83 citations · 88 across the 5 of their papers we have counts for
3 papers · 1 filter
MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures
Elena Zamaraeva, Christopher M. Collins, George R. Darling +8
Geometry optimization of atomic structures is a common and crucial task in computational chemistry and materials design. Following the learning to optimize paradigm, we propose a n…
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hyb…
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics
Michael Moran, Vladimir V. Gusev, Michael W. Gaultois +2
The scarcity of property labels remains a key challenge in materials informatics, whereas materials data without property labels are abundant in comparison. By pretraining supervis…