13 citations · 16 across the 4 of their papers we have counts for
5 papers
Guided Diffusion for the Discovery of New Superconductors
Pawan Prakash, Jason B. Gibson, Zhongwei Li +13
The inverse design of materials with specific desired properties, such as high-temperature superconductivity, represents a formidable challenge in materials science due to the vast…
Developing a Complete AI-Accelerated Workflow for Superconductor Discovery
Jason B. Gibson, Ajinkya C. Hire, Pawan Prakash +8
The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting…
Discovery of Spin-Crossover Candidates with Equivariant Graph Neural Networks and Relevance-Based Classification
Angel Albavera-Mata, Pawan Prakash, Jason B. Gibson +7
Swift discovery of spin-crossover materials for their potential application in quantum information devices requires techniques which enable efficient identification of suitably bis…
When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential
Jason B. Gibson, Tesia D. Janicki, Ajinkya C. Hire +3
Machine-learned interatomic potentials (MLIPs) are becoming an essential tool in materials modeling. However, optimizing the generation of training data used to parameterize the ML…
Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function
Jason B. Gibson, Ajinkya C. Hire, Philip M. Dee +4
Integrating deep learning with the search for new electron-phonon superconductors represents a burgeoning field of research, where the primary challenge lies in the computational i…