9 citations · 10 across the 6 of their papers we have counts for
6 papers
AMGenC: Generating Charge Balanced Amorphous Materials
Yan Lin, Jilin Hu, N. M. Anoop Krishnan +1
Amorphous (disordered) materials are solids that have shown great potential in various domains, including energy storage, thermal management, and advanced materials. Unlike crystal…
LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models
Siddharth Betala, Samuel P. Gleason, Ali Ramlaoui +12
Generative machine learning (ML) models hold great promise for accelerating materials discovery through the inverse design of inorganic crystals, enabling an unprecedented explorat…
Optimization of Transferable Interatomic Potentials for Glasses toward Experimental Properties
Ruoxia Chen, Kai Yang, Morten M. Smedskjaer +3
The accuracy of molecular simulations is fundamentally limited by the interatomic potentials that govern atomic interactions. Traditional potential development, which relies heavil…
Force field optimization by end-to-end differentiable atomistic simulation
Abhijeet S. Gangan, Samuel S. Schoenholz, Ekin Dogus Cubuk +2
The accuracy of atomistic simulations depends on the precision of force fields. Traditional numerical methods often struggle to optimize the empirical force field parameters for re…
Accurate Description of Ion Migration in Solid-State Ion Conductors from Machine-Learning Molecular Dynamics
Takeru Miyagawa, Namita Krishnan, Manuel Grumet +3
Solid-state ion conductors (SSICs) have emerged as a promising material class for electrochemical storage devices and novel compounds of this kind are continuously being discovered…
On the Allowable or Forbidden Nature of Vapor-Deposited Glasses
Zhe Wang, Tao Du, N. M. Anoop Krishnan +2
Vapor deposition can yield glasses that are more stable than those obtained by the traditional melt-quenching route. However, it remains unclear whether vapor-deposited glasses are…