MILADY package was used in the following works.
Many thanks to the authors!
Articles
2023
A. Allera, A. M. Goryaeva, P. Lafourcade, J.-B. Maillet, M.-C. Marinica. Neighbors Map: An efficient atomic descriptor for structural analysis. Comp. Mater. Sci. 231: 112535 (2023)
A. M Goryaeva, C. Domain, A. Chartier, A. Dézaphie, T. D Swinburne, K. Ma, M. Loyer-Prost, J. Creuze, M.-C. Marinica. Compact A15 Frank-Kasper nano-phases at the origin of dislocation loops in face-centred cubic metals. Nature Comm. 14: 3003 (2023)
A. Zhong, C. Lapointe, A.M. Goryaeva, J. Baima, M. Athenes, M.-C. Marinica. Anharmonic thermo-elasticity of tungsten from accelerated Bayesian adaptive biasing force calculations with data-driven force fields. Phys. Rev. Mater. 7: 023802 (2023)
P. Grigorev, A.M. Goryaeva, M.-C. Marinica, J. R. Kermode, T. D. Swinburne. Calculation of dislocation binding to helium-vacancy defects in tungsten using hybrid ab initio-machine learning methods. Acta Mater. 247: 118734 (2023)
2022
C. Lapointe, T. D. Swinburne, L. Proville, C. S. Becquart, N. Mousseau, M.-C. Marinica. Machine learning surrogate models for strain-dependent vibrational properties and migration rates of point defects. Phys. Rev. Mater. 6: 113803 (2022)
J. Baima, A.M. Goryaeva, T. D. Swinburne, J.-B. Maillet, M. Nastar, M.-C. Marinica. Capabilities and limits of autoencoders for extracting collective variables in atomistic materials science. Phys. Chem. Chem. Phys. 24: 23152 (2022)
2021
A.M. Goryaeva, J. Dérès, C. Lapointe, P. Grigorev, T. D. Swinburne, J.R. Kermode, L. Ventelon, J. Baima, and M.-C. Marinica. Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W. Phys. Rev. Mater. 5: 103803 (2021)
M.R. Gilbert, K. Arakawa, Z. Bergstrom, M.J. Caturla, S.L. Dudarev, F. Gao, A.M. Goryaeva, S.Y. Hu, Xunxiang Hu, R.J Kurtz, A. Litnovsky, J. Marian, M.-C. Marinica, et al. Perspectives on multiscale modelling and experiments to accelerate materials development for fusion. J. Nucl. Mater. 554: 153113 (2021)
P. Grigorev, A.M. Goryaeva, M.-C. Marinica, J.R. Kermode, T.D. Swinburne. Synergistic coupling in ab initio-machine learning simulations of dislocations arXiv preprint arXiv:2111.11262
2020
A.M. Goryaeva, C. Lapointe, C. Dai, J. Dérès, J.-B. Maillet, M.-C. Marinica. Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores. Nature Comm. 11: 4691 (2020)
C. Lapointe, T.D. Swinburne, L. Thiry, S. Mallat, L. Proville, C.S. Becquart, M.-C. Marinica. Machine learning surrogate models for prediction of point defect vibrational entropy Phys. Rev. Mater. 4: 063802 (2020)
F. Bruneval, I. Maliyov, C. Lapointe, M.-C. Marinica. Extrapolating unconverged GW energies up to the complete basis set limit with linear regression. J. Chem. Theory Comput. 16: 4399-4407 (2020)
L. Messina, A. Quaglino, A.M. Goryaeva, M.-C. Marinica, C. Domain, N. Castin, G. Bonny, R. Krause. A DFT-driven multifidelity framework for construc-ting efficient energy models for atomic-scale simulations. NIMB B. 483: 15-21 (2020)
2019
A.M. Goryaeva, J.-B. Maillet, M.-C. Marinica. Towards better efficiency of interatomic linear machine learning potentials. Comp. Mater. Sci. 166: 200 (2019)