Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 2 min ago
PreprintCC BY 4.0arXiv

pymatgen: 15 years of community growth, new functionality, and future prospects

154 more authors

60 listed; 166 in total.

Abstract

We present a retrospective on the Python Materials Genomics (pymatgen) library, reflecting on 15 years of community growth and established best practices. The current version of pymatgen builds on its robust, open-source foundation, emphasising its collaborative nature. Over the past decade, pymatgen has thrived as one of the largest open-source materials science codebases. We detail how pymatgen supports advanced computational materials science workflows, how it adapts to changing demands, and the lessons learned from its growing community, providing guidance beyond pymatgen's open-source community. Pymatgen will continue to evolve in response to the current rapidly changing landscape of computational materials science and materials informatics. We discuss the challenges and potential solutions to managing the community-driven evolution of pymatgen and provide an outlook on its future development.

Related

  1. X-ray ptychography: state of the art and future prospects
    Manuel Guizar-Sicairos, Virginie Chamard
    OpticaPeer-reviewedCC BY 4.0Full textApplied physics & instrumentationHAL
  2. Prospects for Simultaneously Detecting Long and Short Duration Gravitational Waves from Pulsar Glitches with Current and Future Detectors
    M. Ball, D. Keitel
    arXivPreprintarXiv licenceFull textHigh-energy astrophysicsGravitation & general relativityarXiv
  3. Exothermic and Endothermic Inelastic Dark Matter Interpretations at LZ: Sideband Constraints and Future Prospects
    James B. Dent, Jayden L. Newstead
    arXivPreprintarXiv licenceFull text42 citedHigh-energy & particle physicsarXivOpenAlex
  4. Probing Dark Matter with Strongly Lensed Binary Black Hole Mergers: Prospects in the Near Future
    Koustav N. Maity, Souvik Jana, Ankur Barsode +2
    arXivPreprintCC BY 4.0Full textCosmologyGravitation & general relativityarXiv
  5. Future of Artificial Intelligence for Science in Japan 2024 Community Report
    Yoshitaka Itow, Jia Liu, Hirokazu Maesaka +16
    arXivPreprintCC BY 4.0Full textInstrumentation & methodsApplied physics & instrumentationarXiv
BibTeX
@misc{george2026pymatgen,
  title = {{pymatgen: 15 years of community growth, new functionality, and future prospects}},
  author = {Janine George and Matthew K. Horton and Aaron D. Kaplan and Seán R. Kavanagh and Janosh Riebesell and Andrew S. Rosen and Haoyu Yang and Felix Adams and Mahdi Amachraa and Oxana Andriuc and Omar A. Ashour and Saurabh Bajaj and Ella Banyas and Luis Barroso-Luque and Karlo Berket and Noam Bernstein and Arpit Bhardwaj and Ali Hussain Umar Bhatti and Samuel M. Blau and Brandon Bocklund and Alexander Bonkowski and Danny Broberg and Guillaume Brunin and Kyle Bystrom and Charles A. Cardot and Rees Chang and Chi Chen and Wei Chen and Xin Chen and Yuan Chiang and Kamal Choudhary and Orion A. Cohen and Miguel D. Costa and Stephen T. Dacek III and Maarten de Jong and Tom Demeyere and Alan K. Dozier and Alexander Dunn and Shyam Dwaraknath and Maxwell T. Dylla and Kristjan Eimre and Alexander R. Epstein and Christina Ertural and Marco Esters and Matthew L. Evans and Johannes Falke and Adam Fekete and Bernard Field and Alexandr Fonari and Nathan C. Frey and Dale W. Gaines II and Alex M. Ganose and Matteo Giantomassi and Rhys E. A. Goodall and Petre-Flaviu Gostin and Aayush Gupta and Viet-Anh Ha and Geoffroy Hautier and Niclas Heinsdorf and Michael F. Herbst},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.23538},
  eprint = {2610.23538},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.23538},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - pymatgen: 15 years of community growth, new functionality, and future prospects
AU  - Janine George
AU  - Matthew K. Horton
AU  - Aaron D. Kaplan
AU  - Seán R. Kavanagh
AU  - Janosh Riebesell
AU  - Andrew S. Rosen
AU  - Haoyu Yang
AU  - Felix Adams
AU  - Mahdi Amachraa
AU  - Oxana Andriuc
AU  - Omar A. Ashour
AU  - Saurabh Bajaj
AU  - Ella Banyas
AU  - Luis Barroso-Luque
AU  - Karlo Berket
AU  - Noam Bernstein
AU  - Arpit Bhardwaj
AU  - Ali Hussain Umar Bhatti
AU  - Samuel M. Blau
AU  - Brandon Bocklund
AU  - Alexander Bonkowski
AU  - Danny Broberg
AU  - Guillaume Brunin
AU  - Kyle Bystrom
AU  - Charles A. Cardot
AU  - Rees Chang
AU  - Chi Chen
AU  - Wei Chen
AU  - Xin Chen
AU  - Yuan Chiang
AU  - Kamal Choudhary
AU  - Orion A. Cohen
AU  - Miguel D. Costa
AU  - Stephen T. Dacek III
AU  - Maarten de Jong
AU  - Tom Demeyere
AU  - Alan K. Dozier
AU  - Alexander Dunn
AU  - Shyam Dwaraknath
AU  - Maxwell T. Dylla
AU  - Kristjan Eimre
AU  - Alexander R. Epstein
AU  - Christina Ertural
AU  - Marco Esters
AU  - Matthew L. Evans
AU  - Johannes Falke
AU  - Adam Fekete
AU  - Bernard Field
AU  - Alexandr Fonari
AU  - Nathan C. Frey
AU  - Dale W. Gaines II
AU  - Alex M. Ganose
AU  - Matteo Giantomassi
AU  - Rhys E. A. Goodall
AU  - Petre-Flaviu Gostin
AU  - Aayush Gupta
AU  - Viet-Anh Ha
AU  - Geoffroy Hautier
AU  - Niclas Heinsdorf
AU  - Michael F. Herbst
PY  - 2026
DA  - 2026/10/16
PB  - arXiv
DO  - 10.48550/arxiv.2610.23538
UR  - https://arxiv.org/abs/2610.23538
AB  - We present a retrospective on the Python Materials Genomics (pymatgen) library, reflecting on 15 years of community growth and established best practices. The current version of pymatgen builds on its robust, open-source foundation, emphasising its collaborative nature. Over the past decade, pymatgen has thrived as one of the largest open-source materials science codebases. We detail how pymatgen supports advanced computational materials science workflows, how it adapts to changing demands, and the lessons learned from its growing community, providing guidance beyond pymatgen's open-source community. Pymatgen will continue to evolve in response to the current rapidly changing landscape of computational materials science and materials informatics. We discuss the challenges and potential solutions to managing the community-driven evolution of pymatgen and provide an outlook on its future development.
ER  -