1 citations · 1 across the 4 of their papers we have counts for
4 papers
Optimal design of experiments in the context of machine-learning inter-atomic potentials: improving the efficiency and transferability of kernel based methods
Bartosz Barzdajn, Christopher P. Race
Data-driven, machine learning (ML) models of atomistic interactions are often based on flexible and non-physical functions that can relate nuanced aspects of atomic arrangements in…
Molecular dynamics simulations of neutron induced collision cascades in Zr - statistical modelling of irradiation damage and potential applications
Bartosz Barzdajn, Christopher P Race
Understanding the nature of irradiation damage often requires a multi-scale and multi-physics approach, i.e. it requires a significant amount of information from experiments, simul…
Exploring descriptors for titanium microstructure via digital fingerprints from variational autoencoders
Michael D. White, Gowtham Nimmal Haribabu, Jeyapriya Thimukonda Jegadeesan +3
Microstructure is key to controlling and understanding the properties of metallic materials, but traditional approaches to describing microstructure capture only a small number of…
Interaction of monoclinic ZrO grain boundaries with oxygen vacancies, Sn and Nb -- implications for the corrosion of Zr alloy fuel cladding
Maria S. Yankova, Christopher P. Race
We used density-functional-theory simulations to examine the structural and electronic properties of the grain boundary in monoclinic ZrO, which is a very lo…