Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Nandy, Aditya; Duan, Chenru; Janet, Jon Paul; Gugler, Stefan; Kulik, Heather J.

doi:10.1021/acs.iecr.8b04015

Title: Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Journal Article · Mon Sep 24 00:00:00 EDT 2018 · Industrial and Engineering Chemistry Research

DOI:https://doi.org/10.1021/acs.iecr.8b04015· OSTI ID:1612842

Nandy, Aditya ^[1]; Duan, Chenru ^[1];

^[1]; Gugler, Stefan ^[2];

^[1]

Massachusetts Institute of Technology (MIT), Cambridge, MA (United States)
Massachusetts Institute of Technology (MIT), Cambridge, MA (United States); Eidgenoessische Technische Hochschule (ETH), Zurich (Switzerland)

Machine learning the electronic structure of open shell transition metal complexes presents unique challenges, including robust and automated data set generation. In this report we introduce tools that simplify data acquisition from density functional theory (DFT) and validation of trained machine learning models using the molSimplify automatic design (mAD) workflow. We demonstrate this workflow by training and comparing the performance of LASSO, kernel ridge regression (KRR), and artificial neural network (ANN) models using heuristic, topological revised autocorrelation (RAC) descriptors we have recently introduced for machine learning inorganic chemistry. On a series of open shell transition metal complexes, we evaluate set aside test errors of these models for predicting the HOMO level and HOMO–LUMO gap. The best performing models are ANNs, which show 0.15 and 0.25 eV test set mean absolute errors on the HOMO level and HOMO–LUMO gap, respectively. Poor performing KRR models using the full 153-feature RAC set are improved to nearly the same performance as the ANNs when trained on down-selected subsets of 20–30 features. Analysis of the essential descriptors for HOMO level and HOMO–LUMO gap prediction as well as comparison to subsets previously obtained for other properties reveal the paramount importance of nonlocal, steric properties in determining frontier molecular orbital energetics. We demonstrate our model performance on diverse complexes and in the discovery of molecules with target HOMO–LUMO gaps from a large 15,000 molecule design space in minutes rather than days that full DFT evaluation would require.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC); US Department of the Navy, Office of Naval Research (ONR); Defense Advanced Research Projects Agency (DARPA); National Science Foundation (NSF)

Grant/Contract Number:: SC0018096; N00014-17-1-2956; N00014-18-1-2434; D18AP00039; CBET-1704266; ACI-1548562; ACI-1429830

OSTI ID:: 1612842

Journal Information:: Industrial and Engineering Chemistry Research, Vol. 57, Issue 42; ISSN 0888-5885

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 79 works

Citation information provided by
Web of Science

References (84)

Switching of Molecular Spin States in Inorganic Complexes by Temperature, Pressure, Magnetic Field and Light: Towards Molecular Devices: Switching of Molecular Spin States in Inorganic Complexes Bousseksou, Azzedine; Molnár, Gábor; Matouzenko, Galina European Journal of Inorganic Chemistry, Vol. 2004, Issue 22 https://doi.org/10.1002/ejic.200400571	journal	November 2004
Light-induced excited spin state trapping in a transition-metal complex: The hexa-1-propyltetrazole-iron (II) tetrafluoroborate spin-crossover system Decurtins, S.; Gütlich, P.; Köhler, C. P. Chemical Physics Letters, Vol. 105, Issue 1 https://doi.org/10.1016/0009-2614(84)80403-0	journal	March 1984
Reversible CO Scavenging via Adsorbate-Dependent Spin State Transitions in an Iron(II)–Triazolate Metal–Organic Framework Reed, Douglas A.; Xiao, Dianne J.; Gonzalez, Miguel I. Journal of the American Chemical Society, Vol. 138, Issue 17 https://doi.org/10.1021/jacs.6b00248	journal	April 2016
Enhanced Cooperativity in Supported Spin-Crossover Metal–Organic Frameworks Groizard, Thomas; Papior, Nick; Le Guennic, Boris The Journal of Physical Chemistry Letters, Vol. 8, Issue 14 https://doi.org/10.1021/acs.jpclett.7b01248	journal	July 2017
Guest Tunable Structure and Spin Crossover Properties in a Nanoporous Coordination Framework Material Neville, Suzanne M.; Halder, Gregory J.; Chapman, Karena W. Journal of the American Chemical Society, Vol. 131, Issue 34 https://doi.org/10.1021/ja905360g	journal	September 2009
The role of transition metal complexes in dye sensitized solar devices Bignozzi, C. A.; Argazzi, R.; Boaretto, R. Coordination Chemistry Reviews, Vol. 257, Issue 9-10 https://doi.org/10.1016/j.ccr.2012.09.008	journal	May 2013
Understanding the reactivity of transition metal complexes involving multiple spin states Harvey, J. Coordination Chemistry Reviews, Vol. 238-239 https://doi.org/10.1016/S0010-8545(02)00283-7	journal	March 2003
Understanding and Breaking Scaling Relations in Single-Site Catalysis: Methane to Methanol Conversion by Fe ^IV ═O Gani, Terry Z. H.; Kulik, Heather J. ACS Catalysis, Vol. 8, Issue 2 https://doi.org/10.1021/acscatal.7b03597	journal	January 2018
Computational Investigation and Design of Cobalt Aqua Complexes for Homogeneous Water Oxidation Schilling, Mauro; Patzke, Greta R.; Hutter, Jürg The Journal of Physical Chemistry C, Vol. 120, Issue 15 https://doi.org/10.1021/acs.jpcc.6b00712	journal	April 2016
When Is Ligand p K _a a Good Descriptor for Catalyst Energetics? In Search of Optimal CO ₂ Hydration Catalysts Kim, Jeong Yun; Kulik, Heather J. The Journal of Physical Chemistry A, Vol. 122, Issue 18 https://doi.org/10.1021/acs.jpca.8b03301	journal	April 2018
Using Gas-Phase Clusters to Screen Porphyrin-Supported Nanocluster Catalysts for Ethane Oxidation to Ethanol Pellizzeri, Steven; Jones, Isaac A.; Doan, Hieu A. Catalysis Letters, Vol. 146, Issue 12 https://doi.org/10.1007/s10562-016-1890-7	journal	October 2016
The Catalyst Genome Nørskov, Jens K.; Bligaard, Thomas Angewandte Chemie International Edition, Vol. 52, Issue 3 https://doi.org/10.1002/anie.201208487	journal	December 2012
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
The high-throughput highway to computational materials design Curtarolo, Stefano; Hart, Gus L. W.; Nardelli, Marco Buongiorno Nature Materials, Vol. 12, Issue 3 https://doi.org/10.1038/nmat3568	journal	February 2013
The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid Hachmann, Johannes; Olivares-Amaya, Roberto; Atahan-Evrenk, Sule The Journal of Physical Chemistry Letters, Vol. 2, Issue 17 https://doi.org/10.1021/jz200866s	journal	August 2011
Efficient Computational Screening of Organic Polymer Photovoltaics Kanal, Ilana Y.; Owens, Steven G.; Bechtel, Jonathon S. The Journal of Physical Chemistry Letters, Vol. 4, Issue 10 https://doi.org/10.1021/jz400215j	journal	April 2013
The ligand field molecular mechanics model and the stereoelectronic effects of d and s electrons Deeth, R. Coordination Chemistry Reviews, Vol. 212, Issue 1 https://doi.org/10.1016/S0010-8545(00)00354-4	journal	February 2001
How Much Can Density Functional Approximations (DFA) Fail? The Extreme Case of the FeO ₄ Species Huang, Wei; Xing, Deng-Hui; Lu, Jun-Bo Journal of Chemical Theory and Computation, Vol. 12, Issue 4 https://doi.org/10.1021/acs.jctc.5b01040	journal	March 2016
Towards quantifying the role of exact exchange in predictions of transition metal complex properties Ioannidis, Efthymios I.; Kulik, Heather J. The Journal of Chemical Physics, Vol. 143, Issue 3 https://doi.org/10.1063/1.4926836	journal	July 2015
Ligand-Field-Dependent Behavior of Meta-GGA Exchange in Transition-Metal Complex Spin-State Ordering Ioannidis, Efthymios I.; Kulik, Heather J. The Journal of Physical Chemistry A, Vol. 121, Issue 4 https://doi.org/10.1021/acs.jpca.6b11930	journal	October 2016
Ironing out the photochemical and spin-crossover behavior of Fe(II) coordination compounds with computational chemistry Ashley, Daniel C.; Jakubikova, Elena Coordination Chemistry Reviews, Vol. 337 https://doi.org/10.1016/j.ccr.2017.02.005	journal	April 2017
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’)] Ganzenmüller, Georg; Berkaïne, Nabil; Fouqueau, Antony The Journal of Chemical Physics, Vol. 122, Issue 23 https://doi.org/10.1063/1.1927081	journal	June 2005
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations Curtarolo, Stefano; Setyawan, Wahyu; Wang, Shidong Computational Materials Science, Vol. 58 https://doi.org/10.1016/j.commatsci.2012.02.002	journal	June 2012
The AFLOW standard for high-throughput materials science calculations Calderon, Camilo E.; Plata, Jose J.; Toher, Cormac Computational Materials Science, Vol. 108 https://doi.org/10.1016/j.commatsci.2015.07.019	journal	October 2015
AFLOW: An automatic framework for high-throughput materials discovery Curtarolo, Stefano; Setyawan, Wahyu; Hart, Gus L. W. Computational Materials Science, Vol. 58 https://doi.org/10.1016/j.commatsci.2012.02.005	journal	June 2012
The atomic simulation environment—a Python library for working with atoms Hjorth Larsen, Ask; Jørgen Mortensen, Jens; Blomqvist, Jakob Journal of Physics: Condensed Matter, Vol. 29, Issue 27 https://doi.org/10.1088/1361-648X/aa680e	journal	June 2017
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis Ong, Shyue Ping; Richards, William Davidson; Jain, Anubhav Computational Materials Science, Vol. 68 https://doi.org/10.1016/j.commatsci.2012.10.028	journal	February 2013
Open Babel: An open chemical toolbox O'Boyle, Noel M.; Banck, Michael; James, Craig A. Journal of Cheminformatics, Vol. 3, Issue 1 https://doi.org/10.1186/1758-2946-3-33	journal	October 2011
The ChEMBL database in 2017 Gaulton, Anna; Hersey, Anne; Nowotka, Michał Nucleic Acids Research, Vol. 45, Issue D1 https://doi.org/10.1093/nar/gkw1074	journal	November 2016
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening Irwin, John J.; Shoichet, Brian K. Journal of Chemical Information and Modeling, Vol. 45, Issue 1 https://doi.org/10.1021/ci049714+	journal	December 2004
molSimplify: A toolkit for automating discovery in inorganic chemistry Ioannidis, Efthymios I.; Gani, Terry Z. H.; Kulik, Heather J. Journal of Computational Chemistry, Vol. 37, Issue 22 https://doi.org/10.1002/jcc.24437	journal	July 2016
Computational Discovery of Hydrogen Bond Design Rules for Electrochemical Ion Separation Gani, Terry Z. H.; Ioannidis, Efthymios I.; Kulik, Heather J. Chemistry of Materials, Vol. 28, Issue 17 https://doi.org/10.1021/acs.chemmater.6b02378	journal	August 2016
Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design Janet, Jon Paul; Gani, Terry Z. H.; Steeves, Adam H. Industrial & Engineering Chemistry Research, Vol. 56, Issue 17 https://doi.org/10.1021/acs.iecr.7b00808	journal	April 2017
Harnessing Organic Ligand Libraries for First-Principles Inorganic Discovery: Indium Phosphide Quantum Dot Precursor Design Strategies Kim, Jeong Yun; Steeves, Adam H.; Kulik, Heather J. Chemistry of Materials, Vol. 29, Issue 8 https://doi.org/10.1021/acs.chemmater.7b00472	journal	April 2017
Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations Bleiziffer, Patrick; Schaller, Kay; Riniker, Sereina Journal of Chemical Information and Modeling, Vol. 58, Issue 3 https://doi.org/10.1021/acs.jcim.7b00663	journal	February 2018
Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations Li, Zheng; Omidvar, Noushin; Chin, Wei Shan The Journal of Physical Chemistry A, Vol. 122, Issue 18 https://doi.org/10.1021/acs.jpca.8b02842	journal	April 2018
Machine learning for the structure–energy–property landscapes of molecular crystals Musil, Félix; De, Sandip; Yang, Jack Chemical Science, Vol. 9, Issue 5 https://doi.org/10.1039/C7SC04665K	journal	January 2018
SchNet – A deep learning architecture for molecules and materials Schütt, K. T.; Sauceda, H. E.; Kindermans, P. -J. The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5019779	journal	June 2018
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces Behler, Jörg; Parrinello, Michele Physical Review Letters, Vol. 98, Issue 14 https://doi.org/10.1103/PhysRevLett.98.146401	journal	April 2007
Perspective: Machine learning potentials for atomistic simulations Behler, Jörg The Journal of Chemical Physics, Vol. 145, Issue 17 https://doi.org/10.1063/1.4966192	journal	November 2016
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost Smith, J. S.; Isayev, O.; Roitberg, A. E. Chemical Science, Vol. 8, Issue 4 https://doi.org/10.1039/C6SC05720A	journal	January 2017
Machine learning in materials informatics: recent applications and prospects Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam npj Computational Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s41524-017-0056-5	journal	December 2017
Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D. Nature Materials, Vol. 15, Issue 10 https://doi.org/10.1038/nmat4717	journal	August 2016
Machine learning in catalysis Kitchin, John R. Nature Catalysis, Vol. 1, Issue 4 https://doi.org/10.1038/s41929-018-0056-y	journal	April 2018
Machine learning for heterogeneous catalyst design and discovery Goldsmith, Bryan R.; Esterhuizen, Jacques; Liu, Jin-Xun AIChE Journal, Vol. 64, Issue 7 https://doi.org/10.1002/aic.16198	journal	May 2018
Machine learning-based screening of complex molecules for polymer solar cells Jørgensen, Peter Bjørn; Mesta, Murat; Shil, Suranjan The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5023563	journal	June 2018
Multi-fidelity machine learning models for accurate bandgap predictions of solids Pilania, G.; Gubernatis, J. E.; Lookman, T. Computational Materials Science, Vol. 129 https://doi.org/10.1016/j.commatsci.2016.12.004	journal	March 2017
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias Journal of Chemical Theory and Computation, Vol. 11, Issue 5 https://doi.org/10.1021/acs.jctc.5b00099	journal	April 2015
Assessment of density functional theory for iron(II) molecules across the spin-crossover transition Droghetti, A.; Alfè, D.; Sanvito, S. The Journal of Chemical Physics, Vol. 137, Issue 12 https://doi.org/10.1063/1.4752411	journal	September 2012
Spin Propensities of Octahedral Complexes From Density Functional Theory Mortensen, Sara R.; Kepp, Kasper P. The Journal of Physical Chemistry A, Vol. 119, Issue 17 https://doi.org/10.1021/acs.jpca.5b01626	journal	April 2015
Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships Janet, Jon Paul; Kulik, Heather J. The Journal of Physical Chemistry A, Vol. 121, Issue 46 https://doi.org/10.1021/acs.jpca.7b08750	journal	November 2017
Simulated evolution of fluorophores for light emitting diodes Shu, Yinan; Levine, Benjamin G. The Journal of Chemical Physics, Vol. 142, Issue 10 https://doi.org/10.1063/1.4914294	journal	March 2015
Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network Janet, Jon Paul; Chan, Lydia; Kulik, Heather J. The Journal of Physical Chemistry Letters, Vol. 9, Issue 5 https://doi.org/10.1021/acs.jpclett.8b00170	journal	February 2018
General Approach to Estimate Error Bars for Quantitative Structure–Activity Relationship Predictions of Molecular Activity Liu, Ruifeng; Glover, Kyle P.; Feasel, Michael G. Journal of Chemical Information and Modeling, Vol. 58, Issue 8 https://doi.org/10.1021/acs.jcim.8b00114	journal	June 2018
Stochastic Voyages into Uncharted Chemical Space Produce a Representative Library of All Possible Drug-Like Compounds Virshup, Aaron M.; Contreras-García, Julia; Wipf, Peter Journal of the American Chemical Society, Vol. 135, Issue 19 https://doi.org/10.1021/ja401184g	journal	May 2013
Prediction of Partition Coefficients (LOGP_oct) Using Autocorrelation Descriptors Devillers, J.; Domine, D.; Guillon, C. SAR and QSAR in Environmental Research, Vol. 7, Issue 1-4 https://doi.org/10.1080/10629369708039129	journal	December 1997
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert Physical Review Letters, Vol. 108, Issue 5 https://doi.org/10.1103/PhysRevLett.108.058301	journal	January 2012
Quantum chemistry structures and properties of 134 kilo molecules Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias Scientific Data, Vol. 1, Issue 1 https://doi.org/10.1038/sdata.2014.22	journal	August 2014
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules Smith, Justin S.; Isayev, Olexandr; Roitberg, Adrian E. Scientific Data, Vol. 4, Issue 1 https://doi.org/10.1038/sdata.2017.193	journal	December 2017
Frontier molecular orbital theory of cycloaddition reactions Houk, Kendall N. Accounts of Chemical Research, Vol. 8, Issue 11 https://doi.org/10.1021/ar50095a001	journal	November 1975
A Molecular Orbital Theory of Reactivity in Aromatic Hydrocarbons Fukui, Kenichi; Yonezawa, Teijiro; Shingu, Haruo The Journal of Chemical Physics, Vol. 20, Issue 4 https://doi.org/10.1063/1.1700523	journal	April 1952
Electronics using hybrid-molecular and mono-molecular devices Joachim, C.; Gimzewski, J. K.; Aviram, A. Nature, Vol. 408, Issue 6812, p. 541-548 https://doi.org/10.1038/35046000	journal	November 2000
Molecular Design of Photovoltaic Materials for Polymer Solar Cells: Toward Suitable Electronic Energy Levels and Broad Absorption Li, Yongfang Accounts of Chemical Research, Vol. 45, Issue 5 https://doi.org/10.1021/ar2002446	journal	January 2012
Comparison of DFT Methods for Molecular Orbital Eigenvalue Calculations Zhang, Gang; Musgrave, Charles B. The Journal of Physical Chemistry A, Vol. 111, Issue 8 https://doi.org/10.1021/jp061633o	journal	March 2007
Understanding band gaps of solids in generalized Kohn–Sham theory Perdew, John P.; Yang, Weitao; Burke, Kieron Proceedings of the National Academy of Sciences, Vol. 114, Issue 11 https://doi.org/10.1073/pnas.1621352114	journal	March 2017
Orbital-dependent density functionals: Theory and applications Kümmel, Stephan; Kronik, Leeor Reviews of Modern Physics, Vol. 80, Issue 1 https://doi.org/10.1103/RevModPhys.80.3	journal	January 2008
A solution for the best rotation to relate two sets of vectors Kabsch, W. Acta Crystallographica Section A, Vol. 32, Issue 5 https://doi.org/10.1107/S0567739476001873	journal	September 1976
Density functional theory for modelling large molecular adsorbate–surface interactions: a mini-review and worked example Janet, Jon Paul; Zhao, Qing; Ioannidis, Efthymios I. Molecular Simulation, Vol. 43, Issue 5-6 https://doi.org/10.1080/08927022.2016.1258465	journal	November 2016
Less is more: Sampling chemical space with active learning Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5023802	journal	June 2018
Quantum Chemistry on Graphical Processing Units. 3. Analytical Energy Gradients, Geometry Optimization, and First Principles Molecular Dynamics Ufimtsev, Ivan S.; Martinez, Todd J. Journal of Chemical Theory and Computation, Vol. 5, Issue 10 https://doi.org/10.1021/ct9003004	journal	August 2009
Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields Stephens, P. J.; Devlin, F. J.; Chabalowski, C. F. The Journal of Physical Chemistry, Vol. 98, Issue 45, p. 11623-11627 https://doi.org/10.1021/j100096a001	journal	November 1994
Density‐functional thermochemistry. III. The role of exact exchange Becke, Axel D. The Journal of Chemical Physics, Vol. 98, Issue 7, p. 5648-5652 https://doi.org/10.1063/1.464913	journal	April 1993
Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density Lee, Chengteh; Yang, Weitao; Parr, Robert G. Physical Review B, Vol. 37, Issue 2 https://doi.org/10.1103/PhysRevB.37.785	journal	January 1988
Ab initio effective core potentials for molecular calculations. Potentials for the transition metal atoms Sc to Hg Hay, P. Jeffrey; Wadt, Willard R. The Journal of Chemical Physics, Vol. 82, Issue 1 https://doi.org/10.1063/1.448799	journal	January 1985
A ?Level-Shifting? method for converging closed shell Hartree-Fock wave functions Saunders, V. R.; Hillier, I. H. International Journal of Quantum Chemistry, Vol. 7, Issue 4 https://doi.org/10.1002/qua.560070407	journal	July 1973
Fractional charge perspective on the band gap in density-functional theory Cohen, Aron J.; Mori-Sánchez, Paula; Yang, Weitao Physical Review B, Vol. 77, Issue 11 https://doi.org/10.1103/PhysRevB.77.115123	journal	March 2008
Physical Content of the Exact Kohn-Sham Orbital Energies: Band Gaps and Derivative Discontinuities Perdew, John P.; Levy, Mel Physical Review Letters, Vol. 51, Issue 20 https://doi.org/10.1103/PhysRevLett.51.1884	journal	November 1983
On representing chemical environments Bartók, Albert P.; Kondor, Risi; Csányi, Gábor Physical Review B, Vol. 87, Issue 18 https://doi.org/10.1103/PhysRevB.87.184115	journal	May 2013
Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity Huang, Bing; von Lilienfeld, O. Anatole The Journal of Chemical Physics, Vol. 145, Issue 16 https://doi.org/10.1063/1.4964627	journal	October 2016
Big Data of Materials Science: Critical Role of the Descriptor Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V. Physical Review Letters, Vol. 114, Issue 10 https://doi.org/10.1103/PhysRevLett.114.105503	journal	March 2015
A Shape Index from Molecular Graphs Kier, Lemont B. Quantitative Structure-Activity Relationships, Vol. 4, Issue 3 https://doi.org/10.1002/qsar.19850040303	journal	January 1985
The Elements of Statistical Learning Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome Springer Series in Statistics https://doi.org/10.1007/978-0-387-84858-7	book	January 2009
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms Bergstra, James; Yamins, Dan; Cox, David Proceedings of the Python in Science Conference https://doi.org/10.25080/Majora-8b375195-003	conference	January 2013
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001

Similar Records

Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles

Journal Article · Wed Oct 13 00:00:00 EDT 2021 · Chemical Science · OSTI ID:1612842

Duan, Chenru; Chen, Shuxin; Taylor, Michael G.; +2 more

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

Journal Article · Thu Feb 15 00:00:00 EST 2018 · Journal of Physical Chemistry Letters · OSTI ID:1612842

Janet, Jon Paul; Chan, Lydia; Kulik, Heather J.

Representations and strategies for transferable machine learning improve model performance in chemical discovery

Journal Article · Tue Feb 15 00:00:00 EST 2022 · Journal of Chemical Physics · OSTI ID:1612842

Harper, Daniel R.; Nandy, Aditya; Arunachalam, Naveen; +3 more

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Title: Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Citation Formats

References (84)

Similar Records

Related Subjects