activity
20072026
most citedRecent Advances and Applications of Deep Learning Methods in Materials Science

1.2k citations · 3.6k across the 37 of their papers we have counts for

collaborators

37 papers

cond-mat.mtrl-sci2026

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning

Wonseok Jeong, Francesca Tavazza, Brian DeCost

Atomic-scale modeling has advanced rapidly through integration of machine learning, yet a key bottleneck remains. Even with an accurate potential energy surface and a clear target…

cond-mat.mtrl-sci2024★ 6 cited

Quantum Monte Carlo and density functional theory study of strain and magnetism in 2D 1T-VSe with charge density wave states

Daniel Wines, Akram Ibrahim, Nishwanth Gudibandla +18

Two-dimensional (2D) 1T-VSe has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) stat…

cond-mat.mtrl-sci2023

Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison

Francesca Tavazza, Kamal Choudhary, Brian DeCost

The development of large databases of material properties, together with the availability of powerful computers, has allowed machine learning (ML) modeling to become a widely used…

cond-mat.mtrl-sci2023★ 59 cited

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

Kamal Choudhary, Daniel Wines, Kangming Li +35

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…

cond-mat.mtrl-sci2023★ 39 cited

Recent progress in the JARVIS infrastructure for next-generation data-driven materials design

Daniel Wines, Ramya Gurunathan, Kevin F. Garrity +4

The Joint Automated Repository for Various Integrated Simulations (JARVIS) infrastructure at the National Institute of Standards and Technology (NIST) is a large-scale collection o…

cond-mat.supr-con2022★ 68 cited

High-Throughput DFT-Based Discovery of Next Generation Two-Dimensional (2D) Superconductors

Daniel Wines, Kamal Choudhary, Adam J. Biacchi +2

High-throughput density functional theory (DFT) calculations allow for a systematic search for conventional superconductors. With the recent interest in two-dimensional (2D) superc…