Publications of Liam Huber

Li, Y.; Wei, Y.; Wang, Z.; Liu, X.; Colnaghi, T.; Han, L.; Rao, Z.; Zhou, X.; Huber, L.; Dsouza, R. et al.; Gong, Y.; Neugebauer, J.; Marek, A.; Rampp, M.; Bauer, S.; Li, H.; Baker, I.; Stephenson, L.; Gault, B.: Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography. Nature Communications 14 (1), 7410 (2023)

Poul, M.; Huber, L.; Bitzek, E.; Neugebauer, J.: Systematic atomic structure datasets for machine learning potentials: Application to defects in magnesium. Physical Review B 107, 104103 (2023)

Poul, M.; Huber, L.; Bitzek, E.; Neugebauer, J.: Systematic Structure Datasets for Machine Learning Potentials: Application to Moment Tensor Potentials of Magnesium and its Defects. Condensed Matter: Materials Science (2022)

Dsouza, R.; Huber, L.; Grabowski, B.; Neugebauer, J.: Approximating the impact of nuclear quantum effects on thermodynamic properties of crystalline solids by temperature remapping. Physical Review B 105 (18), 184111 (2022)

Zhao, H.; Huber, L.; Lu, W.; Peter, N. J.; An, D.; De Geuser, F.; Dehm, G.; Ponge, D.; Neugebauer, J.; Gault, B. et al.; Raabe, D.: Interplay of Chemistry and Faceting at Grain Boundaries in a Model Al Alloy. Physical Review Letters 124 (10), 106102 (2020)

Huber, L.; Hadian, R.; Grabowski, B.; Neugebauer, J.: A machine learning approach to model solute grain boundary segregation. npj Computational Materials 4 (1), 64 (2018)

Huber, L.; Grabowski, B.; Militzer, M.; Neugebauer, J.; Rottler, J.: A QM/MM approach for low-symmetry defects in metals. Computational Materials Science 118, pp. 259 - 268 (2016)