Machine learning for metallurgy V: A neural-network potential for zirconium

M Liyanage, D Reith, V Eyert, WA Curtin - Physical Review Materials, 2022 - APS
The mechanical performance—including deformation, fracture and radiation damage—of
zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the …

Development of machine learning and empirical interatomic potentials for the binary Zr-Sn system

H Mei, L Chen, F Wang, G Liu, J Hu, W Lin… - Journal of Nuclear …, 2024 - Elsevier
Zirconium alloys are pivotal structural materials in nuclear reactors. Enhancing their
properties and performance necessitates a profound understanding of the interactions …

Machine learning for metallurgy II. A neural-network potential for magnesium

M Stricker, B Yin, E Mak, WA Curtin - Physical Review Materials, 2020 - APS
Interatomic potentials are essential for studying fundamental mechanisms of deformation
and failure in metals and alloys because the relevant defects (dislocations, cracks, etc.) are …

Machine learning for metallurgy I. A neural-network potential for Al-Cu

D Marchand, A Jain, A Glensk, WA Curtin - Physical review materials, 2020 - APS
High-strength metal alloys achieve their performance via careful control of precipitates and
solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical …

Machine learning for metallurgy IV: A neural network potential for Al-Cu-Mg and Al-Cu-Mg-Zn

D Marchand, WA Curtin - Physical Review Materials, 2022 - APS
Most metallurgical properties, eg, dislocation propagation, precipitate formation, can only be
fully understood atomistically but most phenomena and quantities of interest cannot be …

Machine learning models for predictive materials science from fundamental physics: An application to titanium and zirconium

MS Nitol, DE Dickel, CD Barrett - Acta Materialia, 2022 - Elsevier
Here we present new neural network potentials capable of accurately modeling the
transformations between the α, β, and ω phases of titanium (Ti) and zirconium (Zr), including …

Pushing the limits of atomistic simulations towards ultra-high temperature: a machine-learning force field for ZrB2

Y Zhang, A Lunghi, S Sanvito - Acta Materialia, 2020 - Elsevier
Determining thermal and physical quantities across a broad temperature domain, especially
up to the ultra-high temperature region, is a formidable theoretical and experimental …

[HTML][HTML] How can machine learning be used for accurate representations and predictions of fracture nucleation in zirconium alloys with hydride populations?

T Hasan, L Capolungo, MA Zikry - APL Materials, 2023 - pubs.aip.org
Zirconium alloys are critical material components of systems subjected to harsh
environments such as high temperatures, irradiation, and corrosion. When exposed to water …

[HTML][HTML] Materials prediction via classification learning

PV Balachandran, J Theiler, JM Rondinelli… - Scientific reports, 2015 - nature.com
In the paradigm of materials informatics for accelerated materials discovery, the choice of
feature set (ie attributes that capture aspects of structure, chemistry and/or bonding) is …

Machine learning for metallurgy III: A neural network potential for Al-Mg-Si

ACP Jain, D Marchand, A Glensk, M Ceriotti… - Physical Review …, 2021 - APS
High-strength metal alloys achieve their performance via careful control of the nucleation,
growth, and kinetics of precipitation. Alloy mechanical properties are then controlled by …