3.6k citations · 10.6k across the 54 of their papers we have counts for
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System of Agentic AI for the Discovery of Metal-Organic Frameworks
Theo Jaffrelot Inizan, Sherry Yang, Aaron Kaplan +12
Generative models and machine learning promise accelerated material discovery in MOFs for CO2 capture and water harvesting but face significant challenges navigating vast chemical…
Computational search for materials having a giant anomalous Hall effect in the pyrochlore and spinel crystal structures
Sean Sullivan, Seungjun Lee, Nathan J. Szymanski +4
Ferromagnetic pyrochlore and spinel materials with topological flat bands are of interest for their potential to exhibit a giant anomalous Hall effect (AHE). In this work, we prese…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Force field optimization by end-to-end differentiable atomistic simulation
Abhijeet S. Gangan, Samuel S. Schoenholz, Ekin Dogus Cubuk +2
The accuracy of atomistic simulations depends on the precision of force fields. Traditional numerical methods often struggle to optimize the empirical force field parameters for re…
Generative Hierarchical Materials Search
Sherry Yang, Simon Batzner, Ruiqi Gao +7
Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials…
Accurate Prediction of Experimental Band Gaps from Large Language Model-Based Data Extraction
Samuel J. Yang, Shutong Li, Subhashini Venugopalan +5
Machine learning is transforming materials discovery by providing rapid predictions of material properties, which enables large-scale screening for target materials. However, such…