Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Cited by (1,039)

Items 1,001–1,039 of 1,039

1. Artificial intelligence-driven approaches for materials design and discovery
   Cheng, Mouyang, Fu, Chu-Liang, Okabe, Ryotaro, Chotrattanapituk, Abhijatmedhi, Boonkird, Artittaya, Hung, Nguyen Tuan, Li, Mingda · 2026 · Nature Materials
2. How to verify the precision of density-functional-theory implementations via reproducible and universal workflows
   Bosoni, Emanuele, Beal, Louis, Bercx, Marnik, Blaha, Peter, Blügel, Stefan, Bröder, Jens, Callsen, Martin, Cottenier, Stefaan, Degomme, Augustin, Dikan, Vladimir et al. · 2023 · Nature Reviews Physics
3. Data-driven design of novel halide perovskite alloys
   Mannodi-Kanakkithodi, Arun, Chan, Maria K. Y. · 2022 · Energy & Environmental Science
4. Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions
   Taylor, Michael G., Yang, Tzuhsiung, Lin, Sean, Nandy, Aditya, Janet, Jon Paul, Duan, Chenru, Kulik, Heather J. · 2020 · The Journal of Physical Chemistry A
5. Automating alloy design and discovery with physics-aware multimodal multiagent AI
   Ghafarollahi, Alireza, Buehler, Markus J. · 2025 · Proceedings of the National Academy of Sciences
6. Machine Learning for the Discovery, Design, and Engineering of Materials.
   Duan, Chenru, Nandy, Aditya, Kulik, Heather J. · 2022 · Annual Review of Chemical and Biomolecular Engineering
7. Text Mining Metal–Organic Framework Papers
   Park, Sanghoon, Kim, Baekjun, Choi, Sihoon, Boyd, Peter G., Smit, Berend, Kim, Jihan · 2018 · Journal of Chemical Information and Modeling
8. Machine learning in materials design: Algorithm and application*
   Song, Zhilong, Chen, Xiwen, Meng, Fanbin, Cheng, Guanjian, Wang, Chen, Sun, Zhongti, Yin, Wan-Jian · 2020 · Chinese Physics B
9. Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design
   Janet, Jon Paul, Gani, Terry Z. H., Steeves, Adam H., Ioannidis, Efthymios I., Kulik, Heather J. · 2017 · Industrial & Engineering Chemistry Research
10. Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21 ^st century
    Ravinder, Venugopal, Vineeth, Bishnoi, Suresh, Singh, Sourabh, Zaki, Mohd, Grover, Hargun Singh, Bauchy, Mathieu, Agarwal, Manish, Krishnan, N. M. Anoop · 2021 · International Journal of Applied Glass Science
11. Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning Perspectives
    Myung, Chang Woo, Hajibabaei, Amir, Cha, Ji‐Hyun, Ha, Miran, Kim, Junu, Kim, Kwang S. · 2022 · Advanced Energy Materials
12. Learn-and-Match Molecular Cations for Perovskites
    Park, Heesoo, Mall, Raghvendra, Alharbi, Fahhad H., Sanvito, Stefano, Tabet, Nouar, Bensmail, Halima, El-Mellouhi, Fedwa · 2019 · The Journal of Physical Chemistry A
13. Combining phonon accuracy with high transferability in Gaussian approximation potential models
    George, Janine, Hautier, Geoffroy, Bartók, Albert P., Csányi, Gábor, Deringer, Volker L. · 2020 · The Journal of Chemical Physics
14. An Ensemble Learning Platform for the Large-Scale Exploration of New Double Perovskites
    Wang, Zhilong, Han, Yanqiang, Lin, Xirong, Cai, Junfei, Wu, Sicheng, Li, Jinjin · 2022 · ACS Applied Materials & Interfaces
15. Mapping and classifying molecules from a high-throughput structural database
    De, Sandip, Musil, Felix, Ingram, Teresa, Baldauf, Carsten, Ceriotti, Michele · 2017 · Journal of Cheminformatics
16. Importance of structural deformation features in the prediction of hybrid perovskite bandgaps
    Park, Heesoo, Mall, Raghvendra, Ali, Adnan, Sanvito, Stefano, Bensmail, Halima, El-Mellouhi, Fedwa · 2020 · Computational Materials Science
17. Computational Discovery of Li-M-O Ion Exchange Materials for Lithium Extraction from Brines
    Snydacker, David H., Hegde, Vinay I., Aykol, Muratahan, Wolverton, C. · 2018 · Chemistry of Materials
18. Screening Perovskites from ABO₃ Combinations Generated by Constraint Satisfaction Techniques Using Machine Learning
    Zhao, Jie, Wang, Xiaoyan · 2022 · ACS Omega
19. Improved physics-based structural descriptors of perovskite materials enable higher accuracy of machine learning
    Li, Changjiao, Hao, Hua, Xu, Ben, Shen, Zhonghui, Zhou, Enhao, Jiang, Dongbing, Liu, Hanxing · 2021 · Computational Materials Science
20. Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts
    Mai, Haoxin, Le, Tu C., Hisatomi, Takashi, Chen, Dehong, Domen, Kazunari, Winkler, David A., Caruso, Rachel A. · 2021 · iScience
21. Phase‐Mapper: Accelerating Materials Discovery with AI
    Bai, Junwen, Xue, Yexiang, Bjorck, Johan, Bras, Ronan Le, Rappazzo, Brendan, Bernstein, Richard, Suram, Santosh K., van Dover, R. Bruce, Gregoire, John M., Gomes, Carla P. · 2018 · AI Magazine
22. Roadmap on methods and software for electronic structure based simulations in chemistry and materials
    Blum, Volker, Asahi, Ryoji, Autschbach, Jochen, Bannwarth, Christoph, Bihlmayer, Gustav, Blügel, Stefan, Burns, Lori A, Crawford, T Daniel, Dawson, William, de Jong, Wibe Albert et al. · 2024 · Electronic Structure
23. A Machine Learning‐Assisted Approach to a Rapid and Reliable Screening for Mechanically Stable Perovskite‐Based Materials
    Jaafreh, Russlan, Sharan, Abhishek, Sajjad, Muhammad, Singh, Nirpendra, Hamad, Kotiba · 2023 · Advanced Functional Materials
24. Generative AI for crystal structures: A review
    De Breuck, Pierre-Paul, Wang, Hai-Chen, Rignanese, Gian-Marco, Botti, Silvana, Marques, Miguel A. L. · 2025 · npj Computational Materials
25. Evaluation of performance of machine learning methods in mining structure–property data of halide perovskite materials
    Zhao, Ruoting, Xing, Bangyu, Mu, Huimin, Fu, Yuhao, Zhang, Lijun · 2022 · Chinese Physics B
26. A review on machine learning-guided design of energy materials
    Kim, Seongmin, Xu, Jiaxin, Shang, Wenjie, Xu, Zhihao, Lee, Eungkyu, Luo, Tengfei · 2024 · Progress in Energy
27. Knowledge-guided large language model for material science
    Wang, Guanjie, Hu, Jingjing, Zhou, Jian, Liu, Sen, Li, Qingjiang, Sun, Zhimei · 2025 · Review of Materials Research
28. Data‐Driven Investigation of the Synthesizability and Bandgap of Double Perovskite Halides
    Kim, Joonchul, Min, Kyoungmin · 2022 · Advanced Theory and Simulations
29. Modular large language model agents for multi-task computational materials science
    Chaudhari, Akshat, Ock, Janghoon, Barati Farimani, Amir · 2026 · Communications Materials
30. Unlocking new horizons, challenges of integrating machine learning to energy conversion and storage research
    Velpandian, Muthuraja, Basu, Suddhasatwa · 2025 · Indian Chemical Engineer
31. Computation of photovoltaic and stability properties of hybrid organic–inorganic perovskites via convolutional neural networks
    Aristizabal-Ferreira, Victor Alexander, Guevara-Vela, José Manuel, Sauza-de la Vega, Arturo, Pendás, Ángel Martín, Fuentes-Pineda, Gibran, Rocha-Rinza, Tomás · 2022 · Theoretical Chemistry Accounts
32. The materials data ecosystem: Materials data science and its role in data-driven materials discovery
    Yin, Hai-Qing, Jiang, Xue, Liu, Guo-Quan, Elder, Sharon, Xu, Bin, Zheng, Qing-Jun, Qu, Xuan-Hui · 2018 · Chinese Physics B
33. VASPilot: MCP-facilitated multi-agent intelligence for autonomous VASP simulations
    Liu 刘, Jiaxuan 家轩, Zhu 朱, Tiannian 天念, Ye 叶, Caiyuan 财渊, Fang 方, Zhong 忠, Weng 翁, Hongming 红明, Wu 吴, Quansheng 泉生 · 2025 · Chinese Physics B
34. Adsorb-Agent: autonomous identification of stable adsorption configurations <i>via</i> a large language model agent
    Ock, Janghoon, Meda, Radheesh Sharma, Vinchurkar, Tirtha, Jadhav, Yayati, Barati Farimani, Amir · 2026 · Digital Discovery
35. Evaluating Large Language Models in Scientific Discovery
    Song, Zhangde, Lu, Jieyu, Du, Yuanqi, Yu, Botao, Pruyn, Thomas M., Huang, Yue, Guo, Kehan, Luo, Xiuzhe, Qu, Yuanhao, Qu, Yi et al. · 2025 · arXiv
36. Effect of Solvent Ratio (Ethylene Glycol/Water) in the Synthesis of Pt/C Cathode Catalysts on PEM Fuel Cell Performance
    Böhm, Daniel, Mangoufis-Giasin, Iosif, Mirzayeva, Farah, Vajic, Zeljana, Hoeft, Hendrik, Hengge, Katharina, Jurzinsky, Tilman, Burger, Thomas, El-Sayed, Hany A. · 2024 · Journal of the Electrochemical Society
37. Agentic framework for programmatic crystal structure generation using a fine-tuned worker-supervisor large language model
    Baibakova, Viktoriia, Serov, Alexey · 2026 · Energy and AI
38. Coevolution of large language models with physical models boosts advanced battery research
    Li, Jia-hui, Hu, Yue, Xia, Guangyu, Mo, Wendi, Li, Baorong, Jia, Yingzhen, Gao, Yang, Xuan, Fuzhen, Liu, Honglai, Lian, Cheng · 2025 · Cell Reports Physical Science
39. Triple Point Topological Metals
    Zhu, Ziming, Winkler, Georg W., Wu, QuanSheng, Li, Ju, Soluyanov, Alexey A. · 2016 · ETH Zurich