Ramakrishnan, R, Dral, PO, Rupp, M, Von Lilienfeld, OA. Quantum chemistry structures and properties of 134 kilo molecules. Sci Data. 2014;1:140022.
Smith, JS, Isayev, O, Roitberg, AE. ANI‐1: A data set of 20 million calculated off‐equilibrium conformations for organic molecules. Sci Data. 2017;4:170193.
Faber, FA, Hutchison, L, Huang, B, et al. Prediction errors of molecular machine learning models lower than hybrid DFT error. J Chem Theory Comput. 2017;13:5255–5264.
von Lilienfeld, OA. Quantum machine learning in chemical compound space. Angew Chem Int Ed. 2018;57:4164–4169.
Behler, J. Perspective: Machine learning potentials for atomistic simulations. J Chem Phys. 2016;145:170901.
Smith, JS, Isayev, O, Roitberg, AE. ANI‐1: An extensible neural network potential with DFT accuracy at force field computational cost. Chem Sci. 2017;8:3192–3203.
Yao, K, Herr, JE, Toth, DW, Mckintyre, R, Parkhill, J. The TensorMol‐0.1 model chemistry: A neural network augmented with long‐range physics. Chem Sci. 2018;9:2261–2269.
Li, H, Collins, C, Tanha, M, Gordon, GJ, Yaron, DJ. A density functional tight binding layer for deep learning of chemical Hamiltonians. J Chem Theory Comput. 2018;14:5764–5776.
Gastegger, M, Behler, J, Marquetand, P. Machine learning molecular dynamics for the simulation of infrared spectra. Chem Sci. 2017;8:6924–6935.
Bleiziffer, P, Schaller, K, Riniker, S. Machine learning of partial charges derived from high‐quality quantum‐mechanical calculations. J Chem Inf Model. 2018;58:579–590.
Sifain, AE, Lubbers, N, Nebgen, BT, et al. Discovering a transferable charge assignment model using machine learning. J Phys Chem Lett. 2018;9:4495–4501.
Ashley, DC, Jakubikova, E. Ironing out the photochemical and spin‐crossover behavior of Fe(II) coordination compounds with computational chemistry. Coord Chem Rev. 2017;337:97–111.
Bowman, DN, Bondarev, A, Mukherjee, S, Jakubikova, E. Tuning the electronic structure of Fe(II) polypyridines via donor atom and ligand scaffold modifications: A computational study. Inorg Chem. 2015;54:8786–8793.
Yella, A, Lee, HW, Tsao, HN, et al. Porphyrin‐sensitized solar cells with cobalt (II/III)‐based redox electrolyte exceed 12 percent efficiency. Science. 2011;334:629–634.
Czerwieniec, R, Yu, JB, Yersin, H. Blue‐light emission of Cu(I) complexes and singlet harvesting. Inorg Chem. 2011;50:8293–8301.
Dias, FB, Bourdakos, KN, Jankus, V, et al. Triplet harvesting with 100% efficiency by way of thermally activated delayed fluorescence in charge transfer OLED emitters. Adv Mater. 2013;25:3707–3714.
Kuttipillai, PS, Zhao, YM, Traverse, CJ, Staples, RJ, Levine, BG, Lunt, RR. Phosphorescent nanocluster light‐emitting diodes. Adv Mater. 2016;28:320–326.
Leitl, MJ, Kuchle, FR, Mayer, HA, Wesemann, L, Yersin, H. Brightly blue and green emitting Cu(I) dimers for singlet harvesting in OLEDs. J Phys Chem A. 2013;117:11823–11836.
Linfoot, CL, Leitl, MJ, Richardson, P, et al. Thermally activated delayed fluorescence (TADF) and enhancing photoluminescence quantum yields of Cu‐I(diimine)(diphosphine)(+) complexes—Photophysical, structural, and computational studies. Inorg Chem. 2014;53:10854–10861.
Zink, DM, Bachle, M, Baumann, T, et al. Synthesis, structure, and characterization of dinuclear copper(I) halide complexes with PAN ligands featuring exciting photoluminescence properties. Inorg Chem. 2013;52:2292–2305.
Vogiatzis, KD, Polynski, MV, Kirkland, JK, et al. Computational approach to molecular catalysis by 3d transition metals: Challenges and opportunities. Chem Rev. 2019;119:2453–2523.
Grajciar, L, Heard, CJ, Bondarenko, AA, et al. Towards operando computational modeling in heterogeneous catalysis. Chem Soc Rev. 2018;47:8307–8348.
Arockiam, PB, Bruneau, C, Dixneuf, PH. Ruthenium(II)‐catalyzed C─H bond activation and functionalization. Chem Rev. 2012;112:5879–5918.
Prier, CK, Rankic, DA, MacMillan, DWC. Visible light photoredox catalysis with transition metal complexes: Applications in organic synthesis. Chem Rev. 2013;113:5322–5363.
Rouquet, G, Chatani, N. Catalytic functionalization of C(sp(2))─H and C(sp(3))─H bonds by using bidentate directing groups. Angew Chem Int Ed. 2013;52:11726–11743.
Schultz, DM, Yoon, TP. Solar synthesis: Prospects in visible light photocatalysis. Science. 2014;343:985.
Shaffer, DW, Bhowmick, I, Rheingold, AL, et al. Spin‐state diversity in a series of Co(II) PNP pincer bromide complexes. Dalton Trans. 2016;45:17910–17917.
Tsay, C, Yang, JY. Electrocatalytic hydrogen evolution under acidic aqueous conditions and mechanistic studies of a highly stable molecular catalyst. J Am Chem Soc. 2016;138:14174–14177.
Deeth, RJ. The ligand field molecular mechanics model and the stereoelectronic effects of d and s electrons. Coord Chem Rev. 2001;212:11–34.
Rappé, AK, Casewit, CJ, Colwell, K, Goddard, WA III, Skiff, W. UFF: A full periodic table force field for molecular mechanics and molecular dynamics simulations. J Am Chem Soc. 1992;114:10024–10035.
Minenkov, Y, Sharapa, DI, Cavallo, L. Application of semiempirical methods to transition metal complexes: Fast results but hard‐to‐predict accuracy. J Chem Theory Comput. 2018;14:3428–3439.
Jorgensen, WL, Maxwell, DS, Tirado‐Rives, J. Development and testing of the OPLS all‐atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc. 1996;118:11225–11236.
Wang, J, Wolf, RM, Caldwell, JW, Kollman, PA, Case, DA. Development and testing of a general amber force field. J Comput Chem. 2004;25:1157–1174.
Brandenburg, JG, Grimme, S. Accurate modeling of organic molecular crystals by dispersion‐corrected density functional tight binding (DFTB). J Phys Chem Lett. 2014;5:1785–1789.
Gaus, M, Cui, Q, Elstner, M. DFTB3: Extension of the self‐consistent‐charge density‐functional tight‐binding method (SCC‐DFTB). J Chem Theory Comput. 2011;7:931–948.
Korth, M, Thiel, W. Benchmarking semiempirical methods for thermochemistry, kinetics, and noncovalent interactions: OMx methods are almost as accurate and robust as DFT‐GGA methods for organic molecules. J Chem Theory Comput. 2011;7:2929–2936.
Janet, JP, Liu, F, Nandy, A, et al. Designing in the face of uncertainty: Exploiting electronic structure and machine learning models for discovery in inorganic chemistry. Inorg Chem. 2019. https://doi.org/10.1021/acs.inorgchem.9b00109
Ioannidis, EI, Gani, TZH, Kulik, HJ. molSimplify: A toolkit for automating discovery in inorganic chemistry. J Comput Chem. 2016;37:2106–2117.
Weininger, D. SMILES: A chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci. 1988;28:31–36.
Hastie, T, Tibshirani, R, Friedman, J. The elements of statistical learning. Vol 18. New York, NY: Springer, 2009.
Rupp, M, Tkatchenko, A, Müller, K‐R, von Lilienfeld, OA. Fast and accurate modeling of molecular atomization energies with machine learning. Phys Rev Lett. 2012;108:058301.
Huang, B, von Lilienfeld, OA. Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity. J Chem Phys. 2016;145:161102.
De, S, Bartok, AP, Csanyi, G, Ceriotti, M. Comparing molecules and solids across structural and alchemical space. Phys Chem Chem Phys. 2016;18:13754–13769.
Ma, X, Li, Z, Achenie, LEK, Xin, H. Machine‐learning‐augmented chemisorption model for CO2 electroreduction catalyst screening. J Phys Chem Lett. 2015;6:3528–3533.
Durand, DJ, Fey, N. Computational ligand descriptors for catalyst design. Chem Rev. 2019;119:6561–6594.
Curtarolo, S, Setyawan, W, Hart, GLW, et al. AFLOW: An automatic framework for high‐throughput materials discovery. Comput Mater Sci. 2012;58:218–226.
Ong, SP, Richards, WD, Jain, A, et al. Python materials genomics (pymatgen): A robust, open‐source python library for materials analysis. Comput Mater Sci. 2013;68:314–319.
Nandy, A, Duan, C, Janet, JP, Gugler, S, Kulik, HJ. Strategies and software for machine learning accelerated discovery in transition metal chemistry. Ind Eng Chem Res. 2018;57:13973–13986.
O`Boyle, NM, Banck, M, James, CA, Morley, C, Vandermeersch, T, Hutchison, GR. Open Babel: An open chemical toolbox. J Chem. 2011;3:33.
Martínez, TJ. Ab initio reactive computer aided molecular design. Acc Chem Res. 2017;50:652–656.
Kulik, HJ, Cococcioni, M, Scherlis, DA, Marzari, N. Density functional theory in transition‐metal chemistry: A self‐consistent Hubbard U approach. Phys Rev Lett. 2006;97:103001.
Ganzenmüller, G, Berkaïne, N, Fouqueau, A, Casida, ME, Reiher, M. Comparison of density functionals for differences between the high‐ (T2g5) and low‐ (A1g1) spin states of iron(II) compounds. IV. Results for the ferrous complexes [Fe(L)(‘NHS4’)]. J Chem Phys. 2005;122:234321.
Droghetti, A, Alfè, D, Sanvito, S. Assessment of density functional theory for iron(II) molecules across the spin‐crossover transition. J Chem Phys. 2012;137:124303.
Ioannidis, EI, Kulik, HJ. Towards quantifying the role of exact exchange in predictions of transition metal complex properties. J Chem Phys. 2015;143:034104.
Mortensen, SR, Kepp, KP. Spin propensities of octahedral complexes from density functional theory. Chem A Eur J. 2015;119:4041–4050.
Ioannidis, EI, Kulik, HJ. Ligand‐field‐dependent behavior of meta‐GGA exchange in transition‐metal complex spin‐state ordering. J Phys Chem A. 2017;121:874–884.
Kulik, HJ. Perspective: Treating electron over‐delocalization with the DFT+U method. J Chem Phys. 2015;142:240901.
Janet, JP, Zhao, Q, Ioannidis, EI, Kulik, HJ. Density functional theory for modelling large molecular adsorbate–surface interactions: A mini‐review and worked example. Mol Simulat. 2017;43:327–345.
Wilbraham, L, Verma, P, Truhlar, DG, Gagliardi, L, Ciofini, I. Multiconfiguration pair‐density functional theory predicts spin‐state ordering in iron complexes with the same accuracy as complete active space second‐order perturbation theory at a significantly reduced computational cost. J Phys Chem Lett. 2017;8:2026–2030.
Gani, TZH, Kulik, HJ. Unifying exchange sensitivity in transition metal spin‐state ordering and catalysis through bond valence metrics. J Chem Theory Comput. 2017;13:5443–5457.
Deeth, RJ, Fey, N. The performance of nonhybrid density functionals for calculating the structures and spin states of Fe(II) and Fe(III) complexes. J Comput Chem. 2004;25:1840–1848.
Konezny, SJ, Doherty, MD, Luca, OR, Crabtree, RH, Soloveichik, GL, Batista, VS. Reduction of systematic uncertainty in DFT redox potentials of transition‐metal complexes. J Phys Chem C. 2012;116:6349–6356.
Mahler, A, Janesko, BG, Moncho, S, Brothers, EN. When Hartree–Fock exchange admixture lowers DFT‐predicted barrier heights: Natural bond orbital analyses and implications for catalysis. J Chem Phys. 2018;148:244106.
Zhou, C, Gagliardi, L, Truhlar, DG. Multiconfiguration pair‐density functional theory for iron porphyrin with CAS, RAS, and DMRG active spaces. J Phys Chem A. 2019;123:3389–3394.
Huang, W, Xing, D‐H, Lu, J‐B, Long, B, Schwarz, WHE, Li, J. How much can density functional approximations (DFA) fail? The extreme case of the FeO4 species. J Chem Theory Comput. 2016;12:1525–1533.
Evangelista, FA. Perspective: Multireference coupled cluster theories of dynamical electron correlation. J Chem Phys. 2018;149:030901.
Cramer, CJ, Truhlar, DG. Density functional theory for transition metals and transition metal chemistry. Phys Chem Chem Phys. 2009;11:10757–10816.
Pierloot, K, Phung, QM, Domingo, A. Spin state energetics in first‐row transition metal complexes: Contribution of (3s3p) correlation and its description by second‐order perturbation theory. J Chem Theory Comput. 2017;13:537–553.
Liu, F, Yang, T, Yang, J, Xu, E, Bajaj, A, Kulik, HJ. Bridging the homogeneous–heterogeneous divide: Modeling spin and reactivity in single atom catalysis. Front Chem. 2019;7:219.
Stein, CJ, Reiher, M. Automated selection of active orbital spaces. J Chem Theory Comput. 2016;12:1760–1771.
Sayfutyarova, ER, Sun, Q, Chan, GK‐L, Knizia, G. Automated construction of molecular active spaces from atomic valence orbitals. J Chem Theory Comput. 2017;13:4063–4078.
Ramprasad, R, Batra, R, Pilania, G, Mannodi‐Kanakkithodi, A, Kim, C. Machine learning in materials informatics: Recent applications and prospects. npj Comput Mater. 2017;3:54.
Kitchin, JR. Machine learning in catalysis. Nat Catal. 2018;1:230–232.
Goldsmith, BR, Esterhuizen, J, Liu, JX, Bartel, CJ, Sutton, C. Machine learning for heterogeneous catalyst design and discovery. AIChE J. 2018;64:2311–2323.
Venkatasubramanian, V. The promise of artificial intelligence in chemical engineering: Is it here, finally? AIChE J. 2019;65:466–478.
Ceriotti, M. Unsupervised machine learning in atomistic simulations, between predictions and understanding. J Chem Phys. 2019;150:150901.
Burns, JA, Whitesides, GM. Feed‐forward neural networks in chemistry: Mathematical systems for classification and pattern recognition. Chem Rev. 1993;93:2583–2601.
Gasteiger, J, Zupan, J. Neural networks in chemistry. Angew Chem Int Ed. 1993;32:503–527.
Lemonick, S. Is machine learning overhyped? Chem Eng News. 2018;96:16–20.
Ufimtsev, IS, Martinez, TJ. Quantum chemistry on graphical processing units. 3. Analytical energy gradients, geometry optimization, and first principles molecular dynamics. J Chem Theory Comput. 2009;5:2619–2628.
Ufimtsev, IS, Martínez, TJ. Quantum chemistry on graphical processing units. 1. Strategies for two‐electron integral evaluation. J Chem Theory Comput. 2008;4:222–231.
Ufimtsev, IS, Martínez, TJ. Quantum chemistry on graphical processing units. 2. Direct self‐consistent‐field implementation. J Chem Theory Comput. 2009;5:1004–1015.
Ochsenfeld, C, Kussmann, J, Lambrecht, DS. Linear‐scaling methods in quantum chemistry. Rev Comput Chem. 2007;23:1.
Eichkorn, K, Weigend, F, Treutler, O, Ahlrichs, R. Auxiliary basis sets for main row atoms and transition metals and their use to approximate Coulomb potentials. Theor Chem Acc. 1997;97:119–124.
Eichkorn, K, Treutler, O, Öhm, H, Häser, M, Ahlrichs, R. Auxiliary basis sets to approximate Coulomb potentials. Chem Phys Lett. 1995;240:283–290.
Libisch, F, Huang, C, Carter, EA. Embedded correlated wavefunction schemes: Theory and applications. Acc Chem Res. 2014;47:2768–2775.
Challacombe, M, Schwegler, E. Linear scaling computation of the Fock matrix. J Chem Phys. 1997;106:5526–5536.
Hampel, C, Werner, HJ. Local treatment of electron correlation in coupled cluster theory. J Chem Phys. 1996;104:6286–6297.
Schütz, M, Hetzer, G, Werner, H‐J. Low‐order scaling local electron correlation methods. I. Linear scaling local MP2. J Chem Phys. 1999;111:5691–5705.
Hohenstein, EG, Parrish, RM, Martínez, TJ. Tensor hypercontraction density fitting. I. Quartic scaling second‐and third‐order Møller‐Plesset perturbation theory. J Chem Phys. 2012;137:044103.
Song, C, Martínez, TJ. Reduced scaling CASPT2 using supporting subspaces and tensor hyper‐contraction. J Chem Phys. 2018;149:044108.
Andermatt, S, Cha, J, Schiffmann, F, VandeVondele, J. Combining linear‐scaling DFT with subsystem DFT in Born–Oppenheimer and Ehrenfest molecular dynamics simulations: From molecules to a virus in solution. J Chem Theory Comput. 2016;12:3214–3227.
Goodpaster, JD, Barnes, TA, Manby, FR, Miller, TF III. Density functional theory embedding for correlated wavefunctions: Improved methods for open‐shell systems and transition metal complexes. J Chem Phys. 2012;137:224113.
Landrum G. Rdkit,. Open‐source cheminformatics software [cited 2019 May 11]. Available from: http://www.rdkit.org.
Calderon, CE, Plata, JJ, Toher, C, et al. The AFLOW standard for high‐throughput materials science calculations. Comput Mater Sci. 2015;108:233–238.
Larsen, AH, Mortensen, JJ, Blomqvist, J, et al. The atomic simulation environment—A Python library for working with atoms. J Phys Condens Matter. 2017;29:273002.
Chollet, François and others. Keras. 2015. [cited 2019 May 11]. Available from: https://keras.io/.
Abadi M, Agarwal A, Barham P, et al. TensorFlow: Large‐Scale Machine Learning on Heterogeneous Distributed Systems. 2016. [cited 2019 May 11]. Available from: https://www.tensorflow.org.
Ramakrishnan, R, Dral, PO, Rupp, M, von Lilienfeld, OA. Big data meets quantum chemistry approximations: The delta‐machine learning approach. J Chem Theory Comput. 2015;11:2087–2096.
Gómez‐Bombarelli, R, Wei, JN, Duvenaud, D, et al. Automatic chemical design using a data‐driven continuous representation of molecules. ACS Cent Sci. 2018;4:268–276.
Zhuo, Y, Mansouri Tehrani, A, Brgoch, J. Predicting the band gaps of inorganic solids by machine learning. J Phys Chem Lett. 2018;9:1668–1673.
Ward, L, Agrawal, A, Choudhary, A, Wolverton, C. A general‐purpose machine learning framework for predicting properties of inorganic materials. npj Comput Mater. 2016;2:16028.
Pilania, G, Wang, C, Jiang, X, Rajasekaran, S, Ramprasad, R. Accelerating materials property predictions using machine learning. Sci Rep. 2013;3:2810.
Meyer, B, Sawatlon, B, Heinen, S, von Lilienfeld, OA, Corminboeuf, C. Machine learning meets volcano plots: Computational discovery of cross‐coupling catalysts. Chem Sci. 2018;9:7069–7077.
Denzel, A, Kästner, J. Gaussian process regression for geometry optimization. J Chem Phys. 2018;148:094114.
Koistinen, O‐P, Dagbjartsdóttir, FB, Ásgeirsson, V, Vehtari, A, Jónsson, H. Nudged elastic band calculations accelerated with Gaussian process regression. J Chem Phys. 2017;147:152720.
Carr, S, Garnett, R, Lo, C. BASC: Applying Bayesian optimization to the search for global minima on potential energy surfaces. Proceedings of the 33rd International Conference on Machine Learning, in PMLR, New York, NY; 2016.
Wu, J, Shen, L, Yang, W. Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations. J Chem Phys. 2017;147:161732.
Welborn, M, Cheng, L, Miller, TF. Transferability in machine learning for electronic structure via the molecular orbital basis. J Chem Theory Comput. 2018;14:4772–4779.
Gani, TZH, Kulik, HJ. Understanding and breaking scaling relations in single‐site catalysis: Methane‐to‐methanol conversion by Fe(IV)═O. ACS Catal. 2018;8:975–986.
Kim, JY, Kulik, HJ. When is ligand pKa a good descriptor for catalyst energetics? In search of optimal CO2 hydration catalysts. Chem A Eur J. 2018;122:4579–4590.
Kim, JY, Steeves, AH, Kulik, HJ. Harnessing organic ligand libraries for first‐principles inorganic discovery: Indium phosphide quantum dot precursor design strategies. Chem Mater. 2017;29:3632–3643.
Gani, TZH, Ioannidis, EI, Kulik, HJ. Computational discovery of hydrogen bond design rules for electrochemical ion separation. Chem Mater. 2016;28:6207–6218.
Janet, JP, Kulik, HJ. Predicting electronic structure properties of transition metal complexes with neural networks. Chem Sci. 2017;8:5137–5152.
Janet, JP, Kulik, HJ. Resolving transition metal chemical space: Feature selection for machine learning and structure–property relationships. J Phys Chem A. 2017;121:8939–8954.
Janet, JP, Duan, C, Yang, T, Nandy, A, Kulik, HJ. A quantitative uncertainty metric controls error in neural network‐driven chemical discovery. Chem Sci. 2019. https://doi.org/10.1039/C9SC02298H
Cubuk, ED, Malone, BD, Onat, B, Waterland, A, Kaxiras, E. Representations in neural network based empirical potentials. J Chem Phys. 2017;147:024104.
Janet, JP, Chan, L, Kulik, HJ. Accelerating chemical discovery with machine learning: Simulated evolution of spin crossover complexes with an artificial neural network. J Phys Chem Lett. 2018;9:1064–1071.
Behler, J, Parrinello, M. Generalized neural‐network representation of high‐dimensional potential‐energy surfaces. Phys Rev Lett. 2007;98:146401.
Hansen, K, Montavon, G, Biegler, F, et al. Assessment and validation of machine learning methods for predicting molecular atomization energies. J Chem Theory Comput. 2013;9:3404–3419.
Behler, J. Representing potential energy surfaces by high‐dimensional neural network potentials. J Phys Condens Matter. 2014;26:183001.
Gastegger, M, Marquetand, P. High‐dimensional neural network potentials for organic reactions and an improved training algorithm. J Chem Theory Comput. 2015;11:2187–2198.
Hansen, K, Biegler, F, Ramakrishnan, R, et al. Machine learning predictions of molecular properties: Accurate many‐body potentials and nonlocality in chemical space. J Phys Chem Lett. 2015;6:2326–2331.
Montavon, G, Hansen, K, Fazli, S, Rupp, M. Learning invariant representations ofmolecules for atomization energy prediction. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ, editors. Advances in neural information processingsystems. Red Hook, NY: Curran Associates, Inc, 2012.
Kier, LB. A shape index from molecular graphs. Quant Struct‐Activity Relat. 1985;4:109–116.
Broto, P, Moreau, G, Vandycke, C. Molecular structures: Perception, autocorrelation descriptor and Sar studies: System of atomic contributions for the calculation of the N‐octanol/water partition coefficients. Eur J Med Chem. 1984;19:71–78.
Schütt, K, Kindermans, P‐J, Felix, HES, Chmiela, S, Tkatchenko, A, Müller, K‐R. Schnet: A continuous‐filter convolutional neural network for modeling quantum interactions. In: Maria Florina Balcan, Kilian Q. Weinberger, editors. Advances inneural information processing systems. New York, NY: PMLR, 2017.
Schütt, KT, Sauceda, HE, Kindermans, P‐J, Tkatchenko, A, Müller, K‐R. SchNet—A deep learning architecture for molecules and materials. J Chem Phys. 2018;148:241722.
Wu, ZQ, Ramsundar, B, Feinberg, EN, et al. MoleculeNet: A benchmark for molecular machine learning. Chem Sci. 2018;9:513–530.
Coley, CW, Barzilay, R, Green, WH, Jaakkola, TS, Jensen, KF. Convolutional embedding of attributed molecular graphs for physical property prediction. J Chem Inf Model. 2017;57:1757–1772.
Xie, T, Grossman, JC. Hierarchical visualization of materials space with graph convolutional neural networks. J Chem Phys. 2018;149:174111.
Tibshirani, R. Regression shrinkage and selection via the lasso. J Roy Stat Soc Ser B. 1996;58:267–288.
Breiman, L. Random forests. Mach Learn. 2001;45:5–32.
Janet, JP, Kulik, HJ. molSimplify web tutorials [cited 2019 May 11]. Available from: http://molsimplify.mit.edu.
Jurečka, P, Šponer, J, Černý, J, Hobza, P. Benchmark database of accurate (MP2 and CCSD (T) complete basis set limit) interaction energies of small model complexes, DNA base pairs, and amino acid pairs. Phys Chem Chem Phys. 2006;8:1985–1993.
Mardirossian, N, Head‐Gordon, M. Thirty years of density functional theory in computational chemistry: An overview and extensive assessment of 200 density functionals. Molec Phys. 2017;115:2315–2372.
Lynch, BJ, Truhlar, DG. Small representative benchmarks for thermochemical calculations. Chem A Eur J. 2003;107:8996–8999.
Mallikarjun Sharada, S, Bligaard, T, Luntz, AC, Kroes, G‐J, Nørskov, JK. SBH10: A benchmark database of barrier heights on transition metal surfaces. J Phys Chem C. 2017;121:19807–19815.
Iron, MA, Janes, T. Evaluating transition metal barrier heights with the latest density functional theory exchange—Correlation functionals: The MOBH35 benchmark database. Chem A Eur J. 2019;123:3761–3781.
Yu, HS, He, X, Li, SL, Truhlar, DG. MN15: A Kohn–Sham global‐hybrid exchange–correlation density functional with broad accuracy for multi‐reference and single‐reference systems and noncovalent interactions. Chem Sci. 2016;7:5032–5051.
Wilbraham, L, Adamo, C, Ciofini, I. Communication: Evaluating non‐empirical double hybrid functionals for spin‐state energetics in transition‐metal complexes. J Chem Phys. 2018;148:041103.
Pilania, G, Gubernatis, J, Lookman, T. Multi‐fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci. 2017;129:156–163.
Pan, SJ, Yang, Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2010;22:1345–1359.
Stewart, JJ. Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements. J Mol Model. 2007;13:1173–1213.
Stephens, PJ, Devlin, FJ, Chabalowski, CF, Frisch, MJ. Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields. J Phys Chem. 1994;98:11623–11627.
Becke, AD. Density‐functional thermochemistry. III. The role of exact exchange. J Chem Phys. 1993;98:5648–5652.
Lee, C, Yang, W, Parr, RG. Development of the Colle–Salvetti correlation‐energy formula into a functional of the electron density. Phys Rev B. 1988;37:785–789.
Smith, JS, Nebgen, B, Lubbers, N, Isayev, O, Roitberg, AE. Less is more: Sampling chemical space with active learning. J Chem Phys. 2018;148:241733.
Butler, KT, Davies, DW, Cartwright, H, Isayev, O, Walsh, A. Machine learning for molecular and materials science. Nature. 2018;559:547–555.
Gal, Y, Ghahramani, Z. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. New York, NY: International Conference on Machine Learning; 2016.
Cortés‐Ciriano, I, Bender, A. Deep confidence: A computationally efficient framework for calculating reliable prediction errors for deep neural networks. J Chem Inf Model. 2019;59:1269–1281.
Morais, CLM, Lima, KMG, Martin, FL. Uncertainty estimation and misclassification probability for classification models based on discriminant analysis and support vector machines. Anal Chim Acta. 2018;1063:40–46.
Musil, F, Willatt, MJ, Langovoy, MA, Ceriotti, M. Fast and accurate uncertainty estimation in chemical machine learning. J Chem Theory Comput. 2019;15:906–915.
Peterson, AA, Christensen, R, Khorshidi, A. Addressing uncertainty in atomistic machine learning. Phys Chem Chem Phys. 2017;19:10978–10985.
Kaim, W. The shrinking world of innocent ligands: Conventional and non‐conventional redox‐active ligands. Eur J Inorg Chem. 2012;2012:343–348.
Sproviero, EM, Gascón, JA, McEvoy, JP, Brudvig, GW, Batista, VS. Computational studies of the O2‐evolving complex of photosystem II and biomimetic oxomanganese complexes. Coord Chem Rev. 2008;252:395–415.
Duan, C, Janet, JP, Liu, F, Nandy, A, Kulik, HJ. Learning from failure: Predicting electronic structure calculation outcomes with machine learning models. J Chem Theory Comput. 2019;15:2331–2345.
Wang, J, Manivasagam, S, Wilson, AK. Multireference character for 4d transition metal‐containing molecules. J Chem Theory Comput. 2015;11:5865–5872.
Kesharwani, MK, Sylvetsky, N, Köhn, A, Tew, DP, Martin, JM. Do CCSD and approximate CCSD‐F12 variants converge to the same basis set limits? The case of atomization energies. J Chem Phys. 2018;149:154109.
Fogueri, UR, Kozuch, S, Karton, A, Martin, JM. A simple DFT‐based diagnostic for nondynamical correlation. Theor Chem Acc. 2013;132:1291.
Husch, T, Freitag, L, Reiher, M. Calculation of ligand dissociation energies in large transition‐metal complexes. J Chem Theory Comput. 2018;14:2456–2468.
Kim, E, Huang, K, Saunders, A, McCallum, A, Ceder, G, Olivetti, E. Materials synthesis insights from scientific literature via text extraction and machine learning. Chem Mater. 2017;29:9436–9444.