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Embracing data science in catalysis research

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  • Vogt, C. & Weckhuysen, B. M. The concept of active site in heterogeneous catalysis. Nat. Rev. Chem. 6, 89–111 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Ye, R., Zhao, J., Wickemeyer, B. B., Toste, F. D. & Somorjai, G. A. Foundations and strategies of the construction of hybrid catalysts for optimized performances. Nat. Catal. 1, 318–325 (2018).

    Article 

    Google Scholar 

  • Copéret, C., Chabanas, M., Petroff Saint-Arroman, R. & Basset, J. M. Homogeneous and heterogeneous catalysis: bridging the gap through surface organometallic chemistry. Angew. Chem. Int. Ed. 42, 156–181 (2003).

    Article 

    Google Scholar 

  • Ye, R., Hurlburt, T. J., Sabyrov, K., Alayoglu, S. & Somorjai, G. A. Molecular catalysis science: perspective on unifying the fields of catalysis. Proc. Natl Acad. Sci. USA 113, 5159–5166 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhao, B., Han, Z. & Ding, K. The N-H functional group in organometallic catalysis. Angew. Chem. Int. Ed. 52, 4744–4788 (2013).

    Article 
    CAS 

    Google Scholar 

  • Sheldon, R. A. & Woodley, J. M. Role of biocatalysis in sustainable chemistry. Chem. Rev. 118, 801–838 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Munnik, P., de Jongh, P. E. & de Jong, K. P. Recent developments in the synthesis of supported catalysts. Chem. Rev. 115, 6687–6718 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Bornscheuer, U. T. et al. Engineering the third wave of biocatalysis. Nature 485, 185–194 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Grunwaldt, J.-D. & Schroer, C. G. Hard and soft X-ray microscopy and tomography in catalysis: bridging the different time and length scales. Chem. Soc. Rev. 39, 4741–4753 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Meirer, F. & Weckhuysen, B. M. Spatial and temporal exploration of heterogeneous catalysts with synchrotron radiation. Nat. Rev. Mater. 3, 324–340 (2018).

    Article 

    Google Scholar 

  • Chen, B. W. J., Xu, L. & Mavrikakis, M. Computational methods in heterogeneous catalysis. Chem. Rev. 121, 1007–1048 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Durand, D. J. & Fey, N. Computational ligand descriptors for catalyst design. Chem. Rev. 119, 6561–6594 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Kitchin, J. R. Machine learning in catalysis. Nat. Catal. 1, 230–232 (2018).

    Article 

    Google Scholar 

  • Toyao, T. et al. Machine learning for catalysis informatics: recent applications and prospects. ACS Catal. 10, 2260–2297 (2020).

    Article 
    CAS 

    Google Scholar 

  • Ma, X., Li, Z., Achenie, L. E. K. & Xin, H. Machine-learning-augmented chemisorption model for CO2 electroreduction catalyst screening. J. Phys. Chem. Lett. 6, 3528–3533 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D. & Doyle, A. G. Predicting reaction performance in C-N cross-coupling using machine learning. Science 360, 186–190 (2018). The application of interpretable machine learning on a high-throughput Buchwald–Hartwig dataset to predict high-performing palladium catalysts and unravel their inhibition mechanism.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Kim, M. et al. Searching for an optimal multi-metallic alloy catalyst by active learning combined with experiments. Adv. Mater. 34, 2108900 (2022).

    Article 
    CAS 

    Google Scholar 

  • Shields, B. J. et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 590, 89–96 (2021). Development of Bayesian optimization on palladium-catalysed Mitsunobu and deoxyfluorination reactions where the algorithm consistently outperformed human decision-making in terms number of experiments and actual yields to optimize the process.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Wu, Z., Kan, S. B. J., Lewis, R. D., Wittmann, B. J. & Arnold, F. H. Machine learning-assisted directed protein evolution with combinatorial libraries. Proc. Natl Acad. Sci. USA 116, 8852–8858 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lu, H. et al. Machine learning-aided engineering of hydrolases for PET depolymerization. Nature 604, 662–667 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ulissi, Z. W., Medford, A. J., Bligaard, T. & Nørskov, J. K. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat. Commun. 8, 14621 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Li, F. et al. Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction. Nat. Catal. 5, 662–672 (2022). A deep learning methodology to predict enzyme turnover numbers of metabolic enzymes from any organism merely from substrate structures and protein sequences.

    Article 
    CAS 

    Google Scholar 

  • Holeňa, M. & Baerns, M. Feedforward neural networks in catalysis: a tool for the approximation of the dependency of yield on catalyst composition and for knowledge extraction. Catal. Today 81, 485–494 (2003). Amongst the earliest reports on applied machine learning in catalysis, wherein a feedforward neural network was used to predict propene yield based on the catalyst composition.

    Article 

    Google Scholar 

  • Baumes, L., Farrusseng, D., Lengliz, M. & Mirodatos, C. Using artificial neural networks to boost high-throughput discovery in heterogeneous catalysis. QSAR Comb. Sci. 23, 767–778 (2004).

    Article 
    CAS 

    Google Scholar 

  • Burello, E., Farrusseng, D. & Rothenberg, G. Combinatorial explosion in homogeneous catalysis: screening 60,000 cross-coupling reactions. Adv. Synth. Catal. 346, 1844–1853 (2004).

    Article 
    CAS 

    Google Scholar 

  • Corma, A. et al. Optimisation of olefin epoxidation catalysts with the application of high-throughput and genetic algorithms assisted by artificial neural networks (softcomputing techniques). J. Catal. 229, 513–524 (2005).

    Article 
    CAS 

    Google Scholar 

  • Venkatasubramanian, V. The promise of artificial intelligence in chemical engineering: is it here, finally? AIChE J. 65, 466–478 (2019).

    Article 
    CAS 

    Google Scholar 

  • Pyzer-Knapp, E. O. et al. Accelerating materials discovery using artificial intelligence, high performance computing and robotics. NPJ Comput. Mater. 8, 84 (2022).

    Article 

    Google Scholar 

  • Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  • RDKit; https://www.rdkit.org/

  • Chanussot, L. et al. Open catalyst 2020 (OC20) dataset and community challenges. ACS Catal. 11, 6059–6072 (2021). The most extensive database consisting of close to 1.3 million density DFT relaxations across a wide swath of materials, surfaces and adsorbates (nitrogen, carbon and oxygen chemistries) for application in heterogeneous catalysis.

    Article 
    CAS 

    Google Scholar 

  • Kearnes, S. M. et al. The open reaction database. J. Am. Chem. Soc. 143, 18820–18826 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Yano, J. et al. The case for data science in experimental chemistry: examples and recommendations. Nat. Rev. Chem. 6, 357–370 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Schlexer Lamoureux, P. et al. Machine learning for computational heterogeneous catalysis. ChemCatChem 11, 3581–3601 (2019).

    Article 
    CAS 

    Google Scholar 

  • Medford, A. J., Kunz, M. R., Ewing, S. M., Borders, T. & Fushimi, R. Extracting knowledge from data through catalysis informatics. ACS Catal. 8, 7403–7429 (2018).

    Article 
    CAS 

    Google Scholar 

  • Maldonado, A. G. & Rothenberg, G. Predictive modeling in homogeneous catalysis: a tutorial. Chem. Soc. Rev. 39, 1891–1902 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Mazurenko, S., Prokop, Z. & Damborsky, J. Machine learning in enzyme engineering. ACS Catal. 10, 1210–1223 (2020).

    Article 
    CAS 

    Google Scholar 

  • Suvarna, M. & Pérez-Ramírez, J. Dataset: Embracing Data Science in Catalysis Research (Zenodo, 2024); https://doi.org/10.5281/zenodo.10640876

  • Zahrt, A. F. et al. Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science 363, eaau5631 (2019). The study models multiple conformations of more than 800 prospective catalysts for the coupling reaction of imines and thiols, and trained machine learning algorithms on a subset of experimental results, to achieve highly accurate predictions of enantioselectivities.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nguyen, T. N. et al. High-throughput experimentation and catalyst informatics for oxidative coupling of methane. ACS Catal. 10, 921–932 (2020).

    Article 
    CAS 

    Google Scholar 

  • Tran, K. & Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat. Catal. 1, 696–703 (2018). A fully automated screening method developed by integrating machine learning and optimization algorithms to guide DFT calculations, for in silico prediction of electrocatalyst performance for CO2 reduction and H2 evolution.

    Article 
    CAS 

    Google Scholar 

  • Wang, G. et al. Accelerated discovery of multi-elemental reverse water–gas shift catalysts using extrapolative machine learning approach. Nat. Commun. 14, 5861 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Amar, Y., Schweidtmann, A. M., Deutsch, P., Cao, L. & Lapkin, A. Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis. Chem. Sci. 10, 6697–6706 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rinehart, N. I. et al. A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C-N couplings. Science 381, 965–972 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Schweidtmann, A. M. et al. Machine learning meets continuous flow chemistry: automated optimization towards the pareto front of multiple objectives. Chem. Eng. J. 352, 277–282 (2018).

    Article 
    CAS 

    Google Scholar 

  • O’Connor, N. J., Jonayat, A. S. M., Janik, M. J. & Senftle, T. P. Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning. Nat. Catal. 1, 531–539 (2018).

    Article 

    Google Scholar 

  • Foppa, L. et al. Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence. MRS Bull. 46, 1016–1026 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhao, S. et al. Enantiodivergent Pd-catalyzed C-C bond formation enabled through ligand parameterization. Science 362, 670–674 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Timoshenko, J., Lu, D., Lin, Y. & Frenkel, A. I. Supervised machine-learning-based determination of three-dimensional structure of metallic nanoparticles. J. Phys. Chem. Lett. 8, 5091–5098 (2017). Application of deep learning to solve metal catalyst from XANES, broadly applicable to the determination of nanoparticle structures in operando studies and generalizable to other nanoscale systems.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Zheng, C. et al. Automated generation and ensemble-learned matching of X-ray absorption spectra. NPJ Comput. Mater. 4, 12 (2018).

    Article 

    Google Scholar 

  • Mitchell, S. et al. Automated image analysis for single-atom detection in catalytic materials by transmission electron microscopy. J. Am. Chem. Soc. 144, 8018–8029 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Büchler, J. et al. Algorithm-aided engineering of aliphatic halogenase WelO5* for the asymmetric late-stage functionalization of soraphens. Nat. Commun. 13, 371 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wulf, C. et al. A unified research data infrastructure for catalysis research – challenges and concepts. ChemCatChem 13, 3223–3236 (2021).

    Article 
    CAS 

    Google Scholar 

  • Mendes, P. S. F., Siradze, S., Pirro, L. & Thybaut, J. W. Open data in catalysis: from today’s big picture to the future of small data. ChemCatChem 13, 836–850 (2021).

    Article 
    CAS 

    Google Scholar 

  • Marshall, C. P., Schumann, J. & Trunschke, A. Achieving digital catalysis: strategies for data acquisition, storage and use. Angew. Chem. Int. Ed. 62, e202302971 (2023).

    Article 
    CAS 

    Google Scholar 

  • Zavyalova, U., Holena, M., Schlögl, R. & Baerns, M. Statistical analysis of past catalytic data on oxidative methane coupling for new insights into the composition of high-performance catalysts. ChemCatChem 3, 1935–1947 (2011).

    Article 
    CAS 

    Google Scholar 

  • Odabasi, C., Gunay, M. E. & Yildrim, R. Knowledge extraction for water gas shift reaction over noble metal catalysts from publications in the literature between 2002 and 2012. Int. J. Hydrog. Energy 39, 5733–5746 (2014).

    Article 
    CAS 

    Google Scholar 

  • Suvarna, M., Araújo, T. P. & Pérez-Ramírez, J. A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation. Appl. Catal. B Environ. 315, 121530 (2022).

    Article 
    CAS 

    Google Scholar 

  • Mamun, O., Winther, K. T., Boes, J. R. & Bligaard, T. High-throughput calculations of catalytic properties of bimetallic alloy surfaces. Sci. Data 6, 76 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jinnouchi, R. & Asahi, R. Predicting catalytic activity of nanoparticles by a DFT-aided machine-learning algorithm. J. Phys. Chem. Lett. 8, 4279–4283 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Berman, H. M. et al. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mitchell, A. L. et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 47, D351–D360 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).

    Article 

    Google Scholar 

  • Schomburg, I., Chang, A. & Schomburg, D. BRENDA, enzyme data and metabolic information. Nucleic Acids Res. 30, 47–49 (2002).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nagano, N. EzCatDB: the enzyme catalytic-mechanism database. Nucleic Acids Res. 33, D407–D412 (2005).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Finnigan, W. et al. RetroBioCat as a computer-aided synthesis planning tool for biocatalytic reactions and cascades. Nat. Catal. 4, 98–104 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Winther, K. T. et al. Catalysis-Hub.org, an open electronic structure database for surface reactions. Sci. Data 6, 75 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Álvarez-Moreno, M. et al. Managing the computational chemistry big data problem: the ioChem-BD platform. J. Chem. Inf. Model. 55, 95–103 (2015).

    Article 
    PubMed 

    Google Scholar 

  • Gensch, T. et al. A comprehensive discovery platform for organophosphorus ligands for catalysis. J. Am. Chem. Soc. 144, 1205–1217 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Bengio, Y., Courville, A. & Vincent, P. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013).

    Article 
    PubMed 

    Google Scholar 

  • Schmidt, J., Marques, M. R. G., Botti, S. & Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. NPJ Comput. Mater. 5, 83 (2019).

    Article 

    Google Scholar 

  • Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360–365 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Mitchell, J. B. O. Machine learning methods in chemoinformatics. WIREs Comput. Mol. Sci. 4, 468–481 (2014).

    Article 
    CAS 

    Google Scholar 

  • Wigh, D. S., Goodman, J. M. & Lapkin, A. A. A review of molecular representation in the age of machine learning. WIREs Comput. Mol. Sci. 12, e1603 (2022).

    Article 

    Google Scholar 

  • Krenn, M., Häse, F., Nigam, A., Friederich, P. & Aspuru-Guzik, A. Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation. Mach. Learn. Sci. Technol. 1, 045024 (2020).

    Article 

    Google Scholar 

  • Kononova, O. et al. Text-mined dataset of inorganic materials synthesis recipes. Sci. Data 6, 203 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Olivetti, E. A. et al. Data-driven materials research enabled by natural language processing and information extraction. Appl. Phys. Rev. 7, 041317 (2020).

    Article 
    CAS 

    Google Scholar 

  • Kim, E. et al. Materials synthesis insights from scientific literature via text extraction and machine learning. Chem. Mater. 29, 9436–9444 (2017).

    Article 
    CAS 

    Google Scholar 

  • Jensen, Z. et al. A machine learning approach to zeolite synthesis enabled by automatic literature data extraction. ACS Cent. Sci. 5, 892–899 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Luo, Y. et al. MOF synthesis prediction enabled by automatic data mining and machine learning. Angew. Chem. Int. Ed. 61, e202200242 (2022).

    Article 
    CAS 

    Google Scholar 

  • Zheng, Z., Zhang, O., Borgs, C., Chayes, J. T. & Yaghi, O. M. ChatGPT chemistry assistant for text mining and the prediction of MOF synthesis. J. Am. Chem. Soc. 145, 18048–18062 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Suvarna, M., Vaucher, A. C., Mitchell, S., Laino, T. & Pérez-Ramírez, J. Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis. Nat. Commun. 14, 7964 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lai, N. S. et al. Artificial intelligence (AI) workflow for catalyst design and optimization. Ind. Eng. Chem. Res. 62, 17835–17848 (2023).

    Article 

    Google Scholar 

  • Probst, D. et al. Biocatalysed synthesis planning using data-driven learning. Nat. Commun. 13, 964 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Moon, J. et al. Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis. Nat. Mater. 23, 108–115 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Zhong, M. et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 581, 178–183 (2020). Discovery of Cu-Al electrocatalysts, though DFT aided machine learning, to efficiently reduce CO2 to ethylene with a Faradaic efficiency of 80%.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Torres, J. A. G. et al. A multi-objective active learning platform and web app for reaction optimization. J. Am. Chem. Soc. 144, 19999–20007 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Greenhalgh, J. C., Fahlberg, S. A., Pfleger, B. F. & Romero, P. A. Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production. Nat. Commun. 12, 5825 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tallorin, L. et al. Discovering de novo peptide substrates for enzymes using machine learning. Nat. Commun. 9, 5253 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Schwaller, P. et al. Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction. ACS Cent. Sci. 5, 1572–1583 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Anstine, D. M. & Isayev, O. Generative models as an emerging paradigm in the chemical sciences. J. Am. Chem. Soc. 145, 8736–8750 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4, 268–276 (2018). A method to convert discrete representations of molecules into multidimensional continuous representations for generating compounds in silico.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Repecka, D. et al. Expanding functional protein sequence spaces using generative adversarial networks. Nat. Mach. Intell. 3, 324–333 (2021).

    Article 

    Google Scholar 

  • Hawkins-Hooker, A. et al. Generating functional protein variants with variational autoencoders. PLoS Comput. Biol. 17, e1008736 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Johnson, S. R. et al. Computational scoring and experimental evaluation of enzymes generated by neural networks. Preprint at https://www.biorxiv.org/content/10.1101/2023.03.04.531015v1 (2023).

  • Schilter, O., Vaucher, A., Schwaller, P. & Laino, T. Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions. Digit. Discov. 2, 728–735 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kreutter, D., Schwaller, P. & Reymond, J.-L. Predicting enzymatic reactions with a molecular transformer. Chem. Sci. 12, 8648–8659 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Popova, M., Isayev, O. & Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv. 4, eaap7885 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhou, Z., Li, X. & Zare, R. N. Optimizing chemical reactions with deep reinforcement learning. ACS Cent. Sci. 3, 1337–1344 (2017). A fully automated deep reinforcement learning to optimize chemical reactions where the model iteratively records the results of a chemical reaction and chooses new experimental conditions to improve the reaction outcome.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lan, T. & An, Q. Discovering catalytic reaction networks using deep reinforcement learning from first-principles. J. Am. Chem. Soc. 143, 16804–16812 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Song, Z. et al. Adaptive design of alloys for CO2 activation and methanation via reinforcement learning Monte Carlo tree search algorithm. J. Phys. Chem. Lett. 14, 3594–3601 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Suvarna, M., Preikschas, P. & Pérez-Ramírez, J. Identifying descriptors for promoted rhodium-based catalysts for higher alcohol synthesis via machine learning. ACS Catal. 12, 15373–15385 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Smith, A., Keane, A., Dumesic, J. A., Huber, G. W. & Zavala, V. M. A machine learning framework for the analysis and prediction of catalytic activity from experimental data. Appl. Catal. B Environ. 263, 118257 (2020).

    Article 
    CAS 

    Google Scholar 

  • Vellayappan, K. et al. Impacts of catalyst and process parameters on Ni-catalyzed methane dry reforming via interpretable machine learning. Appl. Catal. B Environ. 330, 122593 (2023).

    Article 
    CAS 

    Google Scholar 

  • Roh, J. et al. Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane. Appl. Catal. B Environ. 343, 123454 (2024).

    Article 
    CAS 

    Google Scholar 

  • McCullough, K., Williams, T., Mingle, K., Jamshidi, P. & Lauterbach, J. High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery. Phys. Chem. Chem. Phys. 22, 11174–11196 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Suzuki, K. et al. Statistical analysis and discovery of heterogeneous catalysts based on machine learning from diverse published data. ChemCatChem 11, 4537–4547 (2019).

    Article 
    CAS 

    Google Scholar 

  • Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Oviedo, F., Ferres, J. L., Buonassisi, T. & Butler, K. T. Interpretable and explainable machine learning for materials science and chemistry. Acc. Mater. Res. 3, 597–607 (2022).

    Article 
    CAS 

    Google Scholar 

  • Esterhuizen, J. A., Goldsmith, B. R. & Linic, S. Interpretable machine learning for knowledge generation in heterogeneous catalysis. Nat. Catal. 5, 175–184 (2022).

    Article 

    Google Scholar 

  • Wu, K. & Doyle, A. G. Parameterization of phosphine ligands demonstrates enhancement of nickel catalysis via remote steric effects. Nat. Chem. 9, 779–784 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Weng, B. et al. Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts. Nat. Commun. 11, 3513 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M. & Ghiringhelli, L. M. SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys. Rev. Mater. 2, 083802 (2018).

    Article 
    CAS 

    Google Scholar 

  • Foppa, L. et al. Data-centric heterogeneous catalysis: identifying rules and materials genes of alkane selective oxidation. J. Am. Chem. Soc. 145, 3427–3442 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Li, Z., Ma, X. & Xin, H. Feature engineering of machine-learning chemisorption models for catalyst design. Catal. Today 280, 232–238 (2017).

    Article 
    CAS 

    Google Scholar 

  • Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Timoshenko, J. et al. Linking the evolution of catalytic properties and structural changes in copper-zinc nanocatalysts using operando EXAFS and neural-networks. Chem. Sci. 11, 3727–3736 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Scheffler, M. et al. FAIR data enabling new horizons for materials research. Nature 604, 635–642 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Abolhasani, M. & Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nat. Synth. 2, 483–492 (2023). A review of self-driving labs through the integration of machine learning, lab automation and robotics to accelerate digital data curation and enable data-driven discoveries in chemical sciences.

    Article 

    Google Scholar 

  • MacLeod, B. P. et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci. Adv. 6, eaaz8867 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

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