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.10640876Zahrt, 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
Source link