018 | 46 | 2018 |
| In operando active learning of interatomic interaction during large-scale simulations M Hodapp, A Shapeev Machine Learning: Science and Technology 1 (4), 045005, 2020 | 31 | 2020 |
| Coupled atomistic/discrete dislocation method in 3D Part II: Validation of the method M Hodapp, G Anciaux, JF Molinari, WA Curtin Journal of the Mechanics and Physics of Solids 119, 1-19, 2018 | 31 | 2018 |
| Machine-learning potentials enable predictive and tractable high-throughput screening of random alloys M Hodapp, A Shapeev Physical Review Materials 5 (11), 113802, 2021 | 16 | 2021 |
| Lattice Green function methods for atomistic/continuum coupling: Theory and data-sparse implementation M Hodapp, G Anciaux, WA Curtin Computer Methods in Applied Mechanics and Engineering 348, 1039-1075, 2019 | 15 | 2019 |
| Efficient descriptors and active learning for grain boundary segregation C Dösinger, M Hodapp, O Peil, A Reichmann, V Razumovskiy, ... Physical Review Materials 7 (11), 113606, 2023 | 13 | 2023 |
| Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al AS Kotykhov, K Gubaev, M Hodapp, C Tantardini, AV Shapeev, ... Scientific Reports 13 (1), 19728, 2023 | 12 | 2023 |
| AI-accelerated materials informatics method for the discovery of ductile alloys I Novikov, O Kovalyova, A Shapeev, M Hodapp Journal of Materials Research 37 (21), 3491-3504, 2022 | 9 | 2022 |
| Analysis of a Sinclair-type domain decomposition solver for atomistic/continuum coupling M Hodapp Multiscale Modeling & Simulation 19 (4), 1499-1537, 2021 | 9 | 2021 |
| Ab initio framework for deciphering trade-off relationships in multi-component alloys F Moitzi, L Romaner, AV Ruban, M Hodapp, OE Peil npj computational materials 10 (1), 152, 2024 | 4 | 2024 |
| Machine learning is funny but physics makes the money: How machine-learning potentials can advance computer-aided materials design in metallurgy M Hodapp Computational Materials Science 233, 112715, 2024 | 4 | 2024 |
| Efficient flexible boundary conditions for long dislocations M Hodapp arXiv preprint arXiv:2105.08798, 2021 | 4 | 2021 |
| Equivariant tensor network potentials M Hodapp, A Shapeev Machine Learning: Science and Technology 5 (3), 035075, 2024 | 3 | 2024 |
| Equivariant tensor networks M Hodapp, A Shapeev arXiv e-prints, arXiv: 2304.08226, 2023 | 2 | 2023 |
| Efficient lattice Green's function method for bounded domain problems A Gupta, M Hodapp, WA Curtin International Journal for Numerical Methods in Engineering 123 (23), 5854-5883, 2022 | 2 | 2022 |
| On flexible Green function methods for atomistic/continuum coupling ML Hodapp EPFL, 2018 | 2 | 2018 |
| Fitting to magnetic forces improves the reliability of magnetic Moment Tensor Potentials AS Kotykhov, K Gubaev, V Sotskov, C Tantardini, M Hodapp, AV Shapeev, ... Computational Materials Science 245, 113331, 2024 | 1 | 2024 |
| Automated atomistic simulations of dissociated dislocations with ab initio accuracy L Mismetti, M Hodapp Physical Review B 109 (9), 094120, 2024 | 1 | 2024 |
Alexander ShapeevSkolkovo Institute of Science and TechnologyE-mail confirmado em shapeev.com
