activity
20222025
most citedMachine Learning Interatomic Potential for Simulations of Carbon at Extreme Conditions

66 citations · 66 across the 5 of their papers we have counts for

collaborators

5 papers

physics.app-ph2025

X-ray thermal diffuse scattering as a texture-robust temperature diagnostic for dynamically compressed solids

P. G. Heighway, D. J. Peake, T. Stevens +119

We present a model of x-ray thermal diffuse scattering (TDS) from a cubic polycrystal with an arbitrary crystallographic texture, based on the classic approach of Warren. We compar…

cond-mat.mtrl-sci2025

Femtosecond temperature measurements of laser-shocked copper deduced from the intensity of the x-ray thermal diffuse scattering

J. S. Wark, D. J. Peake, T. Stevens +122

We present 50-fs, single-shot measurements of the x-ray thermal diffuse scattering (TDS) from copper foils that have been shocked via nanosecond laser-ablation up to pressures abov…

cond-mat.mtrl-sci2024

Extreme Metastability of Diamond and its Transformation to BC8 Post-Diamond Phase of Carbon

Kien Nguyen-Cong, Jonathan T. Willman, Joseph M. Gonzalez +9

Diamond possesses exceptional physical properties due to its remarkably strong carbon-carbon bonding, leading to significant resilience to structural transformations at very high p…

cond-mat.mtrl-sci2022

Metastable Carbon at Extreme Conditions

Ashley S. Williams, Kien Nguyen-Cong, Jonathan T. Willman +1

Carbon at extreme conditions is the focus of intensive scientific inquiry due to its importance for applications in inertial confinement fusion experiments and for understanding th…

cond-mat.mtrl-sci2022★ 66 cited

Machine Learning Interatomic Potential for Simulations of Carbon at Extreme Conditions

Jonathan T. Willman, Kien Nguyen-Cong, Ashley S. Williams +5

A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential (MLIP) has been developed for simulations of carbon at extreme pressures (up to 5 TPa) and temperatures (…