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The last 5 uploaded publications
Accelerated data-driven materials science with the Materials Project
Matthew K. Horton, Patrick Huck, Ruoxi Yang, Jason M. Munro, Shyam Dwaraknath, Alex M. Ganose, Ryan Kingsbury, Mingjian Wen, Jianxin Shen, Tyler S. Mathis, Aaron D. Kaplan, Karlo Berket, Janosh Riebesell, Janine George, Andrew Rosen, Evan Walter Clark Spotte‐Smith, Matthew J. McDermott, Orion A. Cohen, Alexander Dunn, Matthew C. Kuner, Gian‐Marco Rignanese, Guido Petretto, David Waroquiers, Sinéad M. Griffin, Jeffrey B. Neaton, D. C. Chrzan, Mark Asta, Geoffroy Hautier, Shreyas Cholia, Gerbrand Ceder, Shyue Ping Ong, Anubhav Jain, Kristin A. Persson (2025). Accelerated data-driven materials science with the Materials Project. , 24(10), DOI: https://doi.org/10.1038/s41563-025-02272-0.
Article139 days agoHigh-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration
Andrew Rosen, Victor Fung, Patrick Huck, Cody T. O’Donnell, Matthew K. Horton, Donald G Truhlar, Kristin A. Persson, Justin M. Notestein, Randall Q. Snurr (2022). High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration. , 8(1), DOI: https://doi.org/10.1038/s41524-022-00796-6.
Article66 days agoHigh-Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration
Andrew Rosen, Victor Fung, Patrick Huck, Cody T. O’Donnell, Matthew K. Horton, Donald G Truhlar, Kristin A. Persson, Justin M. Notestein, Randall Q. Snurr (2021). High-Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration. , DOI: https://doi.org/10.26434/chemrxiv-2021-6cs91.
Preprint66 days ago