DataVXACS Energy LettersPaper
journal article·2021-03-23Open access

How Machine Learning Will Revolutionize Electrochemical Sciences

Aashutosh Mistry, Alejandro A. Franco, Samuel J. Cooper, Scott A. Roberts, Venkatasubramanian Viswanathan

ACS Energy Letters
Publisher pageOpen full text
Source abstract

Abstract

Electrochemical systems function via interconversion of electric charge and chemical species and represent promising technologies for our cleaner, more sustainable future. However, their development time is fundamentally limited by our ability to identify new materials and understand their electrochemical response. To shorten this time frame, we need to switch from the trial-and-error approach of finding useful materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can revolutionize the development cycle from decades to a few years. We outline the necessary characteristics of such ML implementations. Instead of enumerating various ML algorithms, we discuss scientific questions about the electrochemical systems to which ML can contribute.
Source: europepmc · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Machine Learning in Materials Science · Fuel Cells and Related Materials · CO2 Reduction Techniques and Catalysts

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United States

Laboratorie de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de I’Energie, 15 rue Baudelocque, 80039 Amiens Cedex, France

Réseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, 15 rue Baudelocque, 80039 Amiens Cedex, France

ALISTORE-European Research Institute, FR CNRS 3104, Hub de l’Energie, 15 rue Baudelocque, 80039 Amiens Cedex, France

Institut Universitaire de France, 103 Boulevard Saint Michel, 75005 Paris, France

Dyson School of Design Engineering, Imperial College London, London SW7 2DB, United Kingdom

Engineering Sciences Center, Sandia National Laboratories, Albuquerque, New Mexico 87185, United States

Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States

Funding

Engineering and Physical Sciences Research Council · Advanced Research Projects Agency - Energy · Institut Universitaire de France · Sandia National Laboratories · H2020 European Research Council · Argonne National Laboratory · Faraday Institution

References (54)

Show source-provided references
1. 10.1149/2.F05203IF10.1149/2.F05203IF ↗
2. 10.1073/pnas.182167211710.1073/pnas.1821672117 ↗
3. 10.1038/451652a10.1038/451652a ↗
4. 10.1149/2.F03204IF10.1149/2.F03204IF ↗
5. Newman J. · 2012
6. 10.1002/aic.69021010310.1002/aic.690210103 ↗
7. 10.1149/1.185085410.1149/1.1850854 ↗
8. 10.1149/1.293730410.1149/1.2937304 ↗
9. 10.1103/PhysRevLett.119.05600310.1103/PhysRevLett.119.056003 ↗
10. 10.1016/j.electacta.2017.07.15210.1016/j.electacta.2017.07.152 ↗
11. 10.1021/acsenergylett.8b0200310.1021/acsenergylett.8b02003 ↗
12. 10.1149/1945-7111/ab632b10.1149/1945-7111/ab632b ↗
13. 10.1149/1945-7111/ab8fd710.1149/1945-7111/ab8fd7 ↗
14. 10.1021/acs.chemrev.8b0023910.1021/acs.chemrev.8b00239 ↗
15. 10.1021/nl202764f10.1021/nl202764f ↗
16. Giibs N. · 2017
17. Park A. · Time
18. 10.1038/s41578-020-0216-y10.1038/s41578-020-0216-y ↗
19. 10.1126/sciadv.aaz886710.1126/sciadv.aaz8867 ↗
20. 10.1038/s41586-020-1994-510.1038/s41586-020-1994-5 ↗
21. Mistry, A.; Trask, S.; Dunlop, A.; Jeka, G.; Polzin, B.; Mukherjee, P. P.; Srinivasan, V.Quantifying Negative Effects of Carbon-binder Networks from Electrochemical Performance of Porous Li-ion Electrodes, 2021, under review.10.1149/1945-7111/ac1033 ↗
22. 10.1149/2.0062007JES10.1149/2.0062007JES ↗
23. 10.1016/j.jpowsour.2016.03.01710.1016/j.jpowsour.2016.03.017 ↗
24. 10.1103/PhysRevB.87.18411510.1103/PhysRevB.87.184115 ↗
25. 10.1063/1.496619210.1063/1.4966192 ↗
26. 10.1063/1.502457710.1063/1.5024577 ↗
27. 10.1103/PhysRevB.87.18411510.1103/PhysRevB.87.184115 ↗
28. 10.1016/j.cpc.2019.10694910.1016/j.cpc.2019.106949 ↗
29. 10.1063/1.509184210.1063/1.5091842 ↗
30. 10.1103/PhysRevB.96.01411210.1103/PhysRevB.96.014112 ↗
31. 10.1088/2515-7655/abc96f10.1088/2515-7655/abc96f ↗
32. 10.1039/C6SC05720A10.1039/C6SC05720A ↗
33. 10.1021/acs.jpclett.7b0264710.1021/acs.jpclett.7b02647 ↗
34. 10.1016/j.jpowsour.2019.22728510.1016/j.jpowsour.2019.227285 ↗
35. 10.1021/acsami.0c0825110.1021/acsami.0c08251 ↗
36. 10.1002/batt.20190013510.1002/batt.201900135 ↗
37. 10.1016/j.jpowsour.2020.22910310.1016/j.jpowsour.2020.229103 ↗
38. 10.1002/batt.20200004910.1002/batt.202000049 ↗
39. 10.1016/j.jpowsour.2020.22932010.1016/j.jpowsour.2020.229320 ↗
40. 10.1038/s41524-020-0340-710.1038/s41524-020-0340-7 ↗
41. 10.1038/s41467-020-16233-510.1038/s41467-020-16233-5 ↗
42. 10.1016/j.jcp.2018.10.04510.1016/j.jcp.2018.10.045 ↗
43. 10.1007/s40304-017-0103-z10.1007/s40304-017-0103-z ↗
44. 10.1007/s10851-019-00903-110.1007/s10851-019-00903-1 ↗
45. 10.1038/s41524-020-0322-910.1038/s41524-020-0322-9 ↗
46. 10.1016/j.compchemeng.2020.10700510.1016/j.compchemeng.2020.107005 ↗
47. 10.1126/science.aat266310.1126/science.aat2663 ↗
48. 10.1021/acscentsci.8b0022910.1021/acscentsci.8b00229 ↗
49. 10.1021/acscentsci.0c0047510.1021/acscentsci.0c00475 ↗
50. 10.1063/1.512409910.1063/1.5124099 ↗
51. 10.1098/rsta.2019.005610.1098/rsta.2019.0056 ↗
52. 10.1038/s41467-019-10663-610.1038/s41467-019-10663-6 ↗
53. 10.1103/PhysRevMaterials.3.05540410.1103/PhysRevMaterials.3.055404 ↗
54. 10.1126/science.112764710.1126/science.1127647 ↗