Literature DB >> 30046072

Machine learning for molecular and materials science.

Keith T Butler1, Daniel W Davies2, Hugh Cartwright3, Olexandr Isayev4, Aron Walsh5,6.   

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.

Entities:  

Year:  2018        PMID： 30046072     DOI： 10.1038/s41586-018-0337-2

Source DB:  PubMed          Journal:  Nature        ISSN： 0028-0836            Impact factor:   49.962


  44 in total

1.  Fast and accurate modeling of molecular atomization energies with machine learning.

Authors:  Matthias Rupp; Alexandre Tkatchenko; Klaus-Robert Müller; O Anatole von Lilienfeld
Journal:  Phys Rev Lett       Date:  2012-01-31       Impact factor: 9.161

2.  Hybrid MPI-OpenMP Parallelism in the ONETEP Linear-Scaling Electronic Structure Code: Application to the Delamination of Cellulose Nanofibrils.

Authors:  Karl A Wilkinson; Nicholas D M Hine; Chris-Kriton Skylaris
Journal:  J Chem Theory Comput       Date:  2014-11-11       Impact factor: 6.006

3.  Stable and Efficient Linear Scaling First-Principles Molecular Dynamics for 10000+ Atoms.

Authors:  Michiaki Arita; David R Bowler; Tsuyoshi Miyazaki
Journal:  J Chem Theory Comput       Date:  2014-11-21       Impact factor: 6.006

4.  Computer-assisted design of complex organic syntheses.

Authors:  E J Corey; W T Wipke
Journal:  Science       Date:  1969-10-10       Impact factor: 47.728

Review 5.  Deep learning in neural networks: an overview.

Authors:  Jürgen Schmidhuber
Journal:  Neural Netw       Date:  2014-10-13

6.  Reproducibility in density functional theory calculations of solids.

Authors:  Kurt Lejaeghere; Gustav Bihlmayer; Torbjörn Björkman; Peter Blaha; Stefan Blügel; Volker Blum; Damien Caliste; Ivano E Castelli; Stewart J Clark; Andrea Dal Corso; Stefano de Gironcoli; Thierry Deutsch; John Kay Dewhurst; Igor Di Marco; Claudia Draxl; Marcin Dułak; Olle Eriksson; José A Flores-Livas; Kevin F Garrity; Luigi Genovese; Paolo Giannozzi; Matteo Giantomassi; Stefan Goedecker; Xavier Gonze; Oscar Grånäs; E K U Gross; Andris Gulans; François Gygi; D R Hamann; Phil J Hasnip; N A W Holzwarth; Diana Iuşan; Dominik B Jochym; François Jollet; Daniel Jones; Georg Kresse; Klaus Koepernik; Emine Küçükbenli; Yaroslav O Kvashnin; Inka L M Locht; Sven Lubeck; Martijn Marsman; Nicola Marzari; Ulrike Nitzsche; Lars Nordström; Taisuke Ozaki; Lorenzo Paulatto; Chris J Pickard; Ward Poelmans; Matt I J Probert; Keith Refson; Manuel Richter; Gian-Marco Rignanese; Santanu Saha; Matthias Scheffler; Martin Schlipf; Karlheinz Schwarz; Sangeeta Sharma; Francesca Tavazza; Patrik Thunström; Alexandre Tkatchenko; Marc Torrent; David Vanderbilt; Michiel J van Setten; Veronique Van Speybroeck; John M Wills; Jonathan R Yates; Guo-Xu Zhang; Stefaan Cottenier
Journal:  Science       Date:  2016-03-25       Impact factor: 47.728

7.  Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure-Activity Modeling and Dataset Comparison.

Authors:  N Kireeva; I I Baskin; H A Gaspar; D Horvath; G Marcou; A Varnek
Journal:  Mol Inform       Date:  2012-04-04       Impact factor: 3.353

8.  Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.

Authors:  Felix A Faber; Luke Hutchison; Bing Huang; Justin Gilmer; Samuel S Schoenholz; George E Dahl; Oriol Vinyals; Steven Kearnes; Patrick F Riley; O Anatole von Lilienfeld
Journal:  J Chem Theory Comput       Date:  2017-10-10       Impact factor: 6.006

9.  Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research.

Authors:  Denis Fourches; Eugene Muratov; Alexander Tropsha
Journal:  J Chem Inf Model       Date:  2010-07-26       Impact factor: 4.956

10.  Universal fragment descriptors for predicting properties of inorganic crystals.

Authors:  Olexandr Isayev; Corey Oses; Cormac Toher; Eric Gossett; Stefano Curtarolo; Alexander Tropsha
Journal:  Nat Commun       Date:  2017-06-05       Impact factor: 14.919
View more
  193 in total

Review 1.  Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.

Authors:  Kevin Maik Jablonka; Daniele Ongari; Seyed Mohamad Moosavi; Berend Smit
Journal:  Chem Rev       Date:  2020-06-10       Impact factor: 60.622

Review 2.  QSAR without borders.

Authors:  Eugene N Muratov; Jürgen Bajorath; Robert P Sheridan; Igor V Tetko; Dmitry Filimonov; Vladimir Poroikov; Tudor I Oprea; Igor I Baskin; Alexandre Varnek; Adrian Roitberg; Olexandr Isayev; Stefano Curtarolo; Denis Fourches; Yoram Cohen; Alan Aspuru-Guzik; David A Winkler; Dimitris Agrafiotis; Artem Cherkasov; Alexander Tropsha
Journal:  Chem Soc Rev       Date:  2020-05-01       Impact factor: 54.564

3.  Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics.

Authors:  Rama K Vasudevan; Kamal Choudhary; Apurva Mehta; Ryan Smith; Gilad Kusne; Francesca Tavazza; Lukas Vlcek; Maxim Ziatdinov; Sergei V Kalinin; Jason Hattrick-Simpers
Journal:  MRS Commun       Date:  2019       Impact factor: 2.566

4.  Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods.

Authors:  Kamal Choudhary; Marnik Bercx; Jie Jiang; Ruth Pachter; Dirk Lamoen; Francesca Tavazza
Journal:  Chem Mater       Date:  2019       Impact factor: 9.811

Review 5.  Insights into Computational Drug Repurposing for Neurodegenerative Disease.

Authors:  Manish D Paranjpe; Alice Taubes; Marina Sirota
Journal:  Trends Pharmacol Sci       Date:  2019-07-17       Impact factor: 14.819

6.  Accurate machine learning in materials science facilitated by using diverse data sources.

Authors:  Rohit Batra
Journal:  Nature       Date:  2021-01       Impact factor: 49.962

7.  Transforming Computational Drug Discovery with Machine Learning and AI.

Authors:  Justin S Smith; Adrian E Roitberg; Olexandr Isayev
Journal:  ACS Med Chem Lett       Date:  2018-10-08       Impact factor: 4.345

Review 8.  Recent advances in fast-scan cyclic voltammetry.

Authors:  Pumidech Puthongkham; B Jill Venton
Journal:  Analyst       Date:  2020-02-17       Impact factor: 4.616

9.  Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning.

Authors:  Jinying Huang; Yun Zhu; James T Kelly; Carey Jang; Shuxiao Wang; Jia Xing; Pen-Chi Chiang; Shaojia Fan; Xuetao Zhao; Lian Yu
Journal:  Sci Total Environ       Date:  2020-03-06       Impact factor: 7.963

Review 10.  A review of mathematical representations of biomolecular data.

Authors:  Duc Duy Nguyen; Zixuan Cang; Guo-Wei Wei
Journal:  Phys Chem Chem Phys       Date:  2020-02-26       Impact factor: 3.676
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.