226 citations · 729 across the 62 of their papers we have counts for
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Neural-networks model for force prediction in multi-principal-element alloys
Rahul Singh, Prashant Singh, Aayush Sharma +6
Atomistic simulations can provide useful insights into the physical properties of multi-principal-element alloys. However, classical potentials mostly fail to capture key quantum (…
3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys
Rahul Singh, Aayush Sharma, Onur Rauf Bingol +4
The need for advanced materials has led to the development of complex, multi-component alloys or solid-solution alloys. These materials have shown exceptional properties like stren…
Physics-aware Deep Generative Models for Creating Synthetic Microstructures
Rahul Singh, Viraj Shah, Balaji Pokuri +3
A key problem in computational material science deals with understanding the effect of material distribution (i.e., microstructure) on material performance. The challenge is to syn…