Thomas A. R. Purcell

University of Arizona — United States

Materials Science · Physical Sciences

12h-index475citations42works3.22yr avg
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Research Topics

Machine Learning in Materials Science(22), Advanced Materials Characterization Techniques(7), Gold and Silver Nanoparticles Synthesis and Applications(5), Plasmonic and Surface Plasmon Research(5), Electron and X-Ray Spectroscopy Techniques(5)

Publications42 total

Materials-Consortia/optimade-python-tools: v1.4.0
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
Artificial Intelligence Guided Search for Chalcogenide Hybrid Inorganic/Organic Polymers Comonomers
Chemistry of Materials·2025
A high-performance and portable implementation of the SISSO method for CPUs and GPUs
Machine Learning Science and Technology·2025· 1 citedOpen Access
The Artificial Intelligence Guided Search for New CHIPs Comonomers
2025Open Access
Accelerating the training and improving the reliability of machine-learned interatomic potentials for strongly anharmonic materials through active learning
Physical Review Materials·2025· 4 citedOpen Access
The Artificial Intelligence Guided Search for New CHIPs Comonomers
2025Open Access
A high-performance and portable implementation of the SISSO method for CPUs and GPUs
arXiv (Cornell University)·2025Open Access
Atomate2: Modular workflows for materials science
2025· 8 citedOpen Access
Atomate2: modular workflows for materials science
Digital Discovery·2025· 19 citedOpen Access
Correction: Atomate2: modular workflows for materials science
Digital Discovery·2025Open Access
Workflows for Artificial Intelligence
2024Open Access
Prediction of the Infrared Absorbance Intensities and Frequencies of Hydrocarbons: A Message Passing Neural Network Approach
The Journal of Physical Chemistry A·2024· 5 cited
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
arXiv (Cornell University)·2024Open Access
Roadmap on data-centric materials science
Modelling and Simulation in Materials Science and Engineering·2024· 17 citedOpen Access
Roadmap on Data-Centric Materials Science
2024· 2 citedOpen Access
Roadmap on Data-Centric Materials Science
2024· 1 citedOpen Access
Roadmap on Data-Centric Materials Science
2024· 1 citedOpen Access
Roadmap on Data-Centric Materials Science
arXiv (Cornell University)·2024Open Access
Interpretable machine learning for materials design
Journal of materials research/Pratt's guide to venture capital sources·2023· 24 cited
Recent advances in the SISSO method and their implementation in the SISSO++ code
The Journal of Chemical Physics·2023· 36 citedOpen Access
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Frequent Co-authors

Matthias Scheffler(29), Christian Carbogno(18), Luca M. Ghiringhelli(14), Sebastian Eibl(7), Lucas Foppa(7), Yiyu Yao(7), Ádám Fekete(6), Markus Scheidgen(6), Florian Knoop(5), Sebastian Bauer(5), Peter Benner(5), Tristan Bereau(5), Volker Blüm(5), Mario Boley(5), C. Richard A. Catlow(5), Gerhard Dehm(5), Ralph Ernstorfer(5), Peter Fratzl(5), Christoph Freysoldt(5), Baptiste Gault(5)