5 papers
GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
Parth Verma, Parv P. Singh, Vipul Garg +3
Graph Neural Networks (GNNs) have revolutionized Neural Force Fields for atomistic simulations, achieving near-quantum accuracy at reduced cost, yet adapting these models to new ch…
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…
Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters
Kai Yang, Ruoxia Chen, Anders K. R. Christensen +4
The simulation of borosilicate glasses is challenging due to the composition and temperature dependent coordination state of boron atoms. Here, we present a newly developed machine…
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…