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Large Language Models (LLMs) excel in diverse areas, yet struggle with complex scientific reasoning, especially in the field of chemistry.
Quantum chemistry
McQuarrie, D. A · 2008
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Pearson prentice-hall; upper saddle river, nj
Hair, J., Black, W., Babin, B., Anderson, R., and Tatham, R · 2009
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Physical chemistry: quanta, matter, and change
Atkins, P., De Paula, J., and Friedman, R · 2014
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Students’ understanding of chemical formulae: A review of empirical research
Taskin, V. and Bernholt, S · 2014
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Potentialnet for molecular property prediction
Feinberg, E. N., Sur, D., Wu, Z., Husic, B. E., Mai, H., Li, Y., Sun, S., Yang, J., Ramsundar, B., and Pande, V. S · 2018
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Language models as knowledge bases?
Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., and Miller, A · 2019
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Analyzing learned molecular representations for property prediction
Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al · 2019
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Exploring chemical space using natural language processing methodologies for drug discovery
Öztürk, H., Özgür, A., Schwaller, P., Laino, T., and Ozkirimli, E · 2020
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Artificial intelligence in chemistry: current trends and future directions
Baum, Z. J., Yu, X., Ayala, P. Y., Zhao, Y., Watkins, S. P., and Zhou, Q · 2021
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Text2Mol: Cross-modal molecule retrieval with natural language queries
Edwards, C., Zhai, C., and Ji, H · 2021
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Translation between molecules and natural language
Edwards, C., Lai, T., Ros, K., Honke, G., Cho, K., and Ji, H · 2022
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To read is the challenge - insights from 100 days, 100 papers reading challenge in chemistry education research
Graulich, N., Rost, M., Schultz, M., and Gallardo-Williams, M · 2022
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LoRA: Low-rank adaptation of large language models
Hu, E. J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
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Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
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Draw me a flower: Processing and grounding abstraction in natural language
Lachmy, R., Pyatkin, V., Manevich, A., and Tsarfaty, R · 2022
Cited alongside, same era.
Learn to explain: Multimodal reasoning via thought chains for science question answering
Lu, P., Mishra, S., Xia, T., Qiu, L., Chang, K.-W., Zhu, S.-C., Tafjord, O., Clark, P., and Kalyan, A · 2022
Cited alongside, same era.
Is a question decomposition unit all we need?
Patel, P., Mishra, S., Parmar, M., and Baral, C · 2022
Cited alongside, same era.
Iteratively prompt pre-trained language models for chain of thought
Wang, B., Deng, X., and Sun, H · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D · 2022
Cited alongside, same era.
Decomposed prompting: A modular approach for solving complex tasks
Khot, T., Trivedi, H., Finlayson, M., Fu, Y., Richardson, K., Clark, P., and Sabharwal, A · 2023
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Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., Welleck, S., Majumder, B. P., Gupta, S., Yazdanbakhsh, A., and Clark, P · 2023
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Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al · 2023
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Gpt-4 doesn’t know it’s wrong: An analysis of iterative prompting for reasoning problems
Stechly, K., Marquez, M., and Kambhampati, S · 2023
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Recitation-augmented language models
Sun, Z., Wang, X., Tay, Y., Yang, Y., and Zhou, D · 2023
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Weng, Y., Zhu, M., He, S., Liu, K., and Zhao, J · 2022
Cited alongside, same era.
Atkins’ physical chemistry
Atkins, P., De Paula, J., and Keeler, J · 2023
Cited alongside, same era.
Graph of Thoughts: Solving Elaborate Problems with Large Language Models, 2023
Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Gianinazzi, L., Gajda, J., Lehmann, T., Podstawski, M., Niewiadomski, H., Nyczyk, P., and Hoefler, T · 2023
Cited alongside, same era.
Chemcrow: Augmenting large-language models with chemistry tools
Bran, A. M., Cox, S., White, A. D., and Schwaller, P · 2023
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P · 2023
Cited alongside, same era.
Synergpt: In-context learning for personalized drug synergy prediction and drug design
Edwards, C. N., Naik, A., Khot, T., Burke, M. D., Ji, H., and Hope, T · 2023
Cited alongside, same era.
Mol-instructions: A large-scale biomolecular instruction dataset for large language models
Fang, Y., Liang, X., Zhang, N., Liu, K., Huang, R., Chen, Z., Fan, X., and Chen, H · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Tree of Thoughts: Deliberate problem solving with large language models, 2023
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K · 2023
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Automatic chain of thought prompting in large language models
Zhang, Z., Zhang, A., Li, M., and Smola, A · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., Zhang, H., Gonzalez, J. E., and Stoica, I · 2023
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ReactIE: Enhancing chemical reaction extraction with weak supervision
Zhong, M., Ouyang, S., Jiang, M., Hu, V., Jiao, Y., Wang, X., and Han, J · 2023
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Least-to-most prompting enables complex reasoning in large language models
Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q. V., and Chi, E. H · 2023
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From words to molecules: A survey of large language models in chemistry
Liao, C., Yu, Y., Mei, Y., and Wei, Y · 2024
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Prioritizing safeguarding over autonomy: Risks of llm agents for science
Tang, X., Jin, Q. J., Zhu, K., Yuan, T., Zhang, Y., Zhou, W., Qu, M., Zhao, Y., Tang, J., Zhang, Z., Cohan, A., Lu, Z., and Gerstein, M · 2024
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