Matbench Discovery - An evaluation framework for machine learning crystal stability prediction. - Janosh Riebesell; Rhys E. A. Goodall; Anubhav Jain 001; Philipp Benner; Kristin A. Persson; Alpha A. Lee (CoRR, 2023)

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. - Bowen Deng; Peichen Zhong; KyuJung Jun; Janosh Riebesell; Kevin Han; Christopher J. Bartel; Gerbrand Ceder (Nat. Mac. Intell., 2023)

Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials. - Janosh Riebesell; T. Wesley Surta; Rhys E. A. Goodall; Michael Gaultois; Alpha A. Lee (CoRR, 2024)

LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation. - Yuan Chiang; Chia-Hong Chou; Janosh Riebesell (CoRR, 2024)

Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning. - Bowen Deng; Yunyeong Choi; Peichen Zhong; Janosh Riebesell; Shashwat Anand; Zhuohan Li; KyuJung Jun; Kristin A. Persson; Gerbrand Ceder (CoRR, 2024)

Jobflow: Computational Workflows Made Simple. - Andrew S. Rosen; Max C. Gallant; Janine George; Janosh Riebesell; Hrushikesh Sahasrabuddhe; Jimmy-Xuan Shen; Mingjian Wen; Matthew L. Evans; Guido Petretto; David Waroquiers; Gian-Marco Rignanese; Kristin A. Persson; et al. (J. Open Source Softw., 2024)