Ultra-Fast Evaluation of Protein Energies Directly from Sequence. - Gevorg Grigoryan; Fei Zhou 002; Steve R. Lustig; Gerbrand Ceder; Dane Morgan; Amy E. Keating (PLoS Comput. Biol., 2006)

Community Accessible Datastore of High-Throughput Calculations: Experiences from the Materials Project. - Dan Gunter; Shreyas Cholia; Anubhav Jain 001; Michael Kocher; Kristin A. Persson; Lavanya Ramakrishnan; Shyue Ping Ong; Gerbrand Ceder (SC Companion, 2012)

Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature. - Leigh Weston; Vahe Tshitoyan; John Dagdelen; Olga Kononova; Amalie Trewartha; Kristin A. Persson; Gerbrand Ceder; Anubhav Jain 001 (J. Chem. Inf. Model., 2019)

Unsupervised word embeddings capture latent knowledge from materials science literature. - Vahe Tshitoyan; John Dagdelen; Leigh Weston; Alexander Dunn; Ziqin Rong; Olga Kononova; Kristin A. Persson; Gerbrand Ceder; Anubhav Jain 001 (Nat., 2019)

COVIDScholar: An automated COVID-19 research aggregation and analysis platform. - Amalie Trewartha; John Dagdelen; Haoyan Huo; Kevin Cruse; Zheren Wang; Tanjin He; Akshay Subramanian; Yuxing Fei; Benjamin Justus; Kristin A. Persson; Gerbrand Ceder (CoRR, 2020)

A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra. - Nathan J. Szymanski; Christopher J. Bartel; Yan Zeng; Qingsong Tu; Gerbrand Ceder (CoRR, 2021)

ULSA: Unified Language of Synthesis Actions for Representation of Synthesis Protocols. - Zheren Wang; Kevin Cruse; Yuxing Fei; Ann Chia; Yan Zeng; Haoyan Huo; Tanjin He; Bowen Deng; Olga Kononova; Gerbrand Ceder (CoRR, 2022)

Structured information extraction from complex scientific text with fine-tuned large language models. - Alexander Dunn; John Dagdelen; Nicholas Walker; Sanghoon Lee; Andrew S. Rosen; Gerbrand Ceder; Kristin A. Persson; Anubhav Jain 001 (CoRR, 2022)

smol: A Python package for cluster expansions and beyond. - Luis Barroso-Luque; Julia H. Yang; Fengyu Xie; Tina Chen; Ronald L. Kam; Zinab Jadidi; Peichen Zhong; Gerbrand Ceder (J. Open Source Softw., 2022)

Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science. - Amalie Trewartha; Nicholas Walker; Haoyan Huo; Sanghoon Lee; Kevin Cruse; John Dagdelen; Alexander Dunn; Kristin A. Persson; Gerbrand Ceder; Anubhav Jain 001 (Patterns, 2022)

Inorganic synthesis recommendation by machine learning materials similarity from scientific literature. - Tanjin He; Haoyan Huo; Christopher J. Bartel; Zheren Wang; Kevin Cruse; Gerbrand Ceder (CoRR, 2023)

CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling. - Bowen Deng; Peichen Zhong; KyuJung Jun; Kevin Han; Christopher J. Bartel; Gerbrand Ceder (CoRR, 2023)

Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-3. - Nicholas Walker; John Dagdelen; Kevin Cruse; Sanghoon Lee; Samuel Gleason; Alexander Dunn; Gerbrand Ceder; A. Paul Alivisatos; Kristin A. Persson; Anubhav Jain 001 (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)

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)

AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories. - Yuxing Fei; Bernardus Rendy; Rishi Kumar; Olympia Dartsi; Hrushikesh P. Sahasrabuddhe; Matthew J. McDermott; Zheren Wang; Nathan J. Szymanski; Lauren N. Walters; David Milsted; Yan Zeng; Anubhav Jain 001; et al. (CoRR, 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)

Cross-functional transferability in universal machine learning interatomic potentials. - Xu Huang; Bowen Deng; Peichen Zhong; Aaron D. Kaplan; Kristin A. Persson; Gerbrand Ceder (CoRR, 2025)

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials. - Kevin Han; Bowen Deng; Amir Barati Farimani; Gerbrand Ceder (CoRR, 2025)