Search Results for author: James M. Rondinelli

Found 5 papers, 3 papers with code

MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling

1 code implementation27 Dec 2023 Hengrui Zhang, Jie Chen, James M. Rondinelli, Wei Chen

This complexity is particularly evident in molecular mixtures, a frequently explored space for materials such as battery electrolytes.

mixture property prediction molecular representation

ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data

1 code implementation15 Nov 2022 Hengrui Zhang, Wei Wayne Chen, James M. Rondinelli, Wei Chen

To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems.

Active Learning Materials Screening

Non-Abelian Stokes theorem and quantized Berry flux

no code implementations11 Feb 2021 Alexander C. Tyner, Shouvik Sur, Qunfei Zhou, Danilo Puggioni, Pierre Darancet, James M. Rondinelli, Pallab Goswami

Band topology of anomalous quantum Hall insulators can be precisely addressed by computing Chern numbers of constituent non-degenerate bands that describe quantized, Abelian Berry flux through two-dimensional Brillouin zone.

Materials Science Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics Strongly Correlated Electrons High Energy Physics - Theory

Database, Features, and Machine Learning Model to Identify Thermally Driven Metal-Insulator Transition Compounds

2 code implementations26 Oct 2020 Alexandru B. Georgescu, Peiwen Ren, Aubrey R. Toland, Elsa A. Olivetti, Nicholas Wagner, James M. Rondinelli

Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics.

Materials Screening Materials Science Strongly Correlated Electrons

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