Effizienz und Effizienzkriterien von Arbeitsplatzrechnern bei kommerziellem Einsatz. - K. J. Schmidt; M. R. Wolff (Personal Computing, 1981)

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)

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; et al. (CoRR, 2023)

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; et al. (Zenodo, 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; et al. (Zenodo, 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; et al. (Zenodo, 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; et al. (Zenodo, 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; et al. (J. Open Source Softw., 2024)

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; et al. (CoRR, 2025)