David Zagaceta
University of Nevada, Las Vegas(US)
Research Areas
Machine Learning in Materials Science, X-ray Diffraction in Crystallography, Advanced Chemical Physics Studies, Force Microscopy Techniques and Applications, Electron and X-Ray Spectroscopy Techniques
Most-Cited Works
- → High dielectric ternary oxides from crystal structure prediction and high-throughput screening(2020)56 cited
- → Spectral neural network potentials for binary alloys(2020)10 cited
- → PyXtal_FF: a python library for automated force field generation(2020)2 cited
- On Transferability of Machine Learning Force Fields: A Case Study on Silicon(2020)
- → qzhu2017/PyXtal_FF: Linked to Zenodo(2020)
- → Publisher’s Note: “Spectral neural network potentials for binary alloys” [J. Appl. Phys 128, 045113 (2020)](2020)