15 citations · 38 across the 10 of their papers we have counts for
3 papers · 1 filter
Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
Sanggyu Chong, Tong Jiang, Michelangelo Domina +4
In many cases, the predictions of machine learning interatomic potentials (MLIPs) can be interpreted as a sum of body-ordered contributions, which is explicit when the model is dir…
Machine learning model for efficient nonthermal tuning of the charge density wave in monolayer NbSe
Luka Benić, Federico Grasselli, Chiheb Ben Mahmoud +2
Understanding and controlling the charge density wave (CDW) phase diagram of transition metal dichalcogenides is a long-studied problem in condensed matter physics. However, due to…
Uncertainty in the era of machine learning for atomistic modeling
Federico Grasselli, Sanggyu Chong, Venkat Kapil +2
The widespread adoption of machine learning surrogate models has significantly improved the scale and complexity of systems and processes that can be explored accurately and effici…