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Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
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SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules
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An introduction to kernel and nearest-neighbor nonparametric regression
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Ridge regression learning algorithm in dual variables
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ESOL: estimating aqueous solubility directly from molecular structure
John S Delaney · 2004
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Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher K I Williams · 2005
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Extractive summarization of meeting recordings
Gabriel Murray, Steve Renals, and Jean Carletta · 2005
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Probabilistic latent maximal marginal relevance
Shengbo Guo and Scott Sanner · 2010
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Review of pt-based bimetallic catalysis: From model surfaces to supported catalysts
Weiting Yu, Marc D Porosoff, and Jingguang G Chen · 2012
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The Materials Project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, and Kristin a. Persson · 2013
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Quantifying mental health signals in twitter
Glen Coppersmith, Mark Dredze, and Craig Harman · 2014
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The materials project: A materials genome approach to accelerating materials innovation
Materials Project · 2014
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Dual function materials for CO2 capture and conversion using renewable H2
Melis S Duyar, Martha A Arellano Treviño, and Robert J Farrauto · 2015
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Understanding kernel ridge regression: Common behaviors from simple functions to density functionals
Kevin Vu, John Snyder, Li Li, Matthias Rupp, Brandon F Chen, Tarek Khelif, Klaus-Robert Müller, and Kieron Burke · 2015
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Statistical inference and adaptive design for materials discovery
Turab Lookman, Prasanna V Balachandran, Dezhen Xue, John Hogden, and James Theiler · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Predictive materials design with high-throughput screening and online optimization
Jose Miguel Hernandez-Lobato, Daniel Reagen, Ryan P Adams, David Duvenaud, Zoubin Ghahramani, Matt J Kusner, Andreas Scherer, Edward Snelson, Jasper Snoek, Steven Swift, et al · 2017
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Machine-learned approximations to density functional theory hamiltonians
Ganesh Hegde and R Chris Bowen · 2017
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Oxidative coupling of methane using mg/ti-doped SiO 2
Rika Tri Yunarti, Sangseo Gu, Jae-Wook Choi, Jungho Jae, Dong Jin Suh, and Jeong-Myeong Ha · 2017
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
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Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
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An update on PUG-REST: RESTful interface for programmatic access to PubChem
Sunghwan Kim, Paul A Thiessen, Tiejun Cheng, Bo Yu, and Evan E Bolton · 2018
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Utilizing BERT for Aspect-Based sentiment analysis via constructing auxiliary sentence
Chi Sun, Luyao Huang, and Xipeng Qiu · 2019
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SMILES transformer: Pre-trained molecular fingerprint for low data drug discovery
Shion Honda, Shoi Shi, and Hiroki R Ueda · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
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Exploring chemical space using natural language processing methodologies for drug discovery
Hakime Öztürk, Arzucan Özgür, Philippe Schwaller, Teodoro Laino, and Elif Ozkirimli · 2020
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Language models are Few-Shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening
Natalie S Eyke, William H Green, and Klavs F Jensen · 2020
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Assessing the viability of K-Mo2C for reverse water–gas shift scale-up: molecular to laboratory to pilot scale
Mitchell Juneau, Madeline Vonglis, J Hartvigsen, L Frost, Dylan J Bayerl, M Dixit, Giannis Mpourmpakis, J R Morse, J Baldwin, H Willauer, and Marc D Porosoff · 2020
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High-Throughput experimentation and catalyst informatics for oxidative coupling of methane
Thanh Nhat Nguyen, Thuy Tran Phuong Nhat, Ken Takimoto, Ashutosh Thakur, Shun Nishimura, Junya Ohyama, Itsuki Miyazato, Lauren Takahashi, Jun Fujima, Keisuke Takahashi, and Toshiaki Taniike · 2020
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Diversity-guided multi-objective bayesian optimization with batch evaluations
Mina Konakovic-Lukovic, Yunsheng Tian, and W Matusik · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
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Mapping the space of chemical reactions using attention-based neural networks
Philippe Schwaller, Daniel Probst, Alain C Vaucher, Vishnu H Nair, David Kreutter, Teodoro Laino, and Jean-Louis Reymond · 2021
Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch · 2023
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High-throughput ab initio design of atomic interfaces using InterMatch
Eli Gerber, Steven B Torrisi, Sara Shabani, Eric Seewald, Jordan Pack, Jennifer E Hoffman, Cory R Dean, Abhay N Pasupathy, and Eun-Ah Kim · 2023
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Positional information matters for invariant in-context learning: A case study of simple function classes
Yongqiang Chen, Binghui Xie, Kaiwen Zhou, Bo Han, Yatao Bian, and James Cheng · 2023
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Molecular structure and catalytic promotional effect of mn on supported Na2WO4/SiO2 catalysts for oxidative coupling of methane (OCM) reaction
Sagar Sourav, Daniyal Kiani, Yixiao Wang, Jonas Baltrusaitis, Rebecca R Fushimi, and Israel E Wachs · 2023
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Initial sample selection in bayesian optimization for combinatorial optimization of chemical compounds
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AI-based language models powering drug discovery and development
Zhichao Liu, Ruth A Roberts, Madhu Lal-Nag, Xi Chen, Ruili Huang, and Weida Tong · 2021
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Benchmarking the performance of bayesian optimization across multiple experimental materials science domains
Qiaohao Liang, Aldair E. Gongora, Zekun Ren, Armi Tiihonen, Zhe Liu, Shijing Sun, James R. Deneault, Daniil Bash, Flore Mekki-Berrada, Saif A. Khan, Kedar Hippalgaonkar, Benji Maruyama, Keith A. Brown, John Fisher III, and Tonio Buonassisi · 2021
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Recent advances in carbon dioxide hydrogenation to produce olefins and aromatics
Dong Wang, Zhenhua Xie, Marc D Porosoff, and Jingguang G Chen · 2021
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Uncertainty toolbox: an Open-Source library for assessing, visualizing, and improving uncertainty quantification
Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, and Willie Neiswanger · 2021
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Machine intelligence for chemical reaction space
Philippe Schwaller, Alain C Vaucher, Ruben Laplaza, Charlotte Bunne, Andreas Krause, Clemence Corminboeuf, and Teodoro Laino · 2022
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Language models for the prediction of SARS-CoV-2 inhibitors
Andrew E Blanchard, John Gounley, Debsindhu Bhowmik, Mayanka Chandra Shekar, Isaac Lyngaas, Shang Gao, Junqi Yin, Aristeidis Tsaris, Feiyi Wang, and Jens Glaser · 2022
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TransPolymer: a transformer-based language model for polymer property predictions
Changwen Xu, Yuyang Wang, and Amir Barati Farimani · 2022
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Molformer: Large scale chemical language representations capture molecular structure and properties
Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, and Payel Das · 2022
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Toshiharu Morishita and Hiromasa Kaneko · 2023
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Optimizing classification of diseases through language model analysis of symptoms
Esraa Hassan, Tarek Abd El-Hafeez, and Mahmoud Y Shams · 2024
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A multimodal generative AI copilot for human pathology
Ming Y Lu, Bowen Chen, Drew F K Williamson, Richard J Chen, Melissa Zhao, Aaron K Chow, Kenji Ikemura, Ahrong Kim, Dimitra Pouli, Ankush Patel, Amr Soliman, Chengkuan Chen, Tong Ding, Judy J Wang, Georg Gerber, Ivy Liang, Long Phi Le, Anil V Parwani, Luca L Weishaupt, and Faisal Mahmood · 2024
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Medical language model specialized in extracting cardiac knowledge
Hansle Gwon, Jiahn Seo, Seohyun Park, Young-Hak Kim, and Tae Joon Jun · 2024
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LlaSMol: Advancing large language models for chemistry with a large-scale, comprehensive, high-quality instruction tuning dataset
Botao Yu, Frazier N Baker, Ziqi Chen, Xia Ning, and Huan Sun · 2024
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Machine learning interatomic potential with DFT accuracy for general grain boundaries in α \alpha -fe
Kazuma Ito, Tatsuya Yokoi, Katsutoshi Hyodo, and Hideki Mori · 2024
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Leveraging large language models for predictive chemistry
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, and Berend Smit · 2024
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LLMs are in-context bandit reinforcement learners
Giovanni Monea, Antoine Bosselut, Kianté Brantley, and Yoav Artzi · 2024
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Large language models to enhance bayesian optimization
Tennison Liu, Nicolás Astorga, Nabeel Seedat, and Mihaela van der Schaar · 2024
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Dual functional materials: At the interface of catalysis and separations
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Many-shot in-context learning for molecular inverse design
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Sequential closed-loop bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery
Xiaobo Li, Yu Che, Linjiang Chen, Tao Liu, Kewei Wang, Lunjie Liu, Haofan Yang, Edward O Pyzer-Knapp, and Andrew I Cooper · 2024
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Context versus prior knowledge in language models
Kevin Du, Vésteinn Snæbjarnarson, Niklas Stoehr, Jennifer C White, Aaron Schein, and Ryan Cotterell · 2024
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Revisiting in-context learning with long context language models
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Large language models must be taught to know what they don’t know
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Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models
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Machine learning hubbard parameters with equivariant neural networks
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