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Scientific discovery increasingly depends on efficient experimental optimization to navigate vast design spaces under time and resource constraints.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
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Kushner, H. J · 1964
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SMILES, a line notation and computerized interpreter for chemical structures (US Environmental Protection Agency, Environmental Research Laboratory, 1987)
Anderson, E., Veith, G. D. & Weininger, D · 1987
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SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
Weininger, D · 1988
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Smiles. 3. depict. graphical depiction of chemical structures
Weininger, D · 1990
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Efficient global optimization of expensive black-box functions
Jones, D. R., Schonlau, M. & Welch, W. J · 1998
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A taxonomy of global optimization methods based on response surfaces
Jones, D. R · 2001
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Using confidence bounds for exploitation-exploration trade-offs
Auer, P · 2002
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Why most published research findings are false
Ioannidis, J. P · 2005
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Gaussian processes for machine learning (MIT press Cambridge, MA, 2006)
Williams, C. K. & Rasmussen, C. E · 2006
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Gaussian process optimization in the bandit setting: No regret and experimental design
Srinivas, N., Krause, A., Kakade, S. M. & Seeger, M · 2009
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Extended-connectivity fingerprints
Rogers, D. & Hahn, M · 2010
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Scalable variational gaussian process classification
Hensman, J., Matthews, A. & Ghahramani, Z · 2015
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Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity
Schneider, N., Lowe, D. M., Sayle, R. A. & Landrum, G. A · 2015
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Molecular fingerprint similarity search in virtual screening
Cereto-Massagué, A. et al · 2015
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The harvard organic photovoltaic dataset
Lopez, S. A. et al · 2016
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Deep kernel learning
Wilson, A. G., Hu, Z., Salakhutdinov, R. & Xing, E. P · 2016
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Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V. & Riley, P · 2016
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Attention is all you need
Vaswani, A. et al · 2017
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Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A. & McAuliffe, J. D · 2017
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Decoupled weight decay regularization
Loshchilov, I. & Hutter, F · 2017
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Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A · 2018
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Planning chemical syntheses with deep neural networks and symbolic ai
Segler, M. H., Preuss, M. & Waller, M. P · 2018
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Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives
Schweidtmann, A. M. et al · 2018
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Active learning across intermetallics to guide discovery of electrocatalysts for co2 reduction and h2 evolution
Tran, K. & Ulissi, Z. W · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K · 2018
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Improving language understanding by generative pre-training (2018)
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I. et al · 2018
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Predicting reaction performance in C–N cross-coupling using machine learning
Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D. & Doyle, A. G · 2018
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A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow
Perera, D. et al · 2018
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A tutorial on bayesian optimization
Frazier, P. I · 2018
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Comment on “predicting reaction performance in C–N cross-coupling using machine learning”
Chuang, K. V. & Keiser, M. J · 2018
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Kudo, T. & Richardson, J · 2018
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Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration
Gardner, J. R., Pleiss, G., Bindel, D., Weinberger, K. Q. & Wilson, A. G · 2018
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Computational ligand descriptors for catalyst design
Durand, D. J. & Fey, N · 2019
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Parameter-efficient transfer learning for nlp
Houlsby, N. et al · 2019
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Language models are unsupervised multitask learners
Radford, A. et al · 2019
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GFN2-xTB—An accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions
Bannwarth, C., Ehlert, S. & Grimme, S · 2019
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PyTorch Lightning (2019)
Falcon, W. & The PyTorch Lightning team · 2019
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A deep learning approach to antibiotic discovery
Stokes, J. M. et al · 2020
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On faithfulness and factuality in abstractive summarization
Maynez, J., Narayan, S., Bohnet, B. & McDonald, R · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Bayesian optimization for chemical reactions
Guo, J., Ranković, B. & Schwaller, P · 2023
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Transfer learning for bayesian optimization: A survey
Bai, T. et al · 2023
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Peng, B. et al · 2023
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Chemcrow: Augmenting large-language models with chemistry tools
Bran, A. M., Cox, S., White, A. D. & Schwaller, P · 2023
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14 examples of how llms can transform materials science and chemistry: a reflection on a large language model hackathon
Jablonka, K. M. et al · 2023
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Raffel, C. et al · 2020
Cited alongside, same era.
Constrained bayesian optimization for automatic chemical design using variational autoencoders
Griffiths, R.-R. & Hernández-Lobato, J. M · 2020
Cited alongside, same era.
One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome
Capecchi, A., Probst, D. & Reymond, J.-L · 2020
Cited alongside, same era.
Experiment tracking with weights and biases (2020)
Biewald, L · 2020
Cited alongside, same era.
Uncertainty quantification in drug design
Mervin, L. H., Johansson, S., Semenova, E., Giblin, K. A. & Engkvist, O · 2021
Cited alongside, same era.
Uncertainty prediction for machine learning models of material properties
Tavazza, F., DeCost, B. & Choudhary, K · 2021
Cited alongside, same era.
Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation
Griffiths, R.-R., Aldrick, A. A., Garcia-Ortegon, M., Lalchand, V. et al · 2021
Cited alongside, same era.
Bayesian optimization of catalysts with in-context learning (2023)
Ramos, M. C., Michtavy, S. S., Porosoff, M. D. & White, A. D · 2023
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Bochemian: Large language model embeddings for bayesian optimization of chemical reactions
Ranković, B. & Schwaller, P · 2023
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Large language models as optimizers
Yang, C. et al · 2023
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Unifying molecular and textual representations via multi-task language modelling
Christofidellis, D. et al · 2023
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Angle-optimized text embeddings
Li, X. & Li, J · 2023
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Bai, J. et al · 2023
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Towards general text embeddings with multi-stage contrastive learning
Li, Z. et al · 2023
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Loftq: Lora-fine-tuning-aware quantization for large language models
Li, Y. et al · 2023
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Darwin series: Domain specific large language models for natural science
Xie, T. et al · 2023
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Bayesian low-rank adaptation for large language models
Yang, A. X., Robeyns, M., Wang, X. & Aitchison, L · 2023
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GAUCHE: A library for gaussian processes in chemistry
Griffiths, R.-R. et al · 2023
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Bayesian optimisation for additive screening and yield improvements–beyond one-hot encoding
Ranković, B., Griffiths, R.-R., Moss, H. B. & Schwaller, P · 2024
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A sober look at LLMs for material discovery: Are they actually good for Bayesian optimization over molecules?
Kristiadi, A. et al · 2024
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Predicting from strings: Language model embeddings for bayesian optimization (2024)
Nguyen, T. et al · 2024
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New embedding models and api updates (2024)
OpenAI · 2024
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Warner, B. et al · 2024
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Open source strikes bread - new fluffy embeddings model (2024)
Lee, S., Shakir, A., Koenig, D. & Lipp, J · 2024
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Grattafiori, A. et al · 2024
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Llm2vec: Large language models are secretly powerful text encoders
BehnamGhader, P. et al · 2024
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Leveraging large language models for predictive chemistry
Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A. & Smit, B · 2024
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Deep kernel learning for reaction outcome prediction and optimization
Singh, S. & Hernández-Lobato, J. M · 2024
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A holistic data-driven approach to synthesis predictions of colloidal nanocrystal shapes
Zaza, L., Rankovic, B., Schwaller, P. & Buonsanti, R · 2025
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A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
Huang, L. et al · 2025
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Automating the practice of science: Opportunities, challenges, and implications
Musslick, S. et al · 2025
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A review of large language models and autonomous agents in chemistry
Ramos, M. C., Collison, C. J. & White, A. D · 2025
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Exploring the role of large language models in the scientific method: from hypothesis to discovery
Zhang, Y. et al · 2025
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Language-based bayesian optimization research assistant (bora)
Cissé, A., Evangelopoulos, X., Gusev, V. V. & Cooper, A. I · 2025
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Pre-trained knowledge elevates large language models beyond traditional chemical reaction optimizers
MacKnight, R., Regio, J. E., Ethier, J. G., Baldwin, L. A. & Gomes, G · 2025
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Why language models hallucinate
Kalai, A. T., Nachum, O., Vempala, S. S. & Zhang, E · 2025
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Understanding high-dimensional bayesian optimization
Papenmeier, L., Poloczek, M. & Nardi, L · 2025
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Standard gaussian process is all you need for high-dimensional bayesian optimization
Xu, Z., Wang, H., Phillips, J. M. & Zhe, S · 2025
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