skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Making machine learning a useful tool in the accelerated discovery of transition metal complexes

Authors:
ORCiD logo [1]
  1. Department of Chemical EngineeringMassachusetts Institute of Technology Cambridge Massachusetts
Publication Date:
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1546100
Grant/Contract Number:  
DE‐SC0018096
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Wiley Interdisciplinary Reviews: Computational Molecular Science
Additional Journal Information:
Journal Name: Wiley Interdisciplinary Reviews: Computational Molecular Science Journal Volume: 10 Journal Issue: 1; Journal ID: ISSN 1759-0876
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United States
Language:
English

Citation Formats

Kulik, Heather J. Making machine learning a useful tool in the accelerated discovery of transition metal complexes. United States: N. p., 2019. Web. doi:10.1002/wcms.1439.
Kulik, Heather J. Making machine learning a useful tool in the accelerated discovery of transition metal complexes. United States. doi:10.1002/wcms.1439.
Kulik, Heather J. Thu . "Making machine learning a useful tool in the accelerated discovery of transition metal complexes". United States. doi:10.1002/wcms.1439.
@article{osti_1546100,
title = {Making machine learning a useful tool in the accelerated discovery of transition metal complexes},
author = {Kulik, Heather J.},
abstractNote = {},
doi = {10.1002/wcms.1439},
journal = {Wiley Interdisciplinary Reviews: Computational Molecular Science},
number = 1,
volume = 10,
place = {United States},
year = {2019},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1002/wcms.1439

Citation Metrics:
Cited by: 6 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Do CCSD and approximate CCSD-F12 variants converge to the same basis set limits? The case of atomization energies
journal, October 2018

  • Kesharwani, Manoj K.; Sylvetsky, Nitai; Köhn, Andreas
  • The Journal of Chemical Physics, Vol. 149, Issue 15
  • DOI: 10.1063/1.5048665

MoleculeNet: a benchmark for molecular machine learning
journal, January 2018

  • Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N.
  • Chemical Science, Vol. 9, Issue 2
  • DOI: 10.1039/C7SC02664A

UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations
journal, December 1992

  • Rappe, A. K.; Casewit, C. J.; Colwell, K. S.
  • Journal of the American Chemical Society, Vol. 114, Issue 25, p. 10024-10035
  • DOI: 10.1021/ja00051a040

Representing potential energy surfaces by high-dimensional neural network potentials
journal, April 2014


The Shrinking World of Innocent Ligands: Conventionaland Non-Conventional Redox-Active Ligands
journal, January 2012


Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation
journal, January 2008

  • Ufimtsev, Ivan S.; Martínez, Todd J.
  • Journal of Chemical Theory and Computation, Vol. 4, Issue 2
  • DOI: 10.1021/ct700268q

Density functional theory embedding for correlated wavefunctions: Improved methods for open-shell systems and transition metal complexes
journal, December 2012

  • Goodpaster, Jason D.; Barnes, Taylor A.; Manby, Frederick R.
  • The Journal of Chemical Physics, Vol. 137, Issue 22
  • DOI: 10.1063/1.4770226

Nudged elastic band calculations accelerated with Gaussian process regression
journal, October 2017

  • Koistinen, Olli-Pekka; Dagbjartsdóttir, Freyja B.; Ásgeirsson, Vilhjálmur
  • The Journal of Chemical Physics, Vol. 147, Issue 15
  • DOI: 10.1063/1.4986787

Porphyrin-Sensitized Solar Cells with Cobalt (II/III)-Based Redox Electrolyte Exceed 12 Percent Efficiency
journal, November 2011


Machine learning for heterogeneous catalyst design and discovery
journal, May 2018

  • Goldsmith, Bryan R.; Esterhuizen, Jacques; Liu, Jin-Xun
  • AIChE Journal, Vol. 64, Issue 7
  • DOI: 10.1002/aic.16198

Auxiliary basis sets to approximate Coulomb potentials
journal, June 1995


ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
journal, December 2017

  • Smith, Justin S.; Isayev, Olexandr; Roitberg, Adrian E.
  • Scientific Data, Vol. 4, Issue 1
  • DOI: 10.1038/sdata.2017.193

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
journal, June 2015

  • Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan
  • The Journal of Physical Chemistry Letters, Vol. 6, Issue 12
  • DOI: 10.1021/acs.jpclett.5b00831

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
journal, October 2017

  • Faber, Felix A.; Hutchison, Luke; Huang, Bing
  • Journal of Chemical Theory and Computation, Vol. 13, Issue 11
  • DOI: 10.1021/acs.jctc.7b00577

Less is more: Sampling chemical space with active learning
journal, June 2018

  • Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5023802

Machine learning in catalysis
journal, April 2018


Predicting electronic structure properties of transition metal complexes with neural networks
journal, January 2017

  • Janet, Jon Paul; Kulik, Heather J.
  • Chemical Science, Vol. 8, Issue 7
  • DOI: 10.1039/C7SC01247K

Perspective: Treating electron over-delocalization with the DFT+U method
journal, June 2015

  • Kulik, Heather J.
  • The Journal of Chemical Physics, Vol. 142, Issue 24
  • DOI: 10.1063/1.4922693

Machine-Learning-Augmented Chemisorption Model for CO 2 Electroreduction Catalyst Screening
journal, August 2015

  • Ma, Xianfeng; Li, Zheng; Achenie, Luke E. K.
  • The Journal of Physical Chemistry Letters, Vol. 6, Issue 18
  • DOI: 10.1021/acs.jpclett.5b01660

Accurate Modeling of Organic Molecular Crystals by Dispersion-Corrected Density Functional Tight Binding (DFTB)
journal, May 2014

  • Brandenburg, Jan Gerit; Grimme, Stefan
  • The Journal of Physical Chemistry Letters, Vol. 5, Issue 11
  • DOI: 10.1021/jz500755u

SchNet – A deep learning architecture for molecules and materials
journal, June 2018

  • Schütt, K. T.; Sauceda, H. E.; Kindermans, P. -J.
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5019779

molSimplify: A toolkit for automating discovery in inorganic chemistry
journal, July 2016

  • Ioannidis, Efthymios I.; Gani, Terry Z. H.; Kulik, Heather J.
  • Journal of Computational Chemistry, Vol. 37, Issue 22
  • DOI: 10.1002/jcc.24437

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
journal, November 2018

  • Musil, Félix; Willatt, Michael J.; Langovoy, Mikhail A.
  • Journal of Chemical Theory and Computation, Vol. 15, Issue 2
  • DOI: 10.1021/acs.jctc.8b00959

Computational studies of the O2-evolving complex of photosystem II and biomimetic oxomanganese complexes
journal, February 2008


Unifying Exchange Sensitivity in Transition-Metal Spin-State Ordering and Catalysis through Bond Valence Metrics
journal, October 2017

  • Gani, Terry Z. H.; Kulik, Heather J.
  • Journal of Chemical Theory and Computation, Vol. 13, Issue 11
  • DOI: 10.1021/acs.jctc.7b00848

Harnessing Organic Ligand Libraries for First-Principles Inorganic Discovery: Indium Phosphide Quantum Dot Precursor Design Strategies
journal, April 2017


Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
journal, February 2018

  • Janet, Jon Paul; Chan, Lydia; Kulik, Heather J.
  • The Journal of Physical Chemistry Letters, Vol. 9, Issue 5
  • DOI: 10.1021/acs.jpclett.8b00170

Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning
journal, October 2017


Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
journal, January 2018

  • Gómez-Bombarelli, Rafael; Wei, Jennifer N.; Duvenaud, David
  • ACS Central Science, Vol. 4, Issue 2
  • DOI: 10.1021/acscentsci.7b00572

Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations
journal, February 2018

  • Bleiziffer, Patrick; Schaller, Kay; Riniker, Sereina
  • Journal of Chemical Information and Modeling, Vol. 58, Issue 3
  • DOI: 10.1021/acs.jcim.7b00663

A simple DFT-based diagnostic for nondynamical correlation
journal, December 2012

  • Fogueri, Uma R.; Kozuch, Sebastian; Karton, Amir
  • Theoretical Chemistry Accounts, Vol. 132, Issue 1
  • DOI: 10.1007/s00214-012-1291-y

Perspective: Multireference coupled cluster theories of dynamical electron correlation
journal, July 2018

  • Evangelista, Francesco A.
  • The Journal of Chemical Physics, Vol. 149, Issue 3
  • DOI: 10.1063/1.5039496

Discovering a Transferable Charge Assignment Model Using Machine Learning
journal, July 2018

  • Sifain, Andrew E.; Lubbers, Nicholas; Nebgen, Benjamin T.
  • The Journal of Physical Chemistry Letters, Vol. 9, Issue 16
  • DOI: 10.1021/acs.jpclett.8b01939

A quantitative uncertainty metric controls error in neural network-driven chemical discovery
journal, January 2019

  • Janet, Jon Paul; Duan, Chenru; Yang, Tzuhsiung
  • Chemical Science, Vol. 10, Issue 34
  • DOI: 10.1039/C9SC02298H

Electrocatalytic Hydrogen Evolution under Acidic Aqueous Conditions and Mechanistic Studies of a Highly Stable Molecular Catalyst
journal, July 2016

  • Tsay, Charlene; Yang, Jenny Y.
  • Journal of the American Chemical Society, Vol. 138, Issue 43
  • DOI: 10.1021/jacs.6b05851

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
journal, January 2012


Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals
journal, April 2017


Communication: Evaluating non-empirical double hybrid functionals for spin-state energetics in transition-metal complexes
journal, January 2018

  • Wilbraham, Liam; Adamo, Carlo; Ciofini, Ilaria
  • The Journal of Chemical Physics, Vol. 148, Issue 4
  • DOI: 10.1063/1.5019641

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
journal, November 2017

  • Janet, Jon Paul; Kulik, Heather J.
  • The Journal of Physical Chemistry A, Vol. 121, Issue 46
  • DOI: 10.1021/acs.jpca.7b08750

Quantum Machine Learning in Chemical Compound Space
journal, March 2018

  • von Lilienfeld, O. Anatole
  • Angewandte Chemie International Edition, Vol. 57, Issue 16
  • DOI: 10.1002/anie.201709686

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
journal, January 2018

  • Yao, Kun; Herr, John E.; Toth, David W.
  • Chemical Science, Vol. 9, Issue 8
  • DOI: 10.1039/C7SC04934J

Catalytic Functionalization of C(sp 2 )H and C(sp 3 )H Bonds by Using Bidentate Directing Groups
journal, September 2013

  • Rouquet, Guy; Chatani, Naoto
  • Angewandte Chemie International Edition, Vol. 52, Issue 45
  • DOI: 10.1002/anie.201301451

Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
journal, July 2017

  • Coley, Connor W.; Barzilay, Regina; Green, William H.
  • Journal of Chemical Information and Modeling, Vol. 57, Issue 8
  • DOI: 10.1021/acs.jcim.6b00601

When Hartree-Fock exchange admixture lowers DFT-predicted barrier heights: Natural bond orbital analyses and implications for catalysis
journal, June 2018

  • Mahler, Andrew; Janesko, Benjamin G.; Moncho, Salvador
  • The Journal of Chemical Physics, Vol. 148, Issue 24
  • DOI: 10.1063/1.5032218

Understanding and Breaking Scaling Relations in Single-Site Catalysis: Methane to Methanol Conversion by Fe IV ═O
journal, January 2018


Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density
journal, January 1988


Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
journal, March 2019

  • Duan, Chenru; Janet, Jon Paul; Liu, Fang
  • Journal of Chemical Theory and Computation, Vol. 15, Issue 4
  • DOI: 10.1021/acs.jctc.9b00057

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
journal, April 2015

  • Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias
  • Journal of Chemical Theory and Computation, Vol. 11, Issue 5
  • DOI: 10.1021/acs.jctc.5b00099

Phosphorescent Nanocluster Light-Emitting Diodes
journal, November 2015

  • Kuttipillai, Padmanaban S.; Zhao, Yimu; Traverse, Christopher J.
  • Advanced Materials, Vol. 28, Issue 2
  • DOI: 10.1002/adma.201504548

Blue-Light Emission of Cu(I) Complexes and Singlet Harvesting
journal, September 2011

  • Czerwieniec, Rafał; Yu, Jiangbo; Yersin, Hartmut
  • Inorganic Chemistry, Vol. 50, Issue 17
  • DOI: 10.1021/ic200811a

Multi-fidelity machine learning models for accurate bandgap predictions of solids
journal, March 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’)]
journal, June 2005

  • Ganzenmüller, Georg; Berkaïne, Nabil; Fouqueau, Antony
  • The Journal of Chemical Physics, Vol. 122, Issue 23
  • DOI: 10.1063/1.1927081

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007


DFTB3: Extension of the Self-Consistent-Charge Density-Functional Tight-Binding Method (SCC-DFTB)
journal, March 2011

  • Gaus, Michael; Cui, Qiang; Elstner, Marcus
  • Journal of Chemical Theory and Computation, Vol. 7, Issue 4
  • DOI: 10.1021/ct100684s

Hierarchical visualization of materials space with graph convolutional neural networks
journal, November 2018

  • Xie, Tian; Grossman, Jeffrey C.
  • The Journal of Chemical Physics, Vol. 149, Issue 17
  • DOI: 10.1063/1.5047803

Assessment of density functional theory for iron(II) molecules across the spin-crossover transition
journal, September 2012

  • Droghetti, A.; Alfè, D.; Sanvito, S.
  • The Journal of Chemical Physics, Vol. 137, Issue 12
  • DOI: 10.1063/1.4752411

A Survey on Transfer Learning
journal, October 2010

  • Pan, Sinno Jialin; Yang, Qiang
  • IEEE Transactions on Knowledge and Data Engineering, Vol. 22, Issue 10
  • DOI: 10.1109/TKDE.2009.191

Brightly Blue and Green Emitting Cu(I) Dimers for Singlet Harvesting in OLEDs
journal, May 2013

  • Leitl, Markus J.; Küchle, Fritz-Robert; Mayer, Hermann A.
  • The Journal of Physical Chemistry A, Vol. 117, Issue 46
  • DOI: 10.1021/jp402975d

Machine learning molecular dynamics for the simulation of infrared spectra
journal, January 2017

  • Gastegger, Michael; Behler, Jörg; Marquetand, Philipp
  • Chemical Science, Vol. 8, Issue 10
  • DOI: 10.1039/C7SC02267K

Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields
journal, November 1994

  • Stephens, P. J.; Devlin, F. J.; Chabalowski, C. F.
  • The Journal of Physical Chemistry, Vol. 98, Issue 45, p. 11623-11627
  • DOI: 10.1021/j100096a001

Unsupervised machine learning in atomistic simulations, between predictions and understanding
journal, April 2019

  • Ceriotti, Michele
  • The Journal of Chemical Physics, Vol. 150, Issue 15
  • DOI: 10.1063/1.5091842

Machine learning for molecular and materials science
journal, July 2018


Gaussian process regression for geometry optimization
journal, March 2018

  • Denzel, Alexander; Kästner, Johannes
  • The Journal of Chemical Physics, Vol. 148, Issue 9
  • DOI: 10.1063/1.5017103

SBH10: A Benchmark Database of Barrier Heights on Transition Metal Surfaces
journal, September 2017

  • Mallikarjun Sharada, Shaama; Bligaard, Thomas; Luntz, Alan C.
  • The Journal of Physical Chemistry C, Vol. 121, Issue 36
  • DOI: 10.1021/acs.jpcc.7b05677

Density Functional Theory in Transition-Metal Chemistry: A Self-Consistent Hubbard U Approach
journal, September 2006


Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
journal, February 2013


Solar Synthesis: Prospects in Visible Light Photocatalysis
journal, February 2014


Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
journal, January 2018

  • Meyer, Benjamin; Sawatlon, Boodsarin; Heinen, Stefan
  • Chemical Science, Vol. 9, Issue 35
  • DOI: 10.1039/C8SC01949E

Neural Networks in Chemistry
journal, April 1993

  • Gasteiger, Johann; Zupan, Jure
  • Angewandte Chemie International Edition in English, Vol. 32, Issue 4
  • DOI: 10.1002/anie.199305031

Tensor hypercontraction density fitting. I. Quartic scaling second- and third-order Møller-Plesset perturbation theory
journal, July 2012

  • Hohenstein, Edward G.; Parrish, Robert M.; Martínez, Todd J.
  • The Journal of Chemical Physics, Vol. 137, Issue 4
  • DOI: 10.1063/1.4732310

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
journal, January 2017

  • Smith, J. S.; Isayev, O.; Roitberg, A. E.
  • Chemical Science, Vol. 8, Issue 4
  • DOI: 10.1039/C6SC05720A

Low-order scaling local electron correlation methods. I. Linear scaling local MP2
journal, October 1999

  • Schütz, Martin; Hetzer, Georg; Werner, Hans-Joachim
  • The Journal of Chemical Physics, Vol. 111, Issue 13
  • DOI: 10.1063/1.479957

How Much Can Density Functional Approximations (DFA) Fail? The Extreme Case of the FeO 4 Species
journal, March 2016

  • Huang, Wei; Xing, Deng-Hui; Lu, Jun-Bo
  • Journal of Chemical Theory and Computation, Vol. 12, Issue 4
  • DOI: 10.1021/acs.jctc.5b01040

Visible Light Photoredox Catalysis with Transition Metal Complexes: Applications in Organic Synthesis
journal, March 2013

  • Prier, Christopher K.; Rankic, Danica A.; MacMillan, David W. C.
  • Chemical Reviews, Vol. 113, Issue 7
  • DOI: 10.1021/cr300503r

The atomic simulation environment—a Python library for working with atoms
journal, June 2017

  • Hjorth Larsen, Ask; Jørgen Mortensen, Jens; Blomqvist, Jakob
  • Journal of Physics: Condensed Matter, Vol. 29, Issue 27
  • DOI: 10.1088/1361-648X/aa680e

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
journal, March 2019


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
journal, January 2006

  • Jurečka, Petr; Šponer, Jiří; Černý, Jiří
  • Physical Chemistry Chemical Physics, Vol. 8, Issue 17, p. 1985-1993
  • DOI: 10.1039/B600027D

Density functional theory for transition metals and transition metal chemistry
journal, January 2009

  • Cramer, Christopher J.; Truhlar, Donald G.
  • Physical Chemistry Chemical Physics, Vol. 11, Issue 46
  • DOI: 10.1039/b907148b

Computational Discovery of Hydrogen Bond Design Rules for Electrochemical Ion Separation
journal, August 2016


Linear scaling computation of the Fock matrix
journal, April 1997

  • Challacombe, Matt; Schwegler, Eric
  • The Journal of Chemical Physics, Vol. 106, Issue 13
  • DOI: 10.1063/1.473575

Ligand-Field-Dependent Behavior of Meta-GGA Exchange in Transition-Metal Complex Spin-State Ordering
journal, October 2016

  • Ioannidis, Efthymios I.; Kulik, Heather J.
  • The Journal of Physical Chemistry A, Vol. 121, Issue 4
  • DOI: 10.1021/acs.jpca.6b11930

Reduced scaling CASPT2 using supporting subspaces and tensor hyper-contraction
journal, July 2018

  • Song, Chenchen; Martínez, Todd J.
  • The Journal of Chemical Physics, Vol. 149, Issue 4
  • DOI: 10.1063/1.5037283

Triplet Harvesting with 100% Efficiency by Way of Thermally Activated Delayed Fluorescence in Charge Transfer OLED Emitters
journal, May 2013

  • Dias, Fernando B.; Bourdakos, Konstantinos N.; Jankus, Vygintas
  • Advanced Materials, Vol. 25, Issue 27
  • DOI: 10.1002/adma.201300753

Synthesis, Structure, and Characterization of Dinuclear Copper(I) Halide Complexes with P^N Ligands Featuring Exciting Photoluminescence Properties
journal, October 2012

  • Zink, Daniel M.; Bächle, Michael; Baumann, Thomas
  • Inorganic Chemistry, Vol. 52, Issue 5
  • DOI: 10.1021/ic300979c

Calculation of Ligand Dissociation Energies in Large Transition-Metal Complexes
journal, March 2018

  • Husch, Tamara; Freitag, Leon; Reiher, Markus
  • Journal of Chemical Theory and Computation, Vol. 14, Issue 5
  • DOI: 10.1021/acs.jctc.8b00061

Addressing uncertainty in atomistic machine learning
journal, January 2017

  • Peterson, Andrew A.; Christensen, Rune; Khorshidi, Alireza
  • Physical Chemistry Chemical Physics, Vol. 19, Issue 18
  • DOI: 10.1039/C7CP00375G

A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians
journal, October 2018

  • Li, Haichen; Collins, Christopher; Tanha, Matteus
  • Journal of Chemical Theory and Computation, Vol. 14, Issue 11
  • DOI: 10.1021/acs.jctc.8b00873

Towards quantifying the role of exact exchange in predictions of transition metal complex properties
journal, July 2015

  • Ioannidis, Efthymios I.; Kulik, Heather J.
  • The Journal of Chemical Physics, Vol. 143, Issue 3
  • DOI: 10.1063/1.4926836

Machine learning in materials informatics: recent applications and prospects
journal, December 2017

  • Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam
  • npj Computational Materials, Vol. 3, Issue 1
  • DOI: 10.1038/s41524-017-0056-5

The ligand field molecular mechanics model and the stereoelectronic effects of d and s electrons
journal, February 2001


Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis
journal, July 2018

  • Welborn, Matthew; Cheng, Lixue; Miller, Thomas F.
  • Journal of Chemical Theory and Computation, Vol. 14, Issue 9
  • DOI: 10.1021/acs.jctc.8b00636

Ironing out the photochemical and spin-crossover behavior of Fe(II) coordination compounds with computational chemistry
journal, April 2017


Auxiliary basis sets for main row atoms and transition metals and their use to approximate Coulomb potentials
journal, October 1997

  • Eichkorn, Karin; Weigend, Florian; Treutler, Oliver
  • Theoretical Chemistry Accounts: Theory, Computation, and Modeling (Theoretica Chimica Acta), Vol. 97, Issue 1-4
  • DOI: 10.1007/s002140050244

Predicting the Band Gaps of Inorganic Solids by Machine Learning
journal, March 2018

  • Zhuo, Ya; Mansouri Tehrani, Aria; Brgoch, Jakoah
  • The Journal of Physical Chemistry Letters, Vol. 9, Issue 7
  • DOI: 10.1021/acs.jpclett.8b00124

Representations in neural network based empirical potentials
journal, July 2017

  • Cubuk, Ekin D.; Malone, Brad D.; Onat, Berk
  • The Journal of Chemical Physics, Vol. 147, Issue 2
  • DOI: 10.1063/1.4990503

Perspective: Machine learning potentials for atomistic simulations
journal, November 2016

  • Behler, Jörg
  • The Journal of Chemical Physics, Vol. 145, Issue 17
  • DOI: 10.1063/1.4966192

Development and testing of a general amber force field
journal, January 2004

  • Wang, Junmei; Wolf, Romain M.; Caldwell, James W.
  • Journal of Computational Chemistry, Vol. 25, Issue 9
  • DOI: 10.1002/jcc.20035

Multireference Character for 4d Transition Metal-Containing Molecules
journal, November 2015

  • Wang, Jiaqi; Manivasagam, Sivabalan; Wilson, Angela K.
  • Journal of Chemical Theory and Computation, Vol. 11, Issue 12
  • DOI: 10.1021/acs.jctc.5b00861

Ab Initio Reactive Computer Aided Molecular Design
journal, March 2017


Automated Construction of Molecular Active Spaces from Atomic Valence Orbitals
journal, August 2017

  • Sayfutyarova, Elvira R.; Sun, Qiming; Chan, Garnet Kin-Lic
  • Journal of Chemical Theory and Computation, Vol. 13, Issue 9
  • DOI: 10.1021/acs.jctc.7b00128

Towards operando computational modeling in heterogeneous catalysis
journal, January 2018

  • Grajciar, Lukáš; Heard, Christopher J.; Bondarenko, Anton A.
  • Chemical Society Reviews, Vol. 47, Issue 22
  • DOI: 10.1039/C8CS00398J

Computational Ligand Descriptors for Catalyst Design
journal, October 2018


Comparing molecules and solids across structural and alchemical space
journal, January 2016

  • De, Sandip; Bartók, Albert P.; Csányi, Gábor
  • Physical Chemistry Chemical Physics, Vol. 18, Issue 20
  • DOI: 10.1039/C6CP00415F

The AFLOW standard for high-throughput materials science calculations
journal, October 2015


Feed-forward neural networks in chemistry: mathematical systems for classification and pattern recognition
journal, December 1993

  • Burns, John A.; Whitesides, George M.
  • Chemical Reviews, Vol. 93, Issue 8
  • DOI: 10.1021/cr00024a001

Density‐functional thermochemistry. III. The role of exact exchange
journal, April 1993

  • Becke, Axel D.
  • The Journal of Chemical Physics, Vol. 98, Issue 7, p. 5648-5652
  • DOI: 10.1063/1.464913

Accelerating materials property predictions using machine learning
journal, September 2013

  • Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun
  • Scientific Reports, Vol. 3, Issue 1
  • DOI: 10.1038/srep02810

Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids
journal, January 1996

  • Jorgensen, William L.; Maxwell, David S.; Tirado-Rives, Julian
  • Journal of the American Chemical Society, Vol. 118, Issue 45
  • DOI: 10.1021/ja9621760

Open Babel: An open chemical toolbox
journal, October 2011

  • O'Boyle, Noel M.; Banck, Michael; James, Craig A.
  • Journal of Cheminformatics, Vol. 3, Issue 1
  • DOI: 10.1186/1758-2946-3-33

Spin-state diversity in a series of Co( ii ) PNP pincer bromide complexes
journal, January 2016

  • Shaffer, David W.; Bhowmick, Indrani; Rheingold, Arnold L.
  • Dalton Transactions, Vol. 45, Issue 44
  • DOI: 10.1039/C6DT03461F

Embedded Correlated Wavefunction Schemes: Theory and Applications
journal, May 2014

  • Libisch, Florian; Huang, Chen; Carter, Emily A.
  • Accounts of Chemical Research, Vol. 47, Issue 9
  • DOI: 10.1021/ar500086h

The performance of nonhybrid density functionals for calculating the structures and spin states of Fe(II) and Fe(III) complexes
journal, November 2004

  • Deeth, Robert J.; Fey, Natalie
  • Journal of Computational Chemistry, Vol. 25, Issue 15
  • DOI: 10.1002/jcc.20101

High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm
journal, April 2015

  • Gastegger, Michael; Marquetand, Philipp
  • Journal of Chemical Theory and Computation, Vol. 11, Issue 5
  • DOI: 10.1021/acs.jctc.5b00211

Quantum Chemistry on Graphical Processing Units. 3. Analytical Energy Gradients, Geometry Optimization, and First Principles Molecular Dynamics
journal, August 2009

  • Ufimtsev, Ivan S.; Martinez, Todd J.
  • Journal of Chemical Theory and Computation, Vol. 5, Issue 10
  • DOI: 10.1021/ct9003004

Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations
journal, October 2017

  • Wu, Jingheng; Shen, Lin; Yang, Weitao
  • The Journal of Chemical Physics, Vol. 147, Issue 16
  • DOI: 10.1063/1.5006882

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
journal, September 2018

  • Nandy, Aditya; Duan, Chenru; Janet, Jon Paul
  • Industrial & Engineering Chemistry Research, Vol. 57, Issue 42
  • DOI: 10.1021/acs.iecr.8b04015

Reduction of Systematic Uncertainty in DFT Redox Potentials of Transition-Metal Complexes
journal, March 2012

  • Konezny, Steven J.; Doherty, Mark D.; Luca, Oana R.
  • The Journal of Physical Chemistry C, Vol. 116, Issue 10
  • DOI: 10.1021/jp300485t

Tuning the Electronic Structure of Fe(II) Polypyridines via Donor Atom and Ligand Scaffold Modifications: A Computational Study
journal, August 2015


Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
journal, July 2013

  • Hansen, Katja; Montavon, Grégoire; Biegler, Franziska
  • Journal of Chemical Theory and Computation, Vol. 9, Issue 8
  • DOI: 10.1021/ct400195d

Density functional theory for modelling large molecular adsorbate–surface interactions: a mini-review and worked example
journal, November 2016


AFLOW: An automatic framework for high-throughput materials discovery
journal, June 2012


Multiconfiguration Pair-Density Functional Theory for Iron Porphyrin with CAS, RAS, and DMRG Active Spaces
journal, February 2019

  • Zhou, Chen; Gagliardi, Laura; Truhlar, Donald G.
  • The Journal of Physical Chemistry A, Vol. 123, Issue 15
  • DOI: 10.1021/acs.jpca.8b12479

Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements
journal, September 2007


Application of Semiempirical Methods to Transition Metal Complexes: Fast Results but Hard-to-Predict Accuracy
journal, May 2018

  • Minenkov, Yury; Sharapa, Dmitry I.; Cavallo, Luigi
  • Journal of Chemical Theory and Computation, Vol. 14, Issue 7
  • DOI: 10.1021/acs.jctc.8b00018

SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
journal, February 1988

  • Weininger, David
  • Journal of Chemical Information and Modeling, Vol. 28, Issue 1
  • DOI: 10.1021/ci00057a005

The promise of artificial intelligence in chemical engineering: Is it here, finally?
journal, December 2018


Ruthenium(II)-Catalyzed C–H Bond Activation and Functionalization
journal, August 2012

  • Arockiam, Percia Beatrice; Bruneau, Christian; Dixneuf, Pierre H.
  • Chemical Reviews, Vol. 112, Issue 11
  • DOI: 10.1021/cr300153j

Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities
journal, October 2018

  • Vogiatzis, Konstantinos D.; Polynski, Mikhail V.; Kirkland, Justin K.
  • Chemical Reviews, Vol. 119, Issue 4
  • DOI: 10.1021/acs.chemrev.8b00361

Quantum Chemistry on Graphical Processing Units. 2. Direct Self-Consistent-Field Implementation
journal, March 2009

  • Ufimtsev, Ivan S.; Martinez, Todd J.
  • Journal of Chemical Theory and Computation, Vol. 5, Issue 4
  • DOI: 10.1021/ct800526s

A Shape Index from Molecular Graphs
journal, January 1985


Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
journal, October 2016

  • Huang, Bing; von Lilienfeld, O. Anatole
  • The Journal of Chemical Physics, Vol. 145, Issue 16
  • DOI: 10.1063/1.4964627

Spin State Energetics in First-Row Transition Metal Complexes: Contribution of (3s3p) Correlation and Its Description by Second-Order Perturbation Theory
journal, January 2017

  • Pierloot, Kristine; Phung, Quan Manh; Domingo, Alex
  • Journal of Chemical Theory and Computation, Vol. 13, Issue 2
  • DOI: 10.1021/acs.jctc.6b01005

Automated Selection of Active Orbital Spaces
journal, March 2016

  • Stein, Christopher J.; Reiher, Markus
  • Journal of Chemical Theory and Computation, Vol. 12, Issue 4
  • DOI: 10.1021/acs.jctc.6b00156

Combining Linear-Scaling DFT with Subsystem DFT in Born–Oppenheimer and Ehrenfest Molecular Dynamics Simulations: From Molecules to a Virus in Solution
journal, June 2016

  • Andermatt, Samuel; Cha, Jinwoong; Schiffmann, Florian
  • Journal of Chemical Theory and Computation, Vol. 12, Issue 7
  • DOI: 10.1021/acs.jctc.6b00398

Local treatment of electron correlation in coupled cluster theory
journal, April 1996

  • Hampel, Claudia; Werner, Hans‐Joachim
  • The Journal of Chemical Physics, Vol. 104, Issue 16
  • DOI: 10.1063/1.471289

A general-purpose machine learning framework for predicting properties of inorganic materials
journal, August 2016


Quantum chemistry structures and properties of 134 kilo molecules
journal, August 2014

  • Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias
  • Scientific Data, Vol. 1, Issue 1
  • DOI: 10.1038/sdata.2014.22

Bridging the Homogeneous-Heterogeneous Divide: Modeling Spin for Reactivity in Single Atom Catalysis
journal, April 2019


Random Forests
journal, January 2001