3 papers
cond-mat.mtrl-sci2026
Worse Forces Can Lead to Better Structures: Trajectory-Level Fine-Tuning of Machine Learning Interatomic Potentials
Evan Dramko, Yizhi Zhu, Aleksandar Krivokapic +4
Machine learning interatomic potentials (MLIPs) are commonly trained and evaluated by their ability to reproduce first-principles energies and forces. Practical value, however, is…
cs.LG2025
ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
Evan Dramko, Yihuang Xiong, Yizhi Zhu +4
Point defects play a central role in driving the properties of materials. First-principles methods are widely used to compute defect energetics and structures, including at scale f…
cond-mat.mtrl-sci2025
Identifying high performance spectrally-stable quantum defects in diamond
Yihuang Xiong, Yizhi Zhu, Shay McBride +2
Point defects in semiconductors are becoming central to quantum technologies. They can be used as spin qubits interfacing with photons, which are fundamental for building quantum n…