| A machine learning approach to model solute grain boundary segregation L Huber, R Hadian, B Grabowski, J Neugebauer npj Computational Materials 4 (1), 64, 2018 | 105 | 2018 |
| Atomistic simulations of the interaction of alloying elements with grain boundaries in Mg L Huber, J Rottler, M Militzer Acta materialia 80, 194-204, 2014 | 65 | 2014 |
| Ab initio calculations of rare-earth diffusion in magnesium L Huber, I Elfimov, J Rottler, M Militzer Physical Review B—Condensed Matter and Materials Physics 85 (14), 144301, 2012 | 64 | 2012 |
| Ab initio modelling of solute segregation energies to a general grain boundary L Huber, B Grabowski, M Militzer, J Neugebauer, J Rottler Acta Materialia 132, 138-148, 2017 | 53 | 2017 |
| Basal slip in Laves phases: the synchroshear dislocation J Guénolé, FZ Mouhib, L Huber, B Grabowski, S Korte-Kerzel Scripta Materialia 166, 134-138, 2019 | 47 | 2019 |
| Interplay of chemistry and faceting at grain boundaries in a model Al alloy H Zhao, L Huber, W Lu, NJ Peter, D An, F De Geuser, G Dehm, D Ponge, ... Physical Review Letters 124 (10), 106102, 2020 | 44 | 2020 |
| Defect phases–thermodynamics and impact on material properties S Korte-Kerzel, T Hickel, L Huber, D Raabe, S Sandlöbes-Haut, ... International Materials Reviews 67 (1), 89-117, 2022 | 37 | 2022 |
| A QM/MM approach for low-symmetry defects in metals L Huber, B Grabowski, M Militzer, J Neugebauer, J Rottler Computational Materials Science 118, 259-268, 2016 | 18 | 2016 |
| Systematic atomic structure datasets for machine learning potentials: Application to defects in magnesium M Poul, L Huber, E Bitzek, J Neugebauer Physical Review B 107 (10), 104103, 2023 | 14 | 2023 |
| Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography Y Li, Y Wei, Z Wang, X Liu, T Colnaghi, L Han, Z Rao, X Zhou, L Huber, ... Nature Communications 14 (1), 7410, 2023 | 11 | 2023 |
| Reactions in viscous media: potential and free energy surfaces in solvent–solute coordinates L Huber, E Edwards, MV Basilevsky, N Weinberg Molecular Physics 107 (21), 2283-2291, 2009 | 5 | 2009 |
| A machine learning approach to model solute grain boundary segregation, Npj Comput. Mater. 4 (2018) 64 L Huber, R Hadian, B Grabowski, J Neugebauer | 5 | |
| Insights from symmetry: Improving machine-learned models for grain boundary segregation Y Borges, L Huber, H Zapolsky, R Patte, G Demange Computational Materials Science 232, 112663, 2024 | 3 | 2024 |
| Evolutionary algorithms for cardinality-constrained Ising models VD Bhuva, DC Dang, L Huber, D Sudholt International Conference on Parallel Problem Solving from Nature, 456-469, 2022 | 2 | 2022 |
| Approximating the impact of nuclear quantum effects on thermodynamic properties of crystalline solids by temperature remapping R Dsouza, L Huber, B Grabowski, J Neugebauer Physical Review B 105 (18), 184111, 2022 | 2 | 2022 |
| Segregation to interfaces in TiAl alloys: A multiscale QM/MM study D Gehringer, L Huber, J Neugebauer, D Holec Physical Review Materials 7 (6), 063604, 2023 | 1 | 2023 |
| Systematic Structure Datasets for Machine Learning Potentials: Application to Moment Tensor Potentials of Magnesium and its Defects M Poul, L Huber, E Bitzek, J Neugebauer Condensed Matter: Materials Science, 2022 | 1 | 2022 |
| A Newtonian algorithm for constant pressure molecular dynamics with periodic boundary conditions N Weinberg, E Edwards, L Huber, Z Sentell, J Spooner Molecular Physics 120 (10), e2060145, 2022 | 1 | 2022 |
| Sampling-free computation of finite temperature material properties in isochoric and isobaric ensembles using the mean-field anharmonic bond model R Dsouza, M Poul, L Huber, TD Swinburne, J Neugebauer Physical Review B 109 (6), 064108, 2024 | | 2024 |
| Tailoring α2/γ and γ/γ interfaces in TiAl alloys via segregation: A multi-method study D Gehringer, D Holec, L Huber, J Neugebauer | | 2023 |
