Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices
This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, pop...
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| Published in | Chemistry of materials Vol. 32; no. 12; pp. 4954 - 4965 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Chemical Society
23.06.2020
American Chemical Society (ACS) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise. |
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| AbstractList | This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. Herein, we cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise. This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise. |
| Author | Gurlo, Aleksander Sparks, Taylor D Wang, Anthony Yu-Tung Brgoch, Jakoah Murdock, Ryan J Kauwe, Steven K Oliynyk, Anton O Persson, Kristin A |
| AuthorAffiliation | Department of Chemistry Energy Storage and Distributed Resources Division Department of Materials Science & Engineering Department of Materials Science University of California Berkeley Fachgebiet Keramische Werkstoffe/Chair of Advanced Ceramic Materials Department of Chemistry & Biochemistry |
| AuthorAffiliation_xml | – name: University of California Berkeley – name: Department of Chemistry & Biochemistry – name: Department of Chemistry – name: Department of Materials Science & Engineering – name: Energy Storage and Distributed Resources Division – name: Fachgebiet Keramische Werkstoffe/Chair of Advanced Ceramic Materials – name: Department of Materials Science |
| Author_xml | – sequence: 1 givenname: Anthony Yu-Tung orcidid: 0000-0002-7947-0309 surname: Wang fullname: Wang, Anthony Yu-Tung organization: Fachgebiet Keramische Werkstoffe/Chair of Advanced Ceramic Materials – sequence: 2 givenname: Ryan J surname: Murdock fullname: Murdock, Ryan J organization: Department of Materials Science & Engineering – sequence: 3 givenname: Steven K surname: Kauwe fullname: Kauwe, Steven K organization: Department of Materials Science & Engineering – sequence: 4 givenname: Anton O orcidid: 0000-0003-0732-7340 surname: Oliynyk fullname: Oliynyk, Anton O organization: Department of Chemistry & Biochemistry – sequence: 5 givenname: Aleksander orcidid: 0000-0001-7047-666X surname: Gurlo fullname: Gurlo, Aleksander organization: Fachgebiet Keramische Werkstoffe/Chair of Advanced Ceramic Materials – sequence: 6 givenname: Jakoah orcidid: 0000-0002-1406-1352 surname: Brgoch fullname: Brgoch, Jakoah organization: Department of Chemistry – sequence: 7 givenname: Kristin A orcidid: 0000-0003-2495-5509 surname: Persson fullname: Persson, Kristin A organization: University of California Berkeley – sequence: 8 givenname: Taylor D orcidid: 0000-0001-8020-7711 surname: Sparks fullname: Sparks, Taylor D email: sparks@eng.utah.edu organization: Department of Materials Science & Engineering |
| BackLink | https://www.osti.gov/servlets/purl/1766496$$D View this record in Osti.gov |
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| Cites_doi | 10.1021/acscatal.9b02531 10.1107/S0108768102002902 10.1021/acs.chemmater.8b00686 10.1016/j.actamat.2019.01.051 10.1088/2515-7639/ab13bb 10.1371/journal.pbio.1002106 10.1021/acs.chemmater.6b02905 10.1021/acs.chemmater.7b05304 10.1021/acs.chemmater.9b01294 10.1002/jcc.25067 10.1107/S2052520616003954 10.26434/chemrxiv.7236029 10.1088/1367-2630/aa57c2 10.1103/PhysRevB.95.144110 10.1039/C6RA19284J 10.1016/j.commatsci.2018.07.052 10.1038/nmat1691 10.1107/S2052520615013979 10.1021/cm400893e 10.1017/S0885715619000812 10.1103/PhysRevMaterials.2.083801 10.1002/cctc.201900595 10.1038/467753a 10.1038/s41586-019-1335-8 10.1016/j.scriptamat.2015.04.026 10.1038/s41586-018-0337-2 10.1103/PhysRevB.89.205118 10.1145/2908812.2908918 10.1103/PhysRevMaterials.2.013805 10.7567/1882-0786/ab2922 10.1038/natrevmats.2017.53 10.1038/s41467-019-08356-1 10.1021/acs.chemmater.9b04078 10.1016/j.actamat.2018.08.002 10.1103/PhysRevLett.120.145301 10.19026/rjaset.6.3638 10.1007/s10845-018-1392-0 10.1021/acs.chemmater.9b01953 10.1107/S0021889809016690 10.1103/PhysRevB.93.085142 10.1021/acs.chemmater.6b02724 10.1007/s40192-020-00174-4 10.1039/C9TA02356A 10.1021/acs.chemmater.8b03272 10.1038/s41598-018-35934-y 10.1038/s41524-019-0221-0 10.1002/9781119148739.ch4 10.1038/s41524-018-0085-8 10.1039/C8ME00012C 10.1038/ncomms15679 10.1021/acs.chemmater.7b02532 10.1080/08893110410001664882 10.1109/ACCESS.2020.2981874 10.26434/chemrxiv.11869026 10.1021/ci200628z 10.1038/npjcompumats.2016.28 10.1039/C6EE02697D 10.1038/ncomms14621 10.1007/s11837-019-03704-4 10.1038/s41524-020-00433-0 10.1016/j.commatsci.2012.02.002 10.1016/j.commatsci.2019.01.013 10.1063/1.4952607 10.21105/joss.00861 10.1021/acs.chemmater.7b03500 10.26434/chemrxiv.11879193 10.1021/acs.jpclett.8b00124 10.1103/PhysRevX.4.011019 10.1038/srep34256 10.1103/PhysRevB.89.094104 10.1016/j.scriptamat.2013.06.005 10.6084/m9.figshare.11954742 10.1021/acscentsci.8b00229 10.1038/s41578-018-0005-z 10.1016/j.commatsci.2018.05.018 10.1038/s41929-018-0056-y 10.1007/978-3-319-23871-5_1 10.1016/j.commatsci.2013.10.016 10.1038/srep19375 10.1021/cm100795d 10.1021/acs.chemmater.9b01519 10.1557/mrs.2019.158 10.1038/ncomms14282 10.1038/s41598-019-42277-9 10.1021/jacs.8b02717 10.1016/j.commatsci.2018.04.033 10.1063/1.5019779 10.1103/PhysRevLett.114.105503 10.1080/08927022.2019.1597271 10.1126/science.aad4998 10.1063/1.4812323 10.1021/acs.chemmater.7b00156 10.1103/PhysRevLett.91.135503 10.1126/sciadv.aaq1566 10.1016/j.commatsci.2019.109498 10.26434/chemrxiv.9782375 10.1016/j.actamat.2019.03.010 10.1016/j.jmat.2017.08.002 10.1186/2193-9772-3-8 10.1145/3292500.3330703 10.1007/s40192-018-0108-9 10.1007/s11837-013-0755-4 |
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| References | ref45/cit45 ref99/cit99 Lookman T. (ref14/cit14) 2016; 225 ref3/cit3 ref81/cit81 ref16/cit16 ref52/cit52 ref114/cit114 ref23/cit23 ref115/cit115 ref111/cit111 ref2/cit2 ref112/cit112 ref77/cit77 ref113/cit113 ref71/cit71 ref20/cit20 ref48/cit48 ref118/cit118 ref74/cit74 ref119/cit119 ref10/cit10 ref35/cit35 ref89/cit89 ref19/cit19 ref93/cit93 ref42/cit42 ref96/cit96 ref107/cit107 ref120/cit120 ref109/cit109 ref13/cit13 ref122/cit122 ref105/cit105 ref61/cit61 ref67/cit67 ref38/cit38 ref128/cit128 ref90/cit90 ref124/cit124 Pedregosa F. (ref117/cit117) 2011; 12 ref64/cit64 ref126/cit126 ref54/cit54 ref6/cit6 ref18/cit18 ref136/cit136 ref65/cit65 ref97/cit97 ref101/cit101 ref11/cit11 ref102/cit102 ref29/cit29 ref76/cit76 ref86/cit86 ref32/cit32 ref39/cit39 ref5/cit5 ref43/cit43 ref80/cit80 ref133/cit133 ref28/cit28 ref132/cit132 ref91/cit91 ref55/cit55 ref12/cit12 ref66/cit66 ref22/cit22 ref121/cit121 ref33/cit33 ref87/cit87 ref106/cit106 ref129/cit129 ref44/cit44 ref70/cit70 ref98/cit98 ref125/cit125 ref9/cit9 ref27/cit27 ref63/cit63 ref56/cit56 ref92/cit92 ref8/cit8 ref31/cit31 ref59/cit59 ref85/cit85 ref34/cit34 ref37/cit37 ref60/cit60 ref88/cit88 ref17/cit17 ref82/cit82 ref53/cit53 ref21/cit21 ref46/cit46 ref49/cit49 van der Maaten L. (ref110/cit110) 2008; 9 ref75/cit75 ref24/cit24 ref50/cit50 ref78/cit78 ref36/cit36 ref83/cit83 ref79/cit79 ref100/cit100 ref25/cit25 ref103/cit103 ref72/cit72 ref57/cit57 ref51/cit51 ref134/cit134 ref135/cit135 ref40/cit40 ref68/cit68 ref94/cit94 ref130/cit130 ref131/cit131 ref26/cit26 ref73/cit73 Cawley G. C. (ref116/cit116) 2010; 11 ref69/cit69 ref15/cit15 ref62/cit62 ref41/cit41 ref58/cit58 ref95/cit95 ref108/cit108 ref104/cit104 ref4/cit4 ref30/cit30 ref47/cit47 ref84/cit84 ref127/cit127 ref1/cit1 ref123/cit123 ref7/cit7 |
| References_xml | – ident: ref108/cit108 – ident: ref71/cit71 doi: 10.1021/acscatal.9b02531 – ident: ref109/cit109 doi: 10.1107/S0108768102002902 – ident: ref38/cit38 doi: 10.1021/acs.chemmater.8b00686 – ident: ref20/cit20 doi: 10.1016/j.actamat.2019.01.051 – ident: ref113/cit113 – ident: ref88/cit88 doi: 10.1088/2515-7639/ab13bb – ident: ref127/cit127 doi: 10.1371/journal.pbio.1002106 – ident: ref47/cit47 doi: 10.1021/acs.chemmater.6b02905 – ident: ref120/cit120 doi: 10.1021/acs.chemmater.7b05304 – ident: ref29/cit29 doi: 10.1021/acs.chemmater.9b01294 – ident: ref58/cit58 doi: 10.1002/jcc.25067 – ident: ref103/cit103 doi: 10.1107/S2052520616003954 – ident: ref37/cit37 doi: 10.26434/chemrxiv.7236029 – ident: ref48/cit48 doi: 10.1088/1367-2630/aa57c2 – ident: ref57/cit57 doi: 10.1103/PhysRevB.95.144110 – ident: ref27/cit27 doi: 10.1039/C6RA19284J – ident: ref77/cit77 doi: 10.1016/j.commatsci.2018.07.052 – ident: ref43/cit43 doi: 10.1038/nmat1691 – ident: ref46/cit46 doi: 10.1107/S2052520615013979 – ident: ref72/cit72 doi: 10.1021/cm400893e – ident: ref92/cit92 – ident: ref97/cit97 – ident: ref106/cit106 doi: 10.1017/S0885715619000812 – ident: ref121/cit121 doi: 10.1103/PhysRevMaterials.2.083801 – ident: ref70/cit70 doi: 10.1002/cctc.201900595 – ident: ref132/cit132 doi: 10.1038/467753a – ident: ref4/cit4 doi: 10.1038/s41586-019-1335-8 – ident: ref73/cit73 doi: 10.1016/j.scriptamat.2015.04.026 – ident: ref18/cit18 doi: 10.1038/s41586-018-0337-2 – ident: ref40/cit40 doi: 10.1103/PhysRevB.89.205118 – ident: ref129/cit129 doi: 10.1145/2908812.2908918 – ident: ref51/cit51 doi: 10.1103/PhysRevMaterials.2.013805 – ident: ref79/cit79 doi: 10.7567/1882-0786/ab2922 – ident: ref17/cit17 doi: 10.1038/natrevmats.2017.53 – ident: ref2/cit2 doi: 10.1038/s41467-019-08356-1 – ident: ref24/cit24 doi: 10.1021/acs.chemmater.9b04078 – ident: ref84/cit84 doi: 10.1016/j.actamat.2018.08.002 – ident: ref133/cit133 – ident: ref93/cit93 – ident: ref9/cit9 doi: 10.1103/PhysRevLett.120.145301 – ident: ref114/cit114 – ident: ref126/cit126 doi: 10.19026/rjaset.6.3638 – ident: ref19/cit19 doi: 10.1007/s10845-018-1392-0 – volume: 9 start-page: 2579 year: 2008 ident: ref110/cit110 publication-title: Journal of Machine Learning Research – ident: ref59/cit59 doi: 10.1021/acs.chemmater.9b01953 – ident: ref10/cit10 – ident: ref107/cit107 doi: 10.1107/S0021889809016690 – ident: ref33/cit33 doi: 10.1103/PhysRevB.93.085142 – ident: ref100/cit100 – ident: ref13/cit13 doi: 10.1021/acs.chemmater.6b02724 – ident: ref135/cit135 doi: 10.1007/s40192-020-00174-4 – ident: ref65/cit65 doi: 10.1039/C9TA02356A – ident: ref118/cit118 – ident: ref63/cit63 doi: 10.1021/acs.chemmater.8b03272 – volume: 11 start-page: 2079 year: 2010 ident: ref116/cit116 publication-title: Journal of Machine Learning Research – ident: ref7/cit7 doi: 10.1038/s41598-018-35934-y – ident: ref23/cit23 doi: 10.1038/s41524-019-0221-0 – ident: ref119/cit119 – ident: ref15/cit15 doi: 10.1002/9781119148739.ch4 – ident: ref74/cit74 doi: 10.1038/s41524-018-0085-8 – ident: ref75/cit75 doi: 10.1039/C8ME00012C – ident: ref53/cit53 doi: 10.1038/ncomms15679 – ident: ref98/cit98 – ident: ref30/cit30 doi: 10.1021/acs.chemmater.7b02532 – ident: ref104/cit104 doi: 10.1080/08893110410001664882 – ident: ref76/cit76 – ident: ref78/cit78 doi: 10.1109/ACCESS.2020.2981874 – ident: ref130/cit130 – ident: ref6/cit6 doi: 10.26434/chemrxiv.11869026 – ident: ref45/cit45 doi: 10.1021/ci200628z – ident: ref34/cit34 doi: 10.1038/npjcompumats.2016.28 – ident: ref61/cit61 doi: 10.1039/C6EE02697D – ident: ref67/cit67 doi: 10.1038/ncomms14621 – ident: ref82/cit82 doi: 10.1007/s11837-019-03704-4 – ident: ref94/cit94 – ident: ref99/cit99 – volume: 12 start-page: 2825 year: 2011 ident: ref117/cit117 publication-title: Journal of Machine Learning Research – ident: ref131/cit131 doi: 10.1038/s41524-020-00433-0 – ident: ref87/cit87 doi: 10.1016/j.commatsci.2012.02.002 – ident: ref22/cit22 doi: 10.1016/j.commatsci.2019.01.013 – ident: ref56/cit56 doi: 10.1063/1.4952607 – ident: ref111/cit111 doi: 10.21105/joss.00861 – ident: ref5/cit5 doi: 10.1021/acs.chemmater.7b03500 – ident: ref11/cit11 doi: 10.26434/chemrxiv.11879193 – ident: ref39/cit39 doi: 10.1021/acs.jpclett.8b00124 – ident: ref134/cit134 – ident: ref55/cit55 doi: 10.1103/PhysRevX.4.011019 – ident: ref28/cit28 doi: 10.1038/srep34256 – ident: ref31/cit31 doi: 10.1103/PhysRevB.89.094104 – ident: ref115/cit115 – ident: ref25/cit25 doi: 10.1016/j.scriptamat.2013.06.005 – ident: ref69/cit69 – ident: ref136/cit136 doi: 10.6084/m9.figshare.11954742 – ident: ref62/cit62 doi: 10.1021/acscentsci.8b00229 – ident: ref101/cit101 – ident: ref102/cit102 – ident: ref3/cit3 doi: 10.1038/s41578-018-0005-z – ident: ref128/cit128 doi: 10.1016/j.commatsci.2018.05.018 – ident: ref68/cit68 doi: 10.1038/s41929-018-0056-y – volume: 225 volume-title: Information Science for Materials Discovery and Design year: 2016 ident: ref14/cit14 doi: 10.1007/978-3-319-23871-5_1 – ident: ref35/cit35 doi: 10.1016/j.commatsci.2013.10.016 – ident: ref36/cit36 doi: 10.1038/srep19375 – ident: ref44/cit44 doi: 10.1021/cm100795d – ident: ref60/cit60 doi: 10.1021/acs.chemmater.9b01519 – ident: ref21/cit21 doi: 10.1557/mrs.2019.158 – ident: ref49/cit49 doi: 10.1038/ncomms14282 – ident: ref90/cit90 – ident: ref105/cit105 – ident: ref41/cit41 doi: 10.1038/s41598-019-42277-9 – ident: ref1/cit1 doi: 10.1021/jacs.8b02717 – ident: ref52/cit52 doi: 10.1016/j.commatsci.2018.04.033 – ident: ref8/cit8 doi: 10.1063/1.5019779 – ident: ref32/cit32 doi: 10.1103/PhysRevLett.114.105503 – ident: ref64/cit64 doi: 10.1080/08927022.2019.1597271 – ident: ref66/cit66 doi: 10.1126/science.aad4998 – ident: ref85/cit85 doi: 10.1063/1.4812323 – ident: ref50/cit50 doi: 10.1021/acs.chemmater.7b00156 – ident: ref42/cit42 doi: 10.1103/PhysRevLett.91.135503 – ident: ref89/cit89 – ident: ref83/cit83 doi: 10.1126/sciadv.aaq1566 – ident: ref95/cit95 – ident: ref12/cit12 doi: 10.1016/j.commatsci.2019.109498 – ident: ref112/cit112 doi: 10.26434/chemrxiv.9782375 – ident: ref81/cit81 doi: 10.1016/j.actamat.2019.03.010 – ident: ref91/cit91 – ident: ref80/cit80 – ident: ref123/cit123 – ident: ref16/cit16 doi: 10.1016/j.jmat.2017.08.002 – ident: ref96/cit96 – ident: ref26/cit26 doi: 10.1186/2193-9772-3-8 – ident: ref122/cit122 doi: 10.1145/3292500.3330703 – ident: ref124/cit124 – ident: ref125/cit125 – ident: ref54/cit54 doi: 10.1007/s40192-018-0108-9 – ident: ref86/cit86 doi: 10.1007/s11837-013-0755-4 |
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| Title | Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices |
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