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Mr Tong Xie

Mr Tong Xie

Adjunct Associate Lecturer
Engineering
Photovoltaic and Renewable Energy Engineering

Tong Xie is an Associate Lecturer at the School of Photovoltaic and Renewable Energy Engineering (SPREE) at UNSW Sydney, where he leads the university’s AI4Science initiative. His research advances the intersection of machine learning, materials science, chemistry, and natural language processing, with a particular focus on large-scale scientific foundational models and AI-driven scientific discovery. Tong has directed several breakthrough projects at UNSW, including DARWIN—the world’s first physical-science large language model—and MiST, the first reasoning-capable model purpose-built for chemical sciences.

Recognised among NCI Australia’s Top 10 HPC–AI Talents, Tong plays an active role in shaping the global AI4Science community, serving as a peer reviewer for more than 15 leading journals and for multiple national and private research funding bodies across Australia, Europe, and the United States.

 UNSW AI4Science group homepage, More details about Tong Xie.
E-mail
tong.xie@unsw.edu.au
  • Journal articles | 2025
    Liu X; Hu B; Liu P; Huang M; Li M; Wan Y; Hoex B; Xie T, 2025, 'Accelerating the discovery of high-efficiency donor-acceptor pairs in organic photovoltaics via SolarPCE-Net guided screening', JOURNAL OF MATERIALS CHEMISTRY A, http://dx.doi.org/10.1039/d5ta04854k
    Journal articles | 2024
    Wang S; Wan Y; Song N; Liu Y; Xie T; Hoex B, 2024, 'Automatically Generated Datasets: Present and Potential Self-Cleaning Coating Materials', Scientific Data, 11, http://dx.doi.org/10.1038/s41597-024-02983-0
    Journal articles | 2024
    Xie T; Wan Y; Wang H; Østrøm I; Wang S; He M; Deng R; Wu X; Grazian C; Kit C; Hoex B, 2024, 'Opinion Mining by Convolutional Neural Networks for Maximizing Discoverability of Nanomaterials', Journal of Chemical Information and Modeling, 64, pp. 2746 - 2759, http://dx.doi.org/10.1021/acs.jcim.3c00746
    Journal articles | 2024
    Xie T; Wan Y; Zhou Y; Huang W; Liu Y; Linghu Q; Wang S; Kit C; Grazian C; Zhang W; Hoex B, 2024, 'Creation of a structured solar cell material dataset and performance prediction using large language models', Patterns, 5, http://dx.doi.org/10.1016/j.patter.2024.100955
    Journal articles | 2023
    Liao B; Wu X; Wu W; Liu C; Ma S; Wang S; Xie T; Wang Q; Du Z; Shen W; Li X; Li W; Hoex B, 2023, 'Tube-type plasma-enhanced atomic layer deposition of aluminum oxide: Enabling record lab performance for the industry with demonstrated cell efficiencies >24%', Progress in Photovoltaics Research and Applications, 31, pp. 52 - 61, http://dx.doi.org/10.1002/pip.3607
    Journal articles | 2022
    Wang S; Xie T; Liang R; Zhang Y; Ma FJ; Payne D; Scardera G; Hoex B, 2022, 'An Artificial-Intelligence-Assisted Investigation on the Potential of Black Silicon Nanotextures for Silicon Solar Cells', ACS Applied Nano Materials, 5, pp. 11636 - 11647, http://dx.doi.org/10.1021/acsanm.2c02619
  • Preprints | 2025
    Xie T; Wan Y; Liu Y; Zeng Y; Wang S; Zhang W; Grazian C; Kit C; Ouyang W; Zhou D; Hoex B, 2025, Large Language Models as Materials Science Adapted Learners, http://dx.doi.org/10.21203/rs.3.rs-6752901/v1
    Preprints | 2024
    Wan Y; Liu Y; Ajith A; Grazian C; Hoex B; Zhang W; Kit C; Xie T; Foster I, 2024, SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation, http://dx.doi.org/10.48550/arxiv.2405.09939
    Preprints | 2024
    Wan Y; Xie T; Wu N; Zhang W; Kit C; Hoex B, 2024, From Tokens to Materials: Leveraging Language Models for Scientific Discovery, http://dx.doi.org/10.48550/arxiv.2410.16165
    Preprints | 2024
    Xie T; Wan Y; Liu Y; Zeng Y; Wang S; Zhang W; Grazian C; Kit C; Ouyang W; Zhou D; Hoex B, 2024, DARWIN 1.5: Large Language Models as Materials Science Adapted Learners, http://dx.doi.org/10.48550/arxiv.2412.11970
    Conference Papers | 2024
    Xie T; Zhang H; Wang S; Wan Y; Razzak I; Kit C; Zhang W; Hoex B, 2024, 'ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity', in IEEE International Conference on Data Mining Workshops Icdmw, pp. 907 - 911, http://dx.doi.org/10.1109/ICDMW65004.2024.00126
    Preprints | 2024
    Xie T; Zhang H; Wang S; Wan Y; Razzak I; Kit C; Zhang W; Hoex B, 2024, ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity, http://dx.doi.org/10.48550/arxiv.2411.12000
    Conference Papers | 2024
    Ye Y; Ren J; Wang S; Wan Y; Razzak I; Hoex B; Wang H; Xie T; Zhang W, 2024, 'Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model', in Advances in Neural Information Processing Systems
    Preprints | 2024
    Ye Y; Ren J; Wang S; Wan Y; Razzak I; Hoex B; Wang H; Xie T; Zhang W, 2024, Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model, http://dx.doi.org/10.48550/arxiv.2404.03080
    Preprints | 2023
    Xie T; Wan Y; Huang W; Yin Z; Liu Y; Wang S; Linghu Q; Kit C; Grazian C; Zhang W; Razzak I; Hoex B, 2023, DARWIN Series: Domain Specific Large Language Models for Natural Science, http://dx.doi.org/10.48550/arxiv.2308.13565
    Preprints | 2023
    Xie T; Wan Y; Huang W; Zhou Y; Liu Y; Linghu Q; Wang S; Kit C; Grazian C; Zhang W; Hoex B, 2023, Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT, http://dx.doi.org/10.48550/arxiv.2304.02213
    Preprints | 2022
    Xie T; Wan Y; Li W; Linghu Q; Wang S; Cai Y; Liu H; Kit C; Grazian C; Hoex B, 2022, Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks, http://dx.doi.org/10.48550/arxiv.2212.02805

AI4Science & Material Discovery, Scientific Large Language Models, Natural Language Processing, Renewable Energy / Photovoltaics & Applied Materials Science, Computational Materials