Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning

1 code implementation • 1 Nov 2023 • Logan Ward, Ben Blaiszik, Cheng-Wei Lee, Troy Martin, Ian Foster, André Schleife

Knowing the rate at which particle radiation releases energy in a material, the stopping power, is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies.

Polar magneto-optical Kerr effect in antiferromagnetic M$_2$As (M=Cr, Mn, Fe) under an external magnetic field

no code implementations • 3 Dec 2020 • Kisung Kang, Kexin Yang, Krithik Puthalath, David G. Cahill, André Schleife

Hence, in this work we combine first-principles simulations with measurements of the polar magneto-optical Kerr effect under external magnetic fields, to study magneto-optical response of antiferromagnetic M$_2$As (M=Cr, Mn, and Fe).

Materials Science

Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-picometer Precision

1 code implementation • 22 Jan 2020 • Chia-Hao Lee, Abid Khan, Di Luo, Tatiane P. Santos, Chuqiao Shi, Blanka E. Janicek, Sangmin Kang, Wenjuan Zhu, Nahil A. Sobh, André Schleife, Bryan K. Clark, Pinshane Y. Huang

2D materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so-far limited electron microscopy methods to probe these atomic-scale strain fields.

Materials Science Mesoscale and Nanoscale Physics