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Large Language Models have recently gained significant attention in scientific discovery for their extensive knowledge and advanced reasoning capabilities.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
Weininger, D · 1988
Earlier work this paper cites.
Application of a particle-in-cell method to solid mechanics
Sulsky, D., Zhou, S.-J., and Schreyer, H. L · 1995
Earlier work this paper cites.
Merck molecular force field. i. basis, form, scope, parameterization, and performance of mmff94
Halgren, T. A · 1996
Earlier work this paper cites.
The boosting approach to machine learning: An overview
Schapire, R. E · 2003
Earlier work this paper cites.
The logic of scientific discovery
Popper, K · 2005
Earlier work this paper cites.
The cma evolution strategy: a comparing review
Hansen, N · 2006
Earlier work this paper cites.
An overview of bilevel optimization
Colson, B., Marcotte, P., and Savard, G · 2007
Earlier work this paper cites.
Ffx: Fast, scalable, deterministic symbolic regression technology
McConaghy, T · 2011
Earlier work this paper cites.
Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Landrum, G. et al · 2013
Earlier work this paper cites.
Multiple regression genetic programming
Arnaldo, I., Krawiec, K., and O’Reilly, U.-M · 2014
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and Von Lilienfeld, O. A · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
Earlier work this paper cites.
Better informed distance geometry: using what we know to improve conformation generation
Riniker, S. and Landrum, G. A · 2015
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
Earlier work this paper cites.
The material point method for simulating continuum materials
Jiang, C., Schroeder, C., Teran, J., Stomakhin, A., and Selle, A · 2016
Earlier work this paper cites.
Lightgbm: A highly efficient gradient boosting decision tree
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y · 2017
Earlier work this paper cites.
Understanding and simplifying one-shot architecture search
Bender, G., Kindermans, P.-J., Zoph, B., Vasudevan, V., and Le, Q · 2018
Earlier work this paper cites.
Science of science
Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., Petersen, A. M., Radicchi, F., Sinatra, R., Uzzi, B., et al · 2018
Earlier work this paper cites.
Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
Earlier work this paper cites.
Automating drug discovery
Schneider, G · 2018
Earlier work this paper cites.
Population-based de novo molecule generation, using grammatical evolution
Yoshikawa, N., Terayama, K., Sumita, M., Homma, T., Oono, K., and Tsuda, K · 2018
Earlier work this paper cites.
ProxylessNAS: Direct neural architecture search on target task and hardware
Cai, H., Zhu, L., and Han, S · 2019
Earlier work this paper cites.
Learning concise representations for regression by evolving networks of trees
Cava, W. L., Singh, T. R., Taggart, J., Suri, S., and Moore, J · 2019
Earlier work this paper cites.
Jin, Y., Fu, W., Kang, J., Guo, J., and Guo, J · 2019
Earlier work this paper cites.
A probabilistic and multi-objective analysis of lexicase selection and ε \varepsilon -lexicase selection
La Cava, W., Helmuth, T., Spector, L., and Moore, J. H · 2019
Cited alongside, same era.
DARTS: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Cited alongside, same era.
Philosophy of science: A contemporary introduction
Rosenberg, A. and McIntyre, L · 2019
Cited alongside, same era.
Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression
Virgolin, M., Alderliesten, T., and Bosman, P. A · 2019
Cited alongside, same era.
Optimization of molecules via deep reinforcement learning
Warp: A high-performance python framework for gpu simulation and graphics, March 2022
Macklin, M · 2022
Later among the works it cites.
OpenAI: Introducing ChatGPT, 2022
OpenAI · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Later among the works it cites.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Later among the works it cites.
The impact of large language models on scientific discovery: a preliminary study using gpt-4
AI4Science, M. R. and Quantum, M. A · 2023
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Zhou, Z., Kearnes, S., Li, L., Zare, R. N., and Riley, P · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Chemberta: large-scale self-supervised pretraining for molecular property prediction
Chithrananda, S., Grand, G., and Ramsundar, B · 2020
Cited alongside, same era.
Parameter identification for symbolic regression using nonlinear least squares
Kommenda, M., Burlacu, B., Kronberger, G., and Affenzeller, M · 2020
Cited alongside, same era.
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Petersen, B. K., Larma, M. L., Mundhenk, T. N., Santiago, C. P., Kim, S. K., and Kim, J. T · 2020
Cited alongside, same era.
Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Udrescu, S.-M., Tan, A., Feng, J., Neto, O., Wu, T., and Tegmark, M · 2020
Cited alongside, same era.
A typology of scientific breakthroughs
Wuestman, M., Hoekman, J., and Frenken, K · 2020
Cited alongside, same era.
Boiko, D. A., MacKnight, R., Kline, B., and Gomes, G · 2023
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Benchmarking large language models as ai research agents
Huang, Q., Vora, J., Liang, P., and Leskovec, J · 2023
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Automated scientific discovery: From equation discovery to autonomous discovery systems
Kramer, S., Cerrato, M., Džeroski, S., and King, R · 2023
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Li, J., Liu, Y., Fan, W., Wei, X.-Y., Liu, H., Tang, J., and Li, Q · 2023
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Learning neural constitutive laws from motion observations for generalizable pde dynamics
Ma, P., Chen, P. Y., Deng, B., Tenenbaum, J. B., Du, T., Gan, C., and Matusik, W · 2023
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OpenAI: GPT-4, 2023
OpenAI · 2023
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Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J., Ellenberg, J. S., Wang, P., Fawzi, O., et al · 2023
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Chatgpt in drug discovery
Sharma, G. and Thakur, A · 2023
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Scientific discovery in the age of artificial intelligence
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al · 2023
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Efficiently programming large language models using sglang
Zheng, L., Yin, L., Xie, Z., Huang, J., Sun, C., Yu, C. H., Cao, S., Kozyrakis, C., Stoica, I., Gonzalez, J. E., et al · 2023
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Uni-mol: A universal 3d molecular representation learning framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., and Ke, G · 2023
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Introducing the next generation of claude, 2024
Anthropic · 2024
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Large language models as tool makers
Cai, T., Wang, X., Ma, T., Chen, X., and Zhou, D · 2024
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Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., Casas, D. d. l., Hanna, E. B., Bressand, F., et al · 2024
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Eureka: Human-level reward design via coding large language models
Ma, Y. J., Liang, W., Wang, G., Huang, D.-A., Bastani, O., Jayaraman, D., Zhu, Y., Fan, L., and Anandkumar, A · 2024
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Cognitive architectures for language agents
Sumers, T., Yao, S., Narasimhan, K., and Griffiths, T · 2024
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Solving olympiad geometry without human demonstrations
Trinh, T. H., Wu, Y., Le, Q. V., He, H., and Luong, T · 2024
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Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2024
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