Machine learning for molecular and materials science

Keywords

Computer scienceField (mathematics)Data scienceCharacterization (materials science)Domain (mathematical analysis)Artificial intelligenceCognitive scienceNanotechnologyMachine learningPsychologyMaterials science

Affiliated Institutions

Related Publications

2D materials: to graphene and beyond

Rubén Mas‐Ballesté , Cristina Gómez‐Navarro , Julio Gómez‐Herrero +1 more

This review is an attempt to illustrate the different alternatives in the field of 2D materials. Graphene seems to be just the tip of the iceberg and we show how the discovery o...

2010 Nanoscale 1627 citations

Deducing chemical structure from crystallographically determined atomic coordinates

Ian Bruno , G.P. Shields , Robin Taylor

An improved algorithm has been developed for assigning chemical structures to incoming entries to the Cambridge Structural Database, using only the information available in the ...

2011 Acta Crystallographica Section B Stru... 27 citations

Geometrical Factors Affecting the Contours of X-Ray Spectrometer Maxima. II. Factors Causing Broadening

Leroy Alexander

By applying Stokes' Fourier transform method for the analysis of diffraction maxima it is shown that the pure diffraction contour generated by a crystallite size distribution is...

1950 Journal of Applied Physics 33 citations

The diffraction of X-rays by distorted-crystal aggregates. II. Diffraction by bent lamellae

A. J. C. Wilson

The apparent particle size of distorted crystallites varies from independence of θ and λ (particle-size broadening) to proportionality with λ cosec θ (distortion broadening) as ...

1949 Acta Crystallographica 13 citations

Techniques used for the isolation and characterization of extracellular vesicles: results of a worldwide survey

Chris Gardiner , Dolores Di Vizio , Susmita Sahoo +4 more

Extracellular vesicles (EVs) represent an important mode of intercellular communication. Research in this field has grown rapidly in the last few years, and there is a plethora ...

2016 Journal of Extracellular Vesicles 1000 citations

Publication Info

Year: 2018
Type: review
Volume: 559
Issue: 7715
Pages: 547-555
Citations: 3984
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

Machine learning for molecular and materials science

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

3984

OpenAlex

Cite This

APA Style

                            
                                
                                    Keith T. Butler, 
                                
                                    Daniel W. Davies, 
                                
                                    Hugh Cartwright
                                
                                et al.
                            
                            (2018). 
                            Machine learning for molecular and materials science. 
                            Nature
                            , 559
                            (7715)
                            , 547-555.
                            https://doi.org/10.1038/s41586-018-0337-2
                        

Identifiers

DOI: 10.1038/s41586-018-0337-2