3 citations · 5 across the 4 of their papers we have counts for
7 papers
Reinforcement Learning on the Discrete Composition Channel of a Crystal Generator: Validated Gains and Reward Hacking
Pawan Prakash, Philipp Höllmer, Addis Fuhr +4
Inverse materials design is a long-standing goal of computational materials discovery. Generative models for crystalline materials are typically trained to match the distribution o…
Benchmarking of Fast and Interpretable UF Machine Learning Potentials
Pawan Prakash, Sam Dong, Richard G. Hennig
Machine learning interatomic potentials (MLIPs) have emerged as a powerful alternative to density functional theory (DFT) for molecular dynamics simulations, offering near-DFT accu…
MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching
Jirui Jin, Cheng Zeng, Pawan Prakash +5
Key objectives in conditional molecular generation include ensuring chemical validity, aligning generated molecules with target properties, promoting structural diversity, and enab…
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…
Open Materials Generation with Stochastic Interpolants
Philipp Hoellmer, Thomas Egg, Maya M. Martirossyan +11
The discovery of new materials is essential for enabling technological advancements. Computational approaches for predicting novel materials must effectively learn the manifold of…