Accurate quantification of dislocation loops in complex functional alloys enabled by deep learning image analysis

Autor: Thomas Bilyk, Alexandra. M. Goryaeva, Mihai-Cosmin Marinica, Camille Flament, Catherine Sabathier, Eric Leroy, Marie Loyer-Prost, Estelle Meslin

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)

Abstract In-depth statistics of individual defects observed during transmission electron microscopy (TEM) experiments are essential for the thorough characterization of materials. In this study, we aim to quantitatively characterize the population of

Externí odkaz: https://doaj.org/article/09d54c984cc246a58903676b7aac31c5

Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics

Autor: Svetoslav Nikolov, Mitchell A. Wood, Attila Cangi, Jean-Bernard Maillet, Mihai-Cosmin Marinica, Aidan P. Thompson, Michael P. Desjarlais, Julien Tranchida

Publikováno v: npj Computational Materials, Vol 7, Iss 1, Pp 1-12 (2021)

Abstract A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a collective atomi

Externí odkaz: https://doaj.org/article/d348af2bd3da41768c9cb3ef7706b759

Capabilities and limits of autoencoders for extracting collective variables in atomistic materials science

Autor: Jacopo Baima, Alexandra M. Goryaeva, Thomas D. Swinburne, Jean-Bernard Maillet, Maylise Nastar, Mihai-Cosmin Marinica

Publikováno v: Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics, 2022, https://doi.org/10.1039/D2CP01917E. ⟨10.1039/D2CP01917E⟩

International audience; Free energy calculations in materials science are routinely hindered by the need to provide re-action coordinates that can meaningfully partition atomic configuration space, a prerequisite formost enhanced sampling approaches.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e53198c64a7473d9f131dbdc607e1b3b
https://doi.org/10.1039/d2cp01917e

Calculation of dislocation binding to helium-vacancy defects in tungsten using hybrid ab initio-machine learning methods

Autor: Petr Grigorev, Alexandra M. Goryaeva, Mihai-Cosmin Marinica, James R. Kermode, Thomas D. Swinburne

Publikováno v: Acta Materialia
Acta Materialia, 2023, 247, pp.118734. ⟨10.1016/j.actamat.2023.118734⟩

International audience; Calculations of dislocation-defect interactions are essential to model metallic strength, but the required system sizes are at or beyond ab initio limits. Current estimates thus have extrapolation or finite size errors that ar

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1594f3d51993a8f6d9f4d556d6d3c5f3
https://hal.science/hal-03993850/document

Machine learning surrogate models for strain-dependent vibrational properties and migration rates of point defects

Autor: Clovis Lapointe, Thomas D. Swinburne, Laurent Proville, Charlotte S. Becquart, Normand Mousseau, Mihai-Cosmin Marinica

Publikováno v: Physical Review Materials
Physical Review Materials, 2022, Physical Review Materials, 6 (11), pp.113803. ⟨10.1103/physrevmaterials.6.113803⟩

International audience; Machine learning surrogate models employing atomic environment descriptors have found wide applicability in materials science. In our previous work, this approach yielded accurate and transferable predictions of the vibrationa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::347caf7fb2bc059876b39400cca5cee4
https://hal.univ-lille.fr/hal-03879650