{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T05:48:22Z","timestamp":1777441702254,"version":"3.51.4"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000006","name":"United States Department of Defense | United States Navy | Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-20-1-2150"],"award-info":[{"award-number":["N00014-20-1-2150"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"United States Department of Defense | United States Navy | Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-18-1-2434"],"award-info":[{"award-number":["N00014-18-1-2434"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"United States Department of Defense | United States Navy | Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-20-1-2150"],"award-info":[{"award-number":["N00014-20-1-2150"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"United States Department of Defense | United States Navy | Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-18-1-2434"],"award-info":[{"award-number":["N00014-18-1-2434"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OAC-1547580"],"award-info":[{"award-number":["OAC-1547580"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["#1122374"],"award-info":[{"award-number":["#1122374"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["#1745302"],"award-info":[{"award-number":["#1745302"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["Scientific Discovery through Advanced Computing (SciDAC) program"],"award-info":[{"award-number":["Scientific Discovery through Advanced Computing (SciDAC) program"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-NA0003965"],"award-info":[{"award-number":["DE-NA0003965"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["Scientific Discovery through Advanced Computing (SciDAC) program"],"award-info":[{"award-number":["Scientific Discovery through Advanced Computing (SciDAC) program"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-NA0003965"],"award-info":[{"award-number":["DE-NA0003965"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"DOI":"10.1038\/s43588-022-00384-0","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T17:04:23Z","timestamp":1671728663000},"page":"38-47","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A transferable recommender approach for selecting the best density functional approximations in chemical discovery"],"prefix":"10.1038","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2592-4237","authenticated-orcid":false,"given":"Chenru","family":"Duan","sequence":"first","affiliation":[]},{"given":"Aditya","family":"Nandy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2236-0261","authenticated-orcid":false,"given":"Ralf","family":"Meyer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9318-3595","authenticated-orcid":false,"given":"Naveen","family":"Arunachalam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9342-0191","authenticated-orcid":false,"given":"Heather J.","family":"Kulik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"384_CR1","doi-asserted-by":"publisher","first-page":"22858","DOI":"10.1002\/anie.201909987","volume":"59","author":"CW Coley","year":"2020","unstructured":"Coley, C. W., Eyke, N. S. & Jensen, K. F. Autonomous discovery in the chemical sciences part I: progress. Angew. Chem. Int. Ed. Engl. 59, 22858\u201322893 (2020).","journal-title":"Angew. Chem. Int. Ed. Engl."},{"key":"384_CR2","doi-asserted-by":"publisher","first-page":"011002","DOI":"10.1063\/1.4812323","volume":"1","author":"A Jain","year":"2013","unstructured":"Jain, A. et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).","journal-title":"APL Mater."},{"key":"384_CR3","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1038\/s41586-018-0337-2","volume":"559","author":"KT Butler","year":"2018","unstructured":"Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547\u2013555 (2018).","journal-title":"Nature"},{"key":"384_CR4","doi-asserted-by":"publisher","first-page":"045002","DOI":"10.1103\/RevModPhys.91.045002","volume":"91","author":"G Carleo","year":"2019","unstructured":"Carleo, G. et al. Machine learning and the physical sciences. Rev. Mod. Phys. 91, 045002 (2019).","journal-title":"Rev. Mod. Phys."},{"key":"384_CR5","doi-asserted-by":"publisher","first-page":"9927","DOI":"10.1021\/acs.chemrev.1c00347","volume":"121","author":"A Nandy","year":"2021","unstructured":"Nandy, A. et al. Computational discovery of transition-metal complexes: from high-throughput screening to machine learning. Chem. Rev. 121, 9927\u201310000 (2021).","journal-title":"Chem. Rev."},{"key":"384_CR6","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1021\/cr200107z","volume":"112","author":"AJ Cohen","year":"2012","unstructured":"Cohen, A. J., Mori-S\u00e1nchez, P. & Yang, W. Challenges for density functional theory. Chem. Rev. 112, 289\u2013320 (2012).","journal-title":"Chem. Rev."},{"key":"384_CR7","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.1080\/00268976.2017.1333644","volume":"115","author":"N Mardirossian","year":"2017","unstructured":"Mardirossian, N. & Head-Gordon, M. Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals. Mol. Phys. 115, 2315\u20132372 (2017).","journal-title":"Mol. Phys."},{"key":"384_CR8","doi-asserted-by":"publisher","first-page":"13021","DOI":"10.1039\/D1SC03701C","volume":"12","author":"C Duan","year":"2021","unstructured":"Duan, C., Chen, S., Taylor, M. G., Liu, F. & Kulik, H. J. Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles. Chem. Sci. 12, 13021\u201313036 (2021).","journal-title":"Chem. Sci."},{"key":"384_CR9","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1021\/jacs.0c09380","volume":"143","author":"M Loipersberger","year":"2021","unstructured":"Loipersberger, M., Cabral, D. G. A., Chu, D. B. K. & Head-Gordon, M. Mechanistic insights into Co and Fe quaterpyridine-based CO2 reduction catalysts: metal\u2013ligand orbital interaction as the key driving force for distinct pathways. J. Am. Chem. Soc. 143, 744\u2013763 (2021).","journal-title":"J. Am. Chem. Soc."},{"key":"384_CR10","doi-asserted-by":"publisher","first-page":"4416","DOI":"10.1021\/acs.jctc.0c00518","volume":"16","author":"DY Zhang","year":"2020","unstructured":"Zhang, D. Y. & Truhlar, D. G. Spin splitting energy of transition metals: a new, more affordable wave function benchmark method and its use to test density functional theory. J. Chem. Theory Comput. 16, 4416\u20134428 (2020).","journal-title":"J. Chem. Theory Comput."},{"key":"384_CR11","doi-asserted-by":"publisher","first-page":"143001","DOI":"10.1103\/PhysRevLett.120.143001","volume":"120","author":"L Zhang","year":"2018","unstructured":"Zhang, L., Han, J., Wang, H., Car, R. & E, W. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 120, 143001 (2018).","journal-title":"Phys. Rev. Lett."},{"key":"384_CR12","doi-asserted-by":"publisher","first-page":"3192","DOI":"10.1039\/C6SC05720A","volume":"8","author":"JS Smith","year":"2017","unstructured":"Smith, J. S., Isayev, O. & Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 8, 3192\u20133203 (2017).","journal-title":"Chem. Sci."},{"key":"384_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29939-5","volume":"13","author":"S Batzner","year":"2022","unstructured":"Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).","journal-title":"Nat. Commun."},{"key":"384_CR14","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-31093-x","volume":"13","author":"HE Sauceda","year":"2022","unstructured":"Sauceda, H. E. et al. BIGDML\u2014towards accurate quantum machine learning force fields for materials. Nat. Commun. 13, 3733 (2022).","journal-title":"Nat. Commun."},{"key":"384_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17265-7","volume":"11","author":"S Dick","year":"2020","unstructured":"Dick, S. & Fernandez-Serra, M. Machine learning accurate exchange and correlation functionals of the electronic density. Nat. Commun. 11, 3509 (2020).","journal-title":"Nat. Commun."},{"key":"384_CR16","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1126\/science.abj6511","volume":"374","author":"J Kirkpatrick","year":"2021","unstructured":"Kirkpatrick, J. et al. Pushing the frontiers of density functionals by solving the fractional electron problem. Science 374, 1385\u20131389 (2021).","journal-title":"Science"},{"key":"384_CR17","doi-asserted-by":"publisher","first-page":"036401","DOI":"10.1103\/PhysRevLett.126.036401","volume":"126","author":"L Li","year":"2021","unstructured":"Li, L. et al. Kohn-sham equations as regularizer: building prior knowledge into machine-learned physics. Phys. Rev. Lett. 126, 036401 (2021).","journal-title":"Phys. Rev. Lett."},{"key":"384_CR18","doi-asserted-by":"publisher","first-page":"eabq0279","DOI":"10.1126\/sciadv.abq0279","volume":"8","author":"H Ma","year":"2022","unstructured":"Ma, H., Narayanaswamy, A., Riley, P. & Li, L. Evolving symbolic density functionals. Sci. Adv. 8, eabq0279 (2022).","journal-title":"Sci. Adv."},{"key":"384_CR19","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1038\/s41557-020-0544-y","volume":"12","author":"J Hermann","year":"2020","unstructured":"Hermann, J., Sch\u00e4tzle, Z. & No\u00e9, F. Deep-neural-network solution of the electronic Schr\u00f6dinger equation. Nat. Chem. 12, 891\u2013897 (2020).","journal-title":"Nat. Chem."},{"key":"384_CR20","doi-asserted-by":"publisher","first-page":"109498","DOI":"10.1016\/j.commatsci.2019.109498","volume":"174","author":"SK Kauwe","year":"2020","unstructured":"Kauwe, S. K., Graser, J., Murdock, R. & Sparks, T. D. Can machine learning find extraordinary materials? Comput. Mater. Sci. 174, 109498 (2020).","journal-title":"Comput. Mater. Sci."},{"key":"384_CR21","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1021\/acs.jcim.7b00542","volume":"58","author":"S McAnanama-Brereton","year":"2018","unstructured":"McAnanama-Brereton, S. & Waller, M. P. Rational density functional selection using game theory. J. Chem. Inf. Model. 58, 61\u201367 (2018).","journal-title":"J. Chem. Inf. Model."},{"key":"384_CR22","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1021\/jp205710e","volume":"116","author":"W Jiang","year":"2012","unstructured":"Jiang, W., DeYonker, N. J., Determan, J. J. & Wilson, A. K. Toward accurate theoretical thermochemistry of first row transition metal complexes. J. Phys. Chem. A 116, 870\u2013885 (2012).","journal-title":"J. Phys. Chem. A"},{"key":"384_CR23","doi-asserted-by":"publisher","first-page":"A1133","DOI":"10.1103\/PhysRev.140.A1133","volume":"140","author":"W Kohn","year":"1965","unstructured":"Kohn, W. & Sham, L. J. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140, A1133\u2013A1138 (1965).","journal-title":"Phys. Rev."},{"key":"384_CR24","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-20471-y","volume":"12","author":"JT Margraf","year":"2021","unstructured":"Margraf, J. T. & Reuter, K. Pure non-local machine-learned density functional theory for electron correlation. Nat. Commun. 12, 344 (2021).","journal-title":"Nat. Commun."},{"key":"384_CR25","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1021\/acscentsci.8b00551","volume":"5","author":"A Grisafi","year":"2019","unstructured":"Grisafi, A. et al. Transferable machine-learning model of the electron density. ACS Cent. Sci. 5, 57\u201364 (2019).","journal-title":"ACS Cent. Sci."},{"key":"384_CR26","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1109\/TNNLS.2013.2292894","volume":"25","author":"B Fr\u00e9nay","year":"2013","unstructured":"Fr\u00e9nay, B. & Verleysen, M. Classification in the presence of label noise: a survey. IEEE Trans. Neural Netw. Learn. Syst. 25, 845\u2013869 (2013).","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"384_CR27","doi-asserted-by":"publisher","first-page":"2224","DOI":"10.1021\/acs.jctc.9b01109","volume":"16","author":"BM Floser","year":"2020","unstructured":"Floser, B. M., Guo, Y., Riplinger, C., Tuczek, F. & Neese, F. Detailed pair natural orbital-based coupled cluster studies of spin crossover energetics. J. Chem. Theory Comput. 16, 2224\u20132235 (2020).","journal-title":"J. Chem. Theory Comput."},{"key":"384_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1063\/1.1390175","volume":"577","author":"JP Perdew","year":"2001","unstructured":"Perdew, J. P. & Schmidt, K. Jacob\u2019s ladder of density functional approximations for the exchange-correlation energy. Density Funct. Theory Its Application Mater. 577, 1\u201320 (2001).","journal-title":"Density Funct. Theory Its Application Mater."},{"key":"384_CR29","doi-asserted-by":"publisher","first-page":"074101","DOI":"10.1063\/5.0082964","volume":"156","author":"DR Harper","year":"2022","unstructured":"Harper, D. R. et al. Representations and strategies for transferable machine learning improve model performance in chemical discovery. J. Chem. Phys. 156, 074101 (2022).","journal-title":"J. Chem. Phys."},{"key":"384_CR30","doi-asserted-by":"publisher","first-page":"4373","DOI":"10.1021\/acs.jctc.0c00358","volume":"16","author":"C Duan","year":"2020","unstructured":"Duan, C., Liu, F., Nandy, A. & Kulik, H. J. Data-driven approaches can overcome the cost-accuracy trade-off in multireference diagnostics. J. Chem. Theory Comput. 16, 4373\u20134387 (2020).","journal-title":"J. Chem. Theory Comput."},{"key":"384_CR31","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1021\/acs.jctc.8b01089","volume":"15","author":"S Lehtola","year":"2019","unstructured":"Lehtola, S. Assessment of initial guesses for self-consistent field calculations. Superposition of atomic potentials: simple yet efficient. J. Chem. Theory Comput. 15, 1593\u20131604 (2019).","journal-title":"J. Chem. Theory Comput."},{"key":"384_CR32","doi-asserted-by":"publisher","first-page":"6134","DOI":"10.1021\/acs.jctc.1c00659","volume":"17","author":"LR Maurer","year":"2021","unstructured":"Maurer, L. R., Bursch, M., Grimme, S. & Hansen, A. Assessing density functional theory for chemically relevant open-shell transition metal reactions. J. Chem. Theory Comput. 17, 6134\u20136151 (2021).","journal-title":"J. Chem. Theory Comput."},{"key":"384_CR33","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2019","unstructured":"Miyato, T., Maeda, S. I., Koyama, M. & Ishii, S. Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1979\u20131993 (2019).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"384_CR34","doi-asserted-by":"publisher","first-page":"8939","DOI":"10.1021\/acs.jpca.7b08750","volume":"121","author":"JP Janet","year":"2017","unstructured":"Janet, J. P. & Kulik, H. J. Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships. J. Phys. Chem. A 121, 8939\u20138954 (2017).","journal-title":"J. Phys. Chem. A"},{"key":"384_CR35","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1107\/S2052520616003954","volume":"72","author":"CR Groom","year":"2016","unstructured":"Groom, C. R., Bruno, I. J., Lightfoot, M. P. & Ward, S. C. The cambridge structural database. Acta Crystallogr. B. 72, 171\u2013179 (2016).","journal-title":"Acta Crystallogr. B."},{"key":"384_CR36","doi-asserted-by":"publisher","first-page":"7913","DOI":"10.1039\/C9SC02298H","volume":"10","author":"JP Janet","year":"2019","unstructured":"Janet, J. P., Duan, C., Yang, T. H., Nandy, A. & Kulik, H. J. A quantitative uncertainty metric controls error in neural network-driven chemical discovery. Chem. Sci. 10, 7913\u20137922 (2019).","journal-title":"Chem. Sci."},{"key":"384_CR37","doi-asserted-by":"publisher","first-page":"8864","DOI":"10.1103\/PhysRev.136.B864","volume":"136","author":"P Hohenberg","year":"1964","unstructured":"Hohenberg, P. & Kohn, W. Inhomogeneous electron gas. Phys. Rev. 136, 8864\u20138871 (1964).","journal-title":"Phys. Rev."},{"key":"384_CR38","doi-asserted-by":"publisher","first-page":"4814","DOI":"10.1021\/acs.jcim.9b00725","volume":"59","author":"BP Pritchard","year":"2019","unstructured":"Pritchard, B. P., Altarawy, D., Didier, B., Gibson, T. D. & Windus, T. L. New basis set exchange: an open, up-to-date resource for the molecular sciences community. J. Chem. Inf. Model. 59, 4814\u20134820 (2019).","journal-title":"J. Chem. Inf. Model."},{"key":"384_CR39","doi-asserted-by":"publisher","first-page":"146401","DOI":"10.1103\/PhysRevLett.98.146401","volume":"98","author":"J Behler","year":"2007","unstructured":"Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).","journal-title":"Phys. Rev. Lett."},{"key":"384_CR40","doi-asserted-by":"publisher","first-page":"5648","DOI":"10.1063\/1.464913","volume":"98","author":"AD Becke","year":"1993","unstructured":"Becke, A. D. Density-functional thermochemistry. III. The role of exact exchange. J. Chem. Phys. 98, 5648\u20135652 (1993).","journal-title":"J. Chem. Phys."},{"key":"384_CR41","doi-asserted-by":"publisher","first-page":"11623","DOI":"10.1021\/j100096a001","volume":"98","author":"PJ Stephens","year":"1994","unstructured":"Stephens, P. J., Devlin, F. J., Chabalowski, C. F. & Frisch, M. J. Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields. J. Phys. Chem. 98, 11623\u201311627 (1994).","journal-title":"J. Phys. Chem."},{"key":"384_CR42","doi-asserted-by":"publisher","first-page":"e1494","DOI":"10.1002\/wcms.1494","volume":"11","author":"S Seritan","year":"2021","unstructured":"Seritan, S. et al. TeraChem: a graphical processing unit-accelerated electronic structure package for large-scale ab initio molecular dynamics. WIREs Comput. Mol. Sci. 11, e1494 (2021).","journal-title":"WIREs Comput. Mol. Sci."},{"key":"384_CR43","doi-asserted-by":"publisher","first-page":"2619","DOI":"10.1021\/ct9003004","volume":"5","author":"IS Ufimtsev","year":"2009","unstructured":"Ufimtsev, I. S. & Martinez, T. J. Quantum chemistry on graphical processing units. 3. Analytical energy gradients, geometry optimization, and first principles molecular dynamics. J. Chem. Theory Comput. 5, 2619\u20132628 (2009).","journal-title":"J. Chem. Theory Comput."},{"key":"384_CR44","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1063\/1.448975","volume":"82","author":"PJ Hay","year":"1985","unstructured":"Hay, P. J. & Wadt, W. R. Ab initio effective core potentials for molecular calculations. Potentials for K to Au including the outermost core orbitals. J. Chem. Phys. 82, 299\u2013310 (1985).","journal-title":"J. Chem. Phys."},{"key":"384_CR45","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1002\/qua.560070407","volume":"7","author":"VR Saunders","year":"1973","unstructured":"Saunders, V. R. & Hillier, I. H. A \u201clevel-shifting\u201d method for converging closed shell Hartree\u2013Fock wave functions. Int. J. Quant. Chem. 7, 699\u2013705 (1973).","journal-title":"Int. J. Quant. Chem."},{"key":"384_CR46","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1002\/jcc.24437","volume":"37","author":"EI Ioannidis","year":"2016","unstructured":"Ioannidis, E. I., Gani, T. Z. H. & Kulik, H. J. molSimplify: a toolkit for automating discovery in inorganic chemistry. J. Comput. Chem. 37, 2106\u20132117 (2016).","journal-title":"J. Comput. Chem."},{"key":"384_CR47","doi-asserted-by":"publisher","first-page":"214108","DOI":"10.1063\/1.4952956","volume":"144","author":"L-P Wang","year":"2016","unstructured":"Wang, L.-P. & Song, C. Geometry optimization made simple with translation and rotation coordinates. J. Chem. Phys. 144, 214108 (2016).","journal-title":"J. Chem. Phys."},{"key":"384_CR48","doi-asserted-by":"publisher","first-page":"1390","DOI":"10.1039\/D1CP04885F","volume":"24","author":"BA Finney","year":"2022","unstructured":"Finney, B. A., Chowdhury, S. R., Kirkvold, C. & Vlaisavljevich, B. CASPT2 molecular geometries of Fe(II) spin-crossover complexes. Phys. Chem. Chem. Phys. 24, 1390\u20131398 (2022).","journal-title":"Phys. Chem. Chem. Phys."},{"key":"384_CR49","doi-asserted-by":"publisher","first-page":"2331","DOI":"10.1021\/acs.jctc.9b00057","volume":"15","author":"C Duan","year":"2019","unstructured":"Duan, C., Janet, J. P., Liu, F., Nandy, A. & Kulik, H. J. Learning from failure: predicting electronic structure calculation outcomes with machine learning models. J. Chem. Theory Comput. 15, 2331\u20132345 (2019).","journal-title":"J. Chem. Theory Comput."},{"key":"384_CR50","doi-asserted-by":"publisher","first-page":"184108","DOI":"10.1063\/5.0006002","volume":"152","author":"DGA Smith","year":"2020","unstructured":"Smith, D. G. A. et al. PSI4 1.4: open-source software for high-throughput quantum chemistry. J. Chem. Phys. 152, 184108 (2020).","journal-title":"J. Chem. Phys."},{"key":"384_CR51","doi-asserted-by":"publisher","first-page":"219","DOI":"10.3389\/fchem.2019.00219","volume":"7","author":"F Liu","year":"2019","unstructured":"Liu, F. et al. Bridging the homogeneous\u2013heterogeneous divide: modeling spin for reactivity in single atom catalysis. Front. Chem. 7, 219 (2019).","journal-title":"Front. Chem."},{"key":"384_CR52","doi-asserted-by":"publisher","first-page":"6928","DOI":"10.1021\/ic025891l","volume":"41","author":"M Reiher","year":"2002","unstructured":"Reiher, M. Theoretical study of the Fe(phen)2(NCS)2 spin-crossover complex with reparametrized density functionals. Inorg. Chem. 41, 6928\u20136935 (2002).","journal-title":"Inorg. Chem."},{"key":"384_CR53","doi-asserted-by":"publisher","first-page":"4109","DOI":"10.1021\/acs.jctc.8b00342","volume":"14","author":"J Shee","year":"2018","unstructured":"Shee, J., Arthur, E. J., Zhang, S., Reichman, D. R. & Friesner, R. A. Phaseless auxiliary-field quantum monte carlo on graphical processing units. J. Chem. Theory Comput. 14, 4109\u20134121 (2018).","journal-title":"J. Chem. Theory Comput."},{"key":"384_CR54","doi-asserted-by":"crossref","unstructured":"Bergstra, J., Yamins, D. & Cox, D. D. HyperOpt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. In Proceedings of the 12th Python in Science Conference, 13, 20 (2013).","DOI":"10.25080\/Majora-8b375195-003"},{"key":"384_CR55","unstructured":"Pytorch https:\/\/pytorch.org\/ (2022)."},{"key":"384_CR56","doi-asserted-by":"publisher","unstructured":"Duan, C., Nandy, A., Meyer, R., Arunachalam, N. & Kulik, H. J. A transferable recommender approach for selecting the best density functional approximations in chemical discovery. Zenodo https:\/\/doi.org\/10.5281\/zenodo.7350957 (2022).","DOI":"10.5281\/zenodo.7350957"}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00384-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00384-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00384-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:07:40Z","timestamp":1675037260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-022-00384-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,22]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["384"],"URL":"https:\/\/doi.org\/10.1038\/s43588-022-00384-0","relation":{},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,22]]},"assertion":[{"value":"7 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing financial interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}