K. J. Schmidt

According to our database, K. J. Schmidt authored at least 10 papers between 1981 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

A practical guide to machine learning interatomic potentials - Status and future.

Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng, Chen Shen, Zhenghao Wu, Clare Yijia Xie, Julia H. Yang, Nongnuch Artrith, Ben Blaiszik, Gerbrand Ceder, Kamal Choudhary, Gábor Csányi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant G. Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, K. J. Schmidt, So Takamoto, Aidan Thompson, Julia Westermayr, Brandon M. Wood

CoRR, March, 2025

2024

Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science.

K. J. Schmidt, Aristana Scourtas, Logan T. Ward, Steve Wangen, Marcus Schwarting, Isaac Darling, Ethan Truelove, Aadit Ambadkar, Ribhav Bose, Zoa Katok, Jingrui Wei, Xiangguo Li, Ryan Jacobs, Lane Schultz, Doyeon Kim, Michael C. Ferris, Paul M. Voyles, Dane Morgan, Ian T. Foster, Ben Blaiszik

J. Open Source Softw., January, 2024

Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science.

K. J. Schmidt, Aristana Scourtas, Logan T. Ward, Steve Wangen, Marcus Schwarting, Isaac Darling, Ethan Truelove, Aadit Ambadkar, Ribhav Bose, Zoa Katok, Jingrui Wei, Xiangguo Li, Ryan Jacobs, Lane Schultz, Doyeon Kim, Michael Ferris, Paul M. Voyles, Dane Morgan, Ian T. Foster, Ben Blaiszik

Dataset, January, 2024

Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science.

K. J. Schmidt, Aristana Scourtas, Logan T. Ward, Steve Wangen, Marcus Schwarting, Isaac Darling, Ethan Truelove, Aadit Ambadkar, Ribhav Bose, Zoa Katok, Jingrui Wei, Xiangguo Li, Ryan Jacobs, Lane Schultz, Doyeon Kim, Michael Ferris, Paul M. Voyles, Dane Morgan, Ian T. Foster, Ben Blaiszik

Dataset, January, 2024

Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science.

K. J. Schmidt, Aristana Scourtas, Logan T. Ward, Steve Wangen, Marcus Schwarting, Isaac Darling, Ethan Truelove, Aadit Ambadkar, Ribhav Bose, Zoa Katok, Jingrui Wei, Xiangguo Li, Ryan Jacobs, Lane Schultz, Doyeon Kim, Michael Ferris, Paul M. Voyles, Dane Morgan, Ian T. Foster, Ben Blaiszik

Dataset, January, 2024

Foundry-ML (latest).

K. J. Schmidt, Aristana Scourtas, Logan T. Ward, Steve Wangen, Marcus Schwarting, Isaac Darling, Ethan Truelove, Aadit Ambadkar, Ribhav Bose, Zoa Katok, Jingrui Wei, Xiangguo Li, Ryan Jacobs, Lane Schultz, Doyeon Kim, Michael Ferris, Paul M. Voyles, Dane Morgan, Ian T. Foster, Ben Blaiszik

Dataset, January, 2024

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility.

Ryan Jacobs, Lane E. Schultz, Aristana Scourtas, K. J. Schmidt, Owen Price Skelly, Will Engler, Ian T. Foster, Ben Blaiszik, Paul M. Voyles, Dane Morgan

Mach. Learn. Sci. Technol., 2024

2023

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon.

Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, Shruti Badhwar, Joshua D. Bocarsly, Andres M. Bran, Stefan Bringuier, L. Catherine Brinson, Kamal Choudhary, Defne Circi, Sam Cox, Wibe A. de Jong, Matthew L. Evans, Nicolas Gastellu, Jerome Genzling, María Victoria Gil, Ankur K. Gupta, Zhi Hong, Alishba Imran, Sabine Kruschwitz, Anne Labarre, Jakub Lála, Tao Liu, Steven Ma, Sauradeep Majumdar, Garrett W. Merz, Nicolas Moitessier, Elias Moubarak, Beatriz Mouriño, Brenden Pelkie, Michael Pieler, Mayk Caldas Ramos, Bojana Rankovic, Samuel G. Rodriques, Jacob N. Sanders, Philippe Schwaller, Marcus Schwarting, Jiale Shi, Berend Smit, Ben E. Smith, Joren Van Heck, Christoph Völker, Logan T. Ward, Sean Warren, Benjamin Weiser, Sylvester Zhang, Xiaoqi Zhang, Ghezal Ahmad Zia, Aristana Scourtas, K. J. Schmidt, Ian T. Foster, Andrew D. White, Ben Blaiszik

CoRR, 2023

2022

FAIR principles for AI models, with a practical application for accelerated high energy diffraction microscopy.

Nikil Ravi, Pranshu Chaturvedi, Eliu A. Huerta, Zhengchun Liu, Ryan Chard, Aristana Scourtas, K. J. Schmidt, Kyle Chard, Ben Blaiszik, Ian T. Foster

CoRR, 2022

1981

Effizienz und Effizienzkriterien von Arbeitsplatzrechnern bei kommerziellem Einsatz.

K. J. Schmidt, M. R. Wolff

Proceedings of the Personal Computing, 1981