Shyue Ping Ong

Last updated
Shyue Ping Ong
Alma mater University of Cambridge
Massachusetts Institute of Technology
Known forAI for materials science, Computational materials science and design
Scientific career
Fields Materials science
Chemistry
Physics
Institutions University of California, San Diego
Doctoral advisor Gerbrand Ceder
Website materialsvirtuallab.org

Shyue Ping Ong is a Singaporean scientist, who is a professor at the Aiiso Yufeng Li Family Department of Chemical and Nano Engineering at the University of California, San Diego (UCSD). He is known for his research in materials informatics. [1] [2] , a field that integrates materials science with data science and artificial intelligence to accelerate the discovery and design of novel materials. His research group, the Materials Virtual Lab, [3] focuses on the development of computational tools and machine learning techniques to predict materials properties and accelerate materials design. He is the founder and lead developer of Python Materials Genomics (pymatgen), [4] an open-source materials analysis code. He is also one of the developers of the Materials Project, a public database of ab initio calculated material properties.

Contents

Career

Shyue Ping Ong received a Bachelor of Arts and Master of Engineering degree in Electrical and Information Science from the University of Cambridge in 1999, and a PhD in materials science and engineering from the Massachusetts Institute of Technology in 2011. [5] In 2013, he joined the Aiiso Yufeng Li Family Department of Chemical and Nano Engineering [6] at the University of California, San Diego as an assistant professor. He was promoted to associate professor with tenure in 2017 and subsequently to full professor in 2021. He has published over 150 scientific papers [7] in the fields of AI for materials science, materials software infrastructure, alkali-ion battery materials, solid-state lighting materials, and complex-concentrated (i.e., "high-entropy") materials.

Research

AI for Materials Science

In 2019, Chi Chen and Shyue Ping Ong showed that graph networks, a machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization, [8] can be used to accurately predict properties in both molecules and crystals. [9] Such models also learn element embeddings that encode periodic chemical trends, which can be used to train property models property models with smaller amounts of data. [10]

In 2022, Chi Chen and Shyue Ping Ong developed the first universal machine learning interatomic potential (MLIP) with coverage of 89 elements of the entire periodic table. [11] This Materials 3-body Graph Network (M3GNet) combines message-passing graph neural network with three-body interactions and was trained on the database of structural relaxations in the Materials Project. [12] Such models are also referred to as foundation potentials with broad applications in materials discovery and simulations, [13] and has spawned a highly active area of research. [14] [15] [16]

In 2025, Aaron Kaplan, Runze Liu, Ji Qi, Kristin Persson, and Shyue Ping Ong introduced MatPES, [17] a foundational potential energy surface (PES) dataset for training foundation potentials. [18] This effort addresses critical issues with the accuracy of existing PES datasets and allows the training of potentials with far less data. It also introduced the first high-fidelity PES training dataset based on the revised regularized strongly constrained and appropriately normed (r2SCAN) functional with improved descriptions of interatomic bonding.

Electrodes and Solid Electrolytes for Alkali-ion Batteries

Shyue Ping Ong has made significant contributions in the development of electrodes and solid electrolytes for alkali-ion batteries. Ong's work often involves the use of high-throughput first-principles computational methods, in combination with machine learning techniques.

In 2011, Yifei Mo, Shyue Ping Ong and Gerbrand Ceder used ab initio molecular dynamics simulations to show that the Li10GeP2S12 (LGPS) solid electrolyte is essentially a 3-dimensional conductor (not a 1-dimensional conductor as previously assumed) and would have a narrow electrochemical window. [19] In 2013, Shyue Ping Ong and Gerbrand Ceder showed that the Ge in LGPS can be replaced with cheaper Si or Sn without significant impact on its ionic conductivity and electrochemical stability. [20] These predictions were independently validated by experimental studies. [21] [22]

In 2020, Haodong Liu, Zhuoying Zhu, Shyue Ping Ong and Ping Liu developed the disordered rock salt Li3V2O5 fast-charging anode [23] that can reversibly cycle two lithium ions at an average voltage of about 0.6 volts versus a Li/Li+ reference electrode. [24] The low voltage and diffusion barriers of this anode lead to significantly improved energy density and rate performance, and is currently being commercialized by Tyfast. [25]

In 2021, Erik A. Wu, Swastika Banerjee, Ying Shirley Meng, Shyue Ping Ong discovered the Na3-xY1-xZrxCl6 (NYZC) ion conductor [26] that is electrochemically stable up to 3.8 V vs. Na/Na+ and chemically compatible with oxide cathodes. [27]

In 2021, Ji Qi and Shyue Ping Ong showed that large-scale MD simulations using MLIPs can be used to overcome the short length and time scales in AIMD simulations, which leads to accurate prediction of the room-temperature ionic conductivities for the Li0.33La0.56TiO3, Li3YCl6 and Li7P3S11 lithium superionic conductors. [28]

In 2024, Jianbin Zhou, Manas Likhit Holekevi Chandrappa, Shyue Ping Ong and Ping Liu developed a novel S9.3I molecular crystal cathode for low-cost solid-state lithium-sulfur batteries. [29] This new cathode has a much higher electronic conductivity compared to elemental sulfur and can be repaired periodically through heating due to its relatively low melting point of around 65 °C. [30]

Solid-state lighting

In 2018, Zhenbin Wang and Shyue Ping Ong discovered Sr2LiAlO4, [31] the first known Sr-Li-Al-O quaternary crystal, via data-driven structure prediction and high-throughput screening. Eu2+- and Ce3+-activated Sr2LiAlO4 are experimentally confirmed to be green-yellow and blue phosphors, respectively, with excellent thermal quenching resistance. [32]

In 2020, Mahdi Amachraa and Shyue Ping Ong unified the crossover and thermal ionization theories of thermal quenching (TQ) in phosphors into a single predictive model. They showed that TQ under the crossover mechanism is related to the local environment stability of the activator, and a unified model can predict the experimental TQ in 29 known phosphors to within a root-mean-square error of ~3.1−7.6%. [33]

Community contributions

Shyue Ping Ong is the founder and lead developer of Python Materials Genomics (pymatgen [34] ), a popular open-source materials analysis code. [35] He is also one of the developers of the Materials Project, an open database of ab initio calculated material properties. [36] He was part of the team that developed much of the early Materials Project infrastructure in 2012-2015, including its application programming interface. [37]

Awards and honors

Shyue Ping Ong is a recipient of the US Department of Energy Early Career Research Program award in 2014 [38] and the Office of Naval Research Young Investigator Program award in 2015. [39] He is also a Clarivate Highly Cited Researcher since 2021. [40]

References

  1. ↑ "Supercomputing Our Way to Better Materials". today.ucsd.edu. Retrieved 2025-05-14.
  2. ↑ Wogan, Tim. "Machine learning massively speeds up scouring of periodic table for stable structures". Chemistry World. Retrieved 2025-05-14.
  3. ↑ "Materials Virtual Lab". Materials Virtual Lab. 2025-03-08. Retrieved 2025-06-22.
  4. ↑ "Home". pymatgen. Retrieved 2025-06-22.
  5. ↑ "Shyue Ping Ong | Jacobs School of Engineering". jacobsschool.ucsd.edu. Retrieved 2025-06-10.
  6. ↑ "Homepage | Department of NanoEngineering". cne.ucsd.edu. Retrieved 2025-06-22.
  7. ↑ "Shyue Ping Ong". scholar.google.com. Retrieved 2025-06-10.
  8. ↑ Battaglia, Peter W.; Hamrick, Jessica B.; Bapst, Victor; Sanchez-Gonzalez, Alvaro; Zambaldi, Vinicius; Malinowski, Mateusz; Tacchetti, Andrea; Raposo, David; Santoro, Adam (2018), Relational inductive biases, deep learning, and graph networks, arXiv: 1806.01261
  9. ↑ Chen, Chi; Ye, Weike; Zuo, Yunxing; Zheng, Chen; Ong, Shyue Ping (2019-05-14). "Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals". Chemistry of Materials. 31 (9): 3564–3572. arXiv: 1812.05055 . doi:10.1021/acs.chemmater.9b01294. ISSN   0897-4756.
  10. ↑ Chen, Chi; Zuo, Yunxing; Ye, Weike; Li, Xiangguo; Ong, Shyue Ping (2021-01-14). "Learning properties of ordered and disordered materials from multi-fidelity data". Nature Computational Science. 1 (1): 46–53. arXiv: 2005.04338 . doi:10.1038/s43588-020-00002-x. ISSN   2662-8457. PMID   38217148.
  11. ↑ Chen, Chi; Ong, Shyue Ping (2022-11-28). "A universal graph deep learning interatomic potential for the periodic table". Nature Computational Science. 2 (11): 718–728. arXiv: 2202.02450 . doi:10.1038/s43588-022-00349-3. ISSN   2662-8457. PMID   38177366.
  12. ↑ "Nanoengineers Develop a Predictive Database for Materials". today.ucsd.edu. Retrieved 2025-05-14.
  13. ↑ "AI invents millions of materials that don't yet exist". The Independent. 2022-11-28. Retrieved 2025-05-14.
  14. ↑ Batatia, Ilyes; Benner, Philipp; Chiang, Yuan; Elena, Alin M.; Kovács, Dávid P.; Riebesell, Janosh; Advincula, Xavier R.; Asta, Mark; Avaylon, Matthew (2024), A foundation model for atomistic materials chemistry, arXiv: 2401.00096
  15. ↑ Deng, Bowen; Zhong, Peichen; Jun, KyuJung; Riebesell, Janosh; Han, Kevin; Bartel, Christopher J.; Ceder, Gerbrand (2023-09-14). "CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling". Nature Machine Intelligence. 5 (9): 1031–1041. doi:10.1038/s42256-023-00716-3. ISSN   2522-5839.
  16. ↑ Barroso-Luque, Luis; Shuaibi, Muhammed; Fu, Xiang; Wood, Brandon M.; Dzamba, Misko; Gao, Meng; Rizvi, Ammar; Zitnick, C. Lawrence; Ulissi, Zachary W. (2024), Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models, arXiv: 2410.12771
  17. ↑ "MatPES". matpes.ai. Retrieved 2025-05-14.
  18. ↑ Kaplan, Aaron D.; Liu, Runze; Qi, Ji; Ko, Tsz Wai; Deng, Bowen; Riebesell, Janosh; Ceder, Gerbrand; Persson, Kristin A.; Ong, Shyue Ping (2025), A Foundational Potential Energy Surface Dataset for Materials, arXiv: 2503.04070
  19. ↑ Mo, Yifei; Ong, Shyue Ping; Ceder, Gerbrand (2012-01-10). "First Principles Study of the Li 10 GeP 2 S 12 Lithium Super Ionic Conductor Material" . Chemistry of Materials. 24 (1): 15–17. doi:10.1021/cm203303y. ISSN   0897-4756.
  20. ↑ Ong, Shyue Ping; Mo, Yifei; Richards, William Davidson; Miara, Lincoln; Lee, Hyo Sug; Ceder, Gerbrand (2013). "Phase stability, electrochemical stability and ionic conductivity of the Li 10±1 MP 2 X 12 (M = Ge, Si, Sn, Al or P, and X = O, S or Se) family of superionic conductors" . Energy Environ. Sci. 6 (1): 148–156. Bibcode:2013EnEnS...6..148O. doi:10.1039/C2EE23355J. ISSN   1754-5692.
  21. ↑ Bron, Philipp; Johansson, Sebastian; Zick, Klaus; Schmedt auf der Günne, Jörn; Dehnen, Stefanie; Roling, Bernhard (2013-10-23). "Li 10 SnP 2 S 12 : An Affordable Lithium Superionic Conductor" . Journal of the American Chemical Society. 135 (42): 15694–15697. Bibcode:2013JAChS.13515694B. doi:10.1021/ja407393y. ISSN   0002-7863. PMID   24079534.
  22. ↑ Kuhn, Alexander; Gerbig, Oliver; Zhu, Changbao; Falkenberg, Frank; Maier, Joachim; Lotsch, Bettina V. (2014-05-30). "A new ultrafast superionic Li-conductor: ion dynamics in Li 11 Si 2 PS 12 and comparison with other tetragonal LGPS-type electrolytes". Phys. Chem. Chem. Phys. 16 (28): 14669–14674. Bibcode:2014PCCP...1614669K. doi: 10.1039/C4CP02046D . ISSN   1463-9076. PMID   24916653.
  23. ↑ Timmer, John (2020-09-04). "Could "disordered rock salts" bring order to next-gen lithium batteries?". Ars Technica. Retrieved 2025-05-14.
  24. ↑ Liu, Haodong; Zhu, Zhuoying; Yan, Qizhang; Yu, Sicen; He, Xin; Chen, Yan; Zhang, Rui; Ma, Lu; Liu, Tongchao; Li, Matthew; Lin, Ruoqian; Chen, Yiming; Li, Yejing; Xing, Xing; Choi, Yoonjung (2020-09-03). "A disordered rock salt anode for fast-charging lithium-ion batteries". Nature. 585 (7823): 63–67. Bibcode:2020Natur.585...63L. doi:10.1038/s41586-020-2637-6. ISSN   0028-0836. OSTI   1659690. PMID   32879503.
  25. ↑ "Home". TYFAST. Retrieved 2025-05-14.
  26. ↑ "New Material Breakthrough for Stable High-Voltage Long-Life Solid-State Batteries". SciTechDaily. 2021-02-23. Retrieved 2025-05-14.
  27. ↑ Wu, Erik A.; Banerjee, Swastika; Tang, Hanmei; Richardson, Peter M.; Doux, Jean-Marie; Qi, Ji; Zhu, Zhuoying; Grenier, Antonin; Li, Yixuan; Zhao, Enyue; Deysher, Grayson; Sebti, Elias; Nguyen, Han; Stephens, Ryan; Verbist, Guy (2021-02-23). "A stable cathode-solid electrolyte composite for high-voltage, long-cycle-life solid-state sodium-ion batteries". Nature Communications. 12 (1): 1256. Bibcode:2021NatCo..12.1256W. doi:10.1038/s41467-021-21488-7. ISSN   2041-1723. PMC   7902639 . PMID   33623048.
  28. ↑ Qi, J.; Banerjee, S.; Zuo, Y.; Chen, C.; Zhu, Z.; Holekevi Chandrappa, M.L.; Li, X.; Ong, S.P. (November 2021). "Bridging the gap between simulated and experimental ionic conductivities in lithium superionic conductors". Materials Today Physics. 21 100463. arXiv: 2102.08413 . Bibcode:2021MTPhy..2100463Q. doi:10.1016/j.mtphys.2021.100463.
  29. ↑ Wogan, Tim. "Iodide addition could make high energy lithium-sulfur batteries commercially viable". Chemistry World. Retrieved 2025-05-14.
  30. ↑ Zhou, Jianbin; Holekevi Chandrappa, Manas Likhit; Tan, Sha; Wang, Shen; Wu, Chaoshan; Nguyen, Howie; Wang, Canhui; Liu, Haodong; Yu, Sicen; Miller, Quin R. S.; Hyun, Gayea; Holoubek, John; Hong, Junghwa; Xiao, Yuxuan; Soulen, Charles (2024-03-14). "Healable and conductive sulfur iodide for solid-state Li–S batteries". Nature. 627 (8003): 301–305. Bibcode:2024Natur.627..301Z. doi:10.1038/s41586-024-07101-z. ISSN   0028-0836. OSTI   2336571. PMID   38448596.
  31. ↑ Diego, University of California-San. "Computers aid discovery of new, inexpensive material to make LEDs with high color quality". phys.org. Retrieved 2025-05-14.
  32. ↑ Wang, Zhenbin; Ha, Jungmin; Kim, Yoon Hwa; Im, Won Bin; McKittrick, Joanna; Ong, Shyue Ping (May 2018). "Mining Unexplored Chemistries for Phosphors for High-Color-Quality White-Light-Emitting Diodes". Joule. 2 (5): 914–926. Bibcode:2018Joule...2..914W. doi: 10.1016/j.joule.2018.01.015 .
  33. ↑ Amachraa, Mahdi; Wang, Zhenbin; Chen, Chi; Hariyani, Shruti; Tang, Hanmei; Brgoch, Jakoah; Ong, Shyue Ping (2020-07-28). "Predicting Thermal Quenching in Inorganic Phosphors" . Chemistry of Materials. 32 (14): 6256–6265. doi:10.1021/acs.chemmater.0c02231. ISSN   0897-4756.
  34. ↑ "Home". pymatgen. Retrieved 2025-05-14.
  35. ↑ Ong, Shyue Ping; Richards, William Davidson; Jain, Anubhav; Hautier, Geoffroy; Kocher, Michael; Cholia, Shreyas; Gunter, Dan; Chevrier, Vincent L.; Persson, Kristin A.; Ceder, Gerbrand (February 2013). "Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis". Computational Materials Science. 68: 314–319. doi:10.1016/j.commatsci.2012.10.028. hdl: 1721.1/101936 .
  36. ↑ Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy; Chen, Wei; Richards, William Davidson; Dacek, Stephen; Cholia, Shreyas; Gunter, Dan; Skinner, David; Ceder, Gerbrand; Persson, Kristin A. (2013-07-01). "Commentary: The Materials Project: A materials genome approach to accelerating materials innovation". APL Materials. 1 (1): 011002. Bibcode:2013APLM....1a1002J. doi:10.1063/1.4812323. hdl: 2078.1/132962 . ISSN   2166-532X.
  37. ↑ Ong, Shyue Ping; Cholia, Shreyas; Jain, Anubhav; Brafman, Miriam; Gunter, Dan; Ceder, Gerbrand; Persson, Kristin A. (February 2015). "The Materials Application Programming Interface (API): A simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles". Computational Materials Science. 97: 209–215. doi:10.1016/j.commatsci.2014.10.037.
  38. ↑ "Early career awards go to 12 UC scientists". University of California. 2014-05-07. Retrieved 2025-05-14.
  39. ↑ "NanoEngineering Professor Wins ONR Grant from U.S. Office of Naval Research to Study Materials Interfaces". jacobsschool.ucsd.edu. Retrieved 2025-05-14.
  40. ↑ "Highly Cited Researchers | Clarivate". clarivate.com. 2024-11-13. Retrieved 2025-05-14.
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