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References (121)
-
D. Boyd (2013)
Quantum Chemistry Program Exchange, Facilitator of Theoretical and Computational Chemistry in Pre-Internet History -
Han Altae-Tran, Bharath Ramsundar, Aneesh Pappu, V. Pande (2016)
Low Data Drug Discovery with One-Shot LearningACS Central Science, 3
-
A radial distribution function description of periodic solids is adapted for ML models and applied to predict the electronic density of states for a range of materials -
Marwin Segler, M. Waller (2017)
Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction.Chemistry, 23 25
-
S. Rudy, S. Brunton, J. Proctor, J. Kutz (2016)
Data-driven discovery of partial differential equationsScience Advances, 3
-
J. Shawe-Taylor, N. Cristianini (2003)
Kernel Methods for Pattern Analysis -
O Isayev (2017)
Universal fragment descriptors for predicting electronic properties of inorganic crystalsNat. Commun., 8
-
A. Walsh, A. Sokol, R. Catlow (2013)
Computational Approaches to Energy Materials -
Sergei Kalinin, B. Sumpter, R. Archibald (2015)
Big-deep-smart data in imaging for guiding materials design.Nature materials, 14 10
-
Felix Faber, Luke Hutchison, B. Huang, J. Gilmer, S. Schoenholz, George Dahl, Oriol Vinyals, S. Kearnes, Patrick Riley, O. Lilienfeld (2017)
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.Journal of chemical theory and computation, 13 11
-
A. Harrow, A. Hassidim, S. Lloyd (2008)
Quantum algorithm for linear systems of equations.Physical review letters, 103 15
-
Yan Duan, Marcin Andrychowicz, Bradly Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, P. Abbeel, Wojciech Zaremba (2017)
One-Shot Imitation Learning -
J. Behler (2017)
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems.Angewandte Chemie, 56 42
-
B. Lake, R. Salakhutdinov, J. Tenenbaum (2015)
Human-level concept learning through probabilistic program inductionScience, 350
-
L. Rokach, O. Maimon (2010)
Classification Trees -
Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Nguyen, Stephen Ho, Jack Sloane, P. Wender, V. Pande (2017)
Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence ModelsACS Central Science, 3
-
Jerome Wicker, R. Cooper (2015)
Will it crystallise? Predicting crystallinity of molecular materialsCrystEngComm, 17
-
Michael Schmidt, Hod Lipson (2009)
Distilling Free-Form Natural Laws from Experimental DataScience, 324
-
Vincenza Dragone, Víctor Sans, Alon Henson, J. Granda, L. Cronin (2017)
An autonomous organic reaction search engine for chemical reactivityNature Communications, 8
-
V. Podgorelec, M. Zorman (2009)
Decision Trees -
M. Ziatdinov, A. Maksov, Sergei Kalinin (2017)
Learning surface molecular structures via machine visionnpj Computational Materials, 3
-
Kristof Schütt, H. Glawe, Felix Brockherde, Felix Brockherde, A. Sanna, Klaus Muller, Klaus Muller, E. Gross (2013)
How to represent crystal structures for machine learning: Towards fast prediction of electronic propertiesPhysical Review B, 89
-
Gregory Shakhnarovich, Trevor Darrell, P. Indyk (2006)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing) -
P. Dirac (1929)
Quantum Mechanics of Many-Electron SystemsProceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences, 123
-
D. Fourches, E. Muratov, A. Tropsha (2010)
Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling ResearchJournal of chemical information and modeling, 50 7
-
Ankit Agrawal, A. Choudhary (2016)
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials scienceAPL Materials, 4
-
A. Pulido, Linjiang Chen, T. Kaczorowski, D. Holden, M. Little, S. Chong, Benjamin Slater, D. McMahon, Baltasar Bonillo, C. Stackhouse, A. Stephenson, Christopher Kane, Rob Clowes, T. Hasell, A. Cooper, G. Day (2017)
Functional materials discovery using energy–structure–function mapsNature, 543
-
J. Pople (1999)
Quantum Chemical Models (Nobel Lecture).Angewandte Chemie, 38 13-14
-
J. Proudfoot (2010)
Faculty Opinions recommendation of Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. -
V. Dunjko, Jacob Taylor, H. Briegel (2016)
Quantum-enhanced machine learningPhysical review letters, 117 13
-
T. Sterling, J. Irwin (2015)
ZINC 15 – Ligand Discovery for EveryoneJournal of Chemical Information and Modeling, 55
-
K. Lejaeghere, G. Bihlmayer, T. Björkman, P. Blaha, S. Blügel, V. Blum, D. Caliste, I. Castelli, S. Clark, A. Corso, Stefano Gironcoli, T. Deutsch, J. Dewhurst, I. Marco, C. Draxl, M. Dulak, O. Eriksson, J. Flores-Livas, K. Garrity, L. Genovese, P. Giannozzi, M. Giantomassi, S. Goedecker, X. Gonze, O. Grånäs, E. Gross, A. Gulans, F. Gygi, D. Hamann, P. Hasnip, N. Holzwarth, D. Iuşan, D. Jochym, F. Jollet, Daniel Jones, G. Kresse, K. Koepernik, E. Küçükbenli, Y. Kvashnin, I. Locht, S. Lubeck, M. Marsman, N. Marzari, U. Nitzsche, L. Nordström, T. Ozaki, L. Paulatto, C. Pickard, Ward Poelmans, Matt Probert, K. Refson, M. Richter, G. Rignanese, S. Saha, M. Scheffler, M. Schlipf, K. Schwarz, Sangeeta Sharma, F. Tavazza, P. Thunström, A. Tkatchenko, M. Torrent, D. Vanderbilt, Michiel Setten, V. Speybroeck, J. Wills, J. Yates, Guo‐Xu Zhang, S. Cottenier (2016)
Reproducibility in density functional theory calculations of solidsScience, 351
-
K. Schwab (2013)
The Fourth Industrial Revolution -
A. Franceschetti, A. Zunger (1999)
The inverse band-structure problem of finding an atomic configuration with given electronic propertiesNature, 402
-
Camilo Calderon, J. Plata, C. Toher, C. Oses, O. Levy, M. Fornari, A. Natan, M. Mehl, G. Hart, M. Nardelli, S. Curtarolo (2015)
The AFLOW standard for high-throughput materials science calculationsComputational Materials Science, 108
-
M. Rupp, A. Tkatchenko, K. Müller, O. Lilienfeld (2011)
Fast and accurate modeling of molecular atomization energies with machine learning.Physical review letters, 108 5
-
P. Hohenberg, Walter Kohn (1964)
Inhomogeneous Electron GasPhysical Review, 136
-
O. Maimon, L. Rokach (2005)
Data Mining And Knowledge Discovery Handbook -
François-Xavier Coudert (2017)
Reproducible Research in Computational Chemistry of MaterialsChemistry of Materials, 29
-
E. Corey, W. Wipke (1969)
Computer-assisted design of complex organic syntheses.Science, 166 3902
-
S. Szymkuć, E. Gajewska, T. Klucznik, K. Molga, P. Dittwald, M. Startek, M. Bajczyk, B. Grzybowski (2016)
Computer-Assisted Synthetic Planning: The End of the Beginning.Angewandte Chemie, 55 20
-
A. Tropsha (2010)
Best Practices for QSAR Model Development, Validation, and ExploitationMolecular Informatics, 29
-
T. Klucznik, Barbara Mikulak-Klucznik, Michael McCormack, H. Lima, S. Szymkuć, Manishabrata Bhowmick, K. Molga, Yubai Zhou, Lindsey Rickershauser, E. Gajewska, A. Toutchkine, P. Dittwald, M. Startek, G. Kirkovits, Rafał Roszak, Ariel Adamski, Bianka Sieredzińska, M. Mrksich, Sarah Trice, B. Grzybowski (2018)
Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the LaboratoryChem, 4
-
S. Segawa (1990)
End of the beginningNature, 345
-
S. Vanstone, P. Oorschot (1989)
INTRODUCTION and FUNDAMENTALS -
C. Handley, P. Popelier (2010)
Potential energy surfaces fitted by artificial neural networks.The journal of physical chemistry. A, 114 10
-
J. Saal, S. Kirklin, Muratahan Aykol, B. Meredig, C. Wolverton (2013)
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)JOM, 65
-
T. Brun (2011)
Quantum Computing -
ML is used to fit interatomic potentials that reproduce the total energy and derivates from quantum mechanical calculations and enable accurate low-cost simulations -
S. Billinge, I. Levin (2007)
The Problem with Determining Atomic Structure at the NanoscaleScience, 316
-
D. Bonchev (1991)
Chemical Graph Theory: Introduction and Fundamentals -
R. Christensen, H. Hansen, T. Vegge (2015)
Identifying systematic DFT errors in catalytic reactionsCatalysis Science & Technology, 5
-
S. Curtarolo, G. Hart, M. Nardelli, N. Mingo, S. Sanvito, O. Levy (2013)
The high-throughput highway to computational materials design.Nature materials, 12 3
-
M. Reiher, N. Wiebe, K. Svore, D. Wecker, M. Troyer (2016)
Elucidating reaction mechanisms on quantum computersProceedings of the National Academy of Sciences, 114
-
Max Pillong, C. Marx, P. Piéchon, Jerome Wicker, R. Cooper, T. Wagner (2017)
A publicly available crystallisation data set and its application in machine learningCrystEngComm, 19
-
PAM Dirac (1929)
Quantum mechanics of many-electron systemsProc. R. Soc. Lond. A, 123
-
FA Faber, A Lindmaa, OA Lilienfeld, R Armiento (2016)
Machine learning energies of 2 million elpasolite (ABC 2 D 6) crystalsPhys. Rev. Lett., 117
-
Yawei Hui, Yaohua Liu (2017)
Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron ScienceArXiv, abs/1710.05994
-
V. Havu, V. Blum, P. Havu, M. Scheffler (2009)
Efficient O(N) integration for all-electron electronic structure calculation using numeric basis functionsJ. Comput. Phys., 228
-
A. Aspuru‐Guzik, A. Dutoi, P. Love, M. Head‐Gordon (2005)
Simulated Quantum Computation of Molecular EnergiesScience, 309
-
D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan Adams (2015)
Convolutional Networks on Graphs for Learning Molecular FingerprintsArXiv, abs/1509.09292
-
John Snyder, M. Rupp, K. Hansen, K. Müller, K. Burke (2011)
Finding Density Functionals with Machine LearningPhysical review letters, 108 25
-
Marwin Segler, M. Preuss, M. Waller (2017)
Planning chemical syntheses with deep neural networks and symbolic AINature, 555
-
C. Kuhn, D. Beratan (1996)
Inverse Strategies for Molecular DesignThe Journal of Physical Chemistry, 100
-
The study trains an ML model to predict the success of a chemical reaction, incorporating the results of uncessfull attempts rather than siimply known (sucessful) reactions -
Tudor Oprea, A. Tropsha (2006)
Target, chemical and bioactivity databases – integration is keyDrug Discovery Today: Technologies, 3
-
Felix Brockherde, Leslie Vogt, Li Li, M. Tuckerman, K. Burke, K. Müller (2016)
Bypassing the Kohn-Sham equations with machine learningNature Communications, 8
-
D. Pensak, E. Corey (1977)
LHASA—Logic and Heuristics Applied to Synthetic Analysis -
F. Legrain, J. Carrete, A. Roekeghem, G. Madsen, N. Mingo (2017)
Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus ab Initio Methods.The journal of physical chemistry. B, 122 2
-
A Walsh (2015)
The quest for new functionalityNat. Chem., 7
-
J. Biamonte, P. Wittek, Nicola Pancotti, P. Rebentrost, N. Wiebe, S. Lloyd (2016)
Quantum machine learningNature, 549
-
G. Guimaraes, Benjamín Sánchez-Lengeling, Pedro Farias, Alán Aspuru-Guzik (2017)
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation ModelsArXiv, abs/1705.10843
-
Juno Nam, Jurae Kim (2016)
Linking the Neural Machine Translation and the Prediction of Organic Chemistry ReactionsArXiv, abs/1612.09529
-
(2015)
The Master Algorithm -
A computer-driven retrosynthesis tool was trained on most published reactions in organic chemistry -
Anubhav Jain, S. Ong, G. Hautier, Wei Chen, W. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K. Persson (2013)
Commentary: The Materials Project: A materials genome approach to accelerating materials innovationAPL Materials, 1
-
A. Bartók, M. Payne, R. Kondor, Gábor Csányi (2009)
Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.Physical review letters, 104 13
-
W. Kohn, L. Sham (1965)
Self-Consistent Equations Including Exchange and Correlation EffectsPhysical Review, 140
-
I. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio (2021)
Generative adversarial networks, 12158
-
Logan Ward, Ruoqian Liu, Amar Krishna, V. Hegde, Ankit Agrawal, A. Choudhary, C. Wolverton (2017)
Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellationsPhysical Review B, 96
-
J. Carrasquilla, R. Melko (2016)
Machine learning phases of matterNature Physics, 13
-
O. Isayev, C. Oses, C. Toher, Eric Gossett, S. Curtarolo, A. Tropsha (2016)
Universal fragment descriptors for predicting properties of inorganic crystalsNature Communications, 8
-
Felix Faber, Alexander Lindmaa, O. Lilienfeld, R. Armiento (2015)
Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals.Physical review letters, 117 13
-
L. Ghiringhelli, J. Vybíral, S. Levchenko, C. Draxl, M. Scheffler (2014)
Big data of materials science: critical role of the descriptor.Physical review letters, 114 10
-
N. Kiselyova, V. Gladun, N. Vashchenko (1998)
Computational materials design using artificial intelligence methodsJournal of Alloys and Compounds, 279
-
W. Fleuren, W. Alkema (2015)
Application of text mining in the biomedical domain.Methods, 74
-
Atsuto Seko, A. Togo, I. Tanaka (2017)
Descriptors for Machine Learning of Materials DataarXiv: Materials Science
-
D. Hand, Keming Yu (2001)
Idiot's Bayes—Not So Stupid After All?International Statistical Review, 69
-
An early example of harnessing materials databases -
Karl Wilkinson, N. Hine, Chris-Kriton Skylaris (2014)
Hybrid MPI-OpenMP Parallelism in the ONETEP Linear-Scaling Electronic Structure Code: Application to the Delamination of Cellulose Nanofibrils.Journal of chemical theory and computation, 10 11
-
Stanislaw Jastrzebski, Damian Lesniak, Wojciech Czarnecki (2016)
Learning to SMILE(S)ArXiv, abs/1602.06289
-
M. Arita, D. Bowler, T. Miyazaki (2014)
Stable and Efficient Linear Scaling First-Principles Molecular Dynamics for 10000+ Atoms.Journal of chemical theory and computation, 10 12
-
J. Cole, C. Groom, Murray Read, I. Giangreco, Patrick McCabe, A. Reilly, G. Shields (2016)
Generation of crystal structures using known crystal structures as analoguesActa Crystallographica Section B, Structural Science, Crystal Engineering and Materials, 72
-
G Shakhnarovich, T Darrell, P Indyk (2005)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice -
Rafael Gómez-Bombarelli, J. Aguilera-Iparraguirre, Timothy Hirzel, D. Duvenaud, D. Maclaurin, M. Blood-Forsythe, H. Chae, Markus Einzinger, Dong-Gwang Ha, Tony Wu, Georgios Markopoulos, S. Jeon, Hosuk Kang, H. Miyazaki, Masaki Numata, Sunghan Kim, Wenliang Huang, Seong Hong, M. Baldo, Ryan Adams, Alán Aspuru-Guzik (2016)
Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach.Nature materials, 15 10
-
G. Pilania, Chenchen Wang, Xun Jiang, S. Rajasekaran, R. Ramprasad (2013)
Accelerating materials property predictions using machine learningScientific Reports, 3
-
J. Wellendorff, Keld Lundgaard, A. Møgelhøj, V. Petzold, D. Landis, J. Nørskov, T. Bligaard, K. Jacobsen (2012)
Density functionals for surface science: Exchange-correlation model development with Bayesian error estimationPhysical Review B, 85
-
N. Kireeva, I. Baskin, I. Baskin, Héléna Gaspar, D. Horvath, G. Marcou, A. Varnek (2012)
Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure‐Activity Modeling and Dataset ComparisonMolecular Informatics, 31
-
Joanne Hill, G. Mulholland, K. Persson, R. Seshadri, C. Wolverton, B. Meredig (2016)
Materials science with large-scale data and informatics: Unlocking new opportunitiesMRS Bulletin, 41
-
Justin Smith, O. Isayev, A. Roitberg (2016)
ANI-1: an extensible neural network potential with DFT accuracy at force field computational costChemical Science, 8
-
I. Skinner, Dawei Wu, C. Schweizer, F. Racioppi, Rie Tsutsumi
Unlocking new opportunities -
Edward Kim, Kevin Huang, Adam Saunders, A. McCallum, G. Ceder, E. Olivetti (2017)
Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine LearningChemistry of Materials, 29
-
Paul Raccuglia, Katherine Elbert, Philip Adler, Casey Falk, Malia Wenny, Aurelio Mollo, M. Zeller, Sorelle Friedler, Joshua Schrier, A. Norquist (2016)
Machine-learning-assisted materials discovery using failed experimentsNature, 533
-
S. Kirklin, J. Saal, B. Meredig, A. Thompson, J. Doak, Muratahan Aykol, S. Ruhl, C. Wolverton (2015)
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies, 1
-
C. Hansch, T. Fujita (1964)
p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical StructureJournal of the American Chemical Society, 86
-
(2016)
This study uses ML to guide all stages of a materials discovery workflow from quantum chemical calculations to materials synthesis -
This study transcends the standard approach to DFT by providing a direct mapping from density to energy, paving the way for higher-accuracy approaches -
A crystal engineering application of ML to assess the probability of a given molecule forming a highquality crystal -
J. Hachmann, R. Olivares-Amaya, Sule Atahan-Evrenk, C. Amador-Bedolla, Roel Sánchez-Carrera, Aryeh Gold-Parker, Leslie Vogt, Anna Brockway, Alán Aspuru-Guzik (2011)
The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community GridJournal of Physical Chemistry Letters, 2
-
D. Davies, K. Butler, A. Jackson, A. Morris, J. Frost, J. Skelton, A. Walsh (2016)
Computational Screening of All Stoichiometric Inorganic MaterialsChem, 1
-
I. Tetko, U. Maran, A. Tropsha (2017)
Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future DevelopmentMolecular Informatics, 36
-
G. Hautier, C. Fischer, Anubhav Jain, Tim Mueller, G. Ceder (2010)
Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional TheoryChemistry of Materials, 22
-
J. Schmidhuber (2014)
Deep learning in neural networks: An overviewNeural networks : the official journal of the International Neural Network Society, 61
-
N. Mardirossian, M. Head‐Gordon (2016)
ωB97M-V: A combinatorially optimized, range-separated hybrid, meta-GGA density functional with VV10 nonlocal correlation.The Journal of chemical physics, 144 21
-
A. Walsh (2015)
Inorganic materials: The quest for new functionality.Nature chemistry, 7 4
-
V. Albuquerque, P. Cortez, A. Alexandria, J. Tavares (2008)
A new solution for automatic microstructures analysis from images based on a backpropagation artificial neural networkNondestructive Testing and Evaluation, 23
-
A. Oliynyk, Erin Antono, Taylor Sparks, Leila Ghadbeigi, M. Gaultois, B. Meredig, A. Mar (2016)
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler CompoundsChemistry of Materials, 28
-
N. Jankowski, Wlodzislaw Duch, Krzysztof Grabczewski (2013)
Meta-Learning in Computational Intelligence, 358
-
Taylor Moot, O. Isayev, R. Call, S. McCullough, M. Zemaitis, R. Lopez, J. Cahoon, A. Tropsha (2016)
Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathodeMaterials Discovery, 6
-
A. Trabesinger (2017)
Quantum leaps, bit by bitNature, 543
-
Marco Piñón (2020)
I OverviewThe Diaries and Letters of Lord Woolton 1940-1945
- Publisher
- Springer Journals
- Copyright
- Copyright © 2018 by Macmillan Publishers Ltd., part of Springer Nature
- Subject
- Science, Humanities and Social Sciences, multidisciplinary; Science, Humanities and Social Sciences, multidisciplinary; Science, multidisciplinary
- ISSN
- 0028-0836
- eISSN
- 1476-4687
- DOI
- 10.1038/s41586-018-0337-2
- Publisher site
- See Article on Publisher Site
Abstract
Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.
Journal
Nature – Springer Journals
Published: Jul 25, 2018