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Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions.
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Chi-Kai Lin, Dan Zhao, Wen-Yang Gao, Zhenzhen Yang, Jingyun Ye, Tao Xu, Qingfeng Ge, Shengqian Ma, and Di-Jia Liu. Tunability of band gaps in metal–organic frameworks. Inorganic chemistry
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Li-Ming Yang, Guo-Yong Fang, Jing Ma, Eric Ganz, and Sang Soo Han. Band gap engineering of paradigm mof-5. Crystal growth & design
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Muhammad Usman, Shruti Mendiratta, and Kuang-Lieh Lu. Semiconductor metal–organic frameworks: future low- g”bandgap materials. Advanced Materials
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Hjorth Larsen, A., Jørgen Mortensen, J., Blomqvist, J., Castelli, I. E., Christensen, R., Dułak, M., Friis, J., Groves, M. N., Hammer, B., Hargus, C., Hermes, E. D., Jennings, P. C., Bjerre Jensen, P., Kermode, J., Kitchin, J. R., Leonhard Kolsbjerg, E., Kubal, J., Kaasbjerg, K., Lysgaard, S., … Jacobsen, K. W. (2017). The atomic simulation environment—a python library for working with atoms. Journal of Physics: Condensed Matter, 29(27), 273002. https://doi.org/10.1088/1361-648x/aa680e
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G. Petretto, S. Dwaraknath, H. P.C. Miranda, D. Winston, M. Giantomassi, M. J. van Setten, X. Gonze, K. A. Persson, G. Hautier, G.-M. Rignanese, Sci Data
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Seyed Mohamad Moosavi, Aditya Nandy, Kevin Maik Jablonka, Daniele Ongari, Jon Paul Janet, Peter G Boyd, Yongjin Lee, Berend Smit, and Heather J Kulik. Understanding the diversity of the metal-organic framework ecosystem. Nature communications
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H. S. Gökçe, M. Tuyan, K. Ramyar and M. L. Nehdi, ”Development of Eco-Efficient Fly Ash–Based Alkali-Activated and Geopolymer Composites with Reduced Alkaline Activator Dosage,” Journal of Materials in Civil Engineering, vol. 32, no. 2, pp. 04019350; DOI: 10.1061/(ASCE)MT.1943-5533.0003017 , 2020
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Maryum Ali, Erum Pervaiz, Tayyaba Noor, Osama Rabi, Rubab Zahra, and Minghui Yang. Recent advancements in mof-based catalysts for applications in electrochemical and photoelectrochemical water splitting: A review. International Journal of Energy Research
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Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning
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Andrew S. Rosen, Shaelyn M. Iyer, Debmalya Ray, Zhenpeng Yao, Alan Aspuru-Guzik, Laura Gagliardi, Justin M. Notestein, and Randall Q. Snurr. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter
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Madsen, Jacob, and Toma Susi. ”The abTEM code: transmission electron microscopy from first principles.” Open Research Europe 1 (2021)
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Liu, Hanmeng, Leyang Cui, Jian Liu, and Yue Zhang. ”Natural language inference in context-investigating contextual reasoning over long texts.” In Proceedings of the AAAI conference on artificial intelligence
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Marques, F., Balcerzak, M., Winkelmann, F., Zepon, G., Felderhoff, M. (2021). Review and outlook on high-entropy alloys for hydrogen storage. Energy & Environmental Science
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A. Trewartha, et al., ’Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science’, Patterns, vol. 3, no. 8, 2022
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V. Gupta, et al., MatSciBERT: A materials domain language model for text mining and information extraction
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Hargreaves et al., A Database of Experimentally Measured Lithium Solid Electrolyte Conductivities Evaluated with Machine Learning
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Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., … & Kozinsky, B. (2022). E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature Communications, 13(1), 2453
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Gupta, A. K., & Raghavachari, K. (2022). Three-dimensional convolutional neural networks utilizing molecular topological features for accurate atomization energy predictions. Journal of Chemical Theory and Computation, 18(4), 2132-2143
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Bowman, J. M., Qu, C., Conte, R., Nandi, A., Houston, P. L., & Yu, Q. (2022). The MD17 datasets from the perspective of datasets for gas-phase “small” molecule potentials. The Journal of chemical physics, 156(24)
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Lirong Li, Han Sol Jung, Jae Won Lee, and Yong Tae Kang. Review on applications of metal–organic frameworks for co2 capture and the performance enhancement mechanisms. Renewable and Sustainable Energy Reviews
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Ryan Greene, Ted Sanders, Lilian Weng, and Arvind Neelakantan. New and improved embedding model, 2022
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Li, Y.; Peng, L.; Fu, J.; Dai, X.; Wang, G. A Microscopic Survey on Microplastics in Beverages: The Case of Beer, Mineral Water and Tea. Analyst 2022, 147 (6), 1099–1105. https://doi.org/10.1039/D2AN00083K
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Ross Irwin, Spyridon Dimitriadis, Jiazhen He, and Esben Jannik Bjerrum. Chemformer: a pre-trained transformer for computational chemistry. Machine Learning: Science and Technology, 3(1):015022, January 2022. doi: 10.1088/2632-2153/ac3ffb. URL https://dx.doi.org/10.1088/2632-2153/ac3ffb
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2041
Closest in time.
Hafner, Jürgen, ”Ab‐initio simulations of materials using VASP: Density‐functional theory and beyond.” Journal of computational chemistry 29.13 (2008): 2044-2078
2078
Closest in time.