Page 59 - Read Online
P. 59
Page 14 of 14 Li et al. J Mater Inf 2024;4:4 I http://dx.doi.org/10.20517/jmi.2023.41
27. Yang Z, Gao W. Applications of machine learning in alloy catalysts: rational selection and future development of descriptors. Adv Sci
2022;9:2106043. DOI
28. George J, Hautier G. Chemist versus machine: traditional knowledge versus machine learning techniques. Trends Chem 2021;3:86–
95. DOI
29. Zebari RR, Abdulazeez AM, Zeebaree DQ, Zebari DA, Saeed JN. A comprehensive review of dimensionality reduction techniques for
feature selection and feature extraction. J Appl Sci Technol Trends 2020;1:56–70. DOI
30. Otchere DA, Ganat TOA, Gholami R, Ridha S. Application of supervised machine learning paradigms in the prediction of petroleum
reservoir properties: comparative analysis of ANN and SVM models. J Petrol Sci Eng 2021;200:108182. DOI
31. Huang X, Ma S, Zhao CY, Wang H, Ju S. Exploring high thermal conductivity polymers via interpretable machine learning with physical
descriptors. npj Comput Mater 2023;9:191. DOI
32. Li CN, Liang HP, Zhang X, Lin Z, Wei SH. Graph deep learning accelerated efficient crystal structure search and feature extraction. npj
Comput Mater 2023;9:176. DOI
33. Isayev O, Fourches D, Muratov EN, et al. Materials cartography: representing and mining materials space using structural and electronic
fingerprints. Chem Mater 2015;27:735–43. DOI
34. Ward L, Agrawal A, Choudhary A, Wolverton C. A general-purpose machine learning framework for predicting properties of inorganic
materials. npj Comput Mater 2016;2:16028. DOI
35. Schütt KT, Glawe H, Brockherde F, Sanna A, Müller KR, Gross EKU. How to represent crystal structures for machine learning: towards
fast prediction of electronic properties. Phys Rev B 2014;89:205118. DOI
36. Luo Y, Du X, Wu L, Wang Y, Li J, Ricardez-Sandoval L. Machine-learning-accelerated screening of double-atom/cluster electrocatalysts
for the oxygen reduction reaction. J Phys Chem C 2023;127:20372–84. DOI
37. Tran K, Ulissi ZW. Active learning across intermetallics to guide discovery of electrocatalysts for CO 2 reduction and H 2 evolution. Nat
Catal 2018;1:696–703. DOI
38. Calle-Vallejo F, Martínez JI, García-Lastra JM, Sautet P, Loffreda D. Fast prediction of adsorption properties for platinum nanocatalysts
with generalized coordination numbers. Angew Chem Int Edit 2014;53:8316–9. DOI
39. Cao S, Luo Y, Li T, Li J, Wu L, Liu G. Machine learning assisted screening of doped metals phosphides electrocatalyst towards efficient
hydrogen evolution reaction. Mol Catal 2023;551:113625. DOI
40. Calle-Vallejo F, Tymoczko J, Colic V, et al. Finding optimal surface sites on heterogeneous catalysts by counting nearest neighbors.
Science 2015;350:185–9. DOI
41. Zhou C, Chen C, Hu P, Wang H. Topology-determined structural genes enable data-driven discovery and intelligent design of potential
metal oxides for inert C–H bond activation. J Am Chem Soc 2023;145:21897–903. DOI
42. Li X, Chiong R, Hu Z, Page AJ. A graph neural network model with local environment pooling for predicting adsorption energies. Comput
Theor Chem 2023;1226:114161. DOI
43. Li Y, Zhu R, Wang Y, Feng L, Liu Y. Center-environment deep transfer machine learning across crystal structures: from spinel oxides to
perovskite oxides. npj Comput Mater 2023;9:109. DOI
44. Chen R, Liu F, Tang Y, et al. Combined first-principles and machine learning study of the initial growth of carbon nanomaterials on metal
surfaces. Appl Surf Sci 2022;586:152762. DOI
45. Yang T, Zhou J, Song TT, Shen L, Feng YP, Yang M. High-throughput identification of exfoliable two-dimensional materials with active
basal planes for hydrogen evolution. ACS Energy Lett 2020;5:2313–21. DOI
46. Zhou J, Shen L, Costa MD, et al. 2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-
up approaches. Sci Data 2019;6:86. DOI
47. Tran K, Palizhati A, Back S, Ulissi ZW. Dynamic workflows for routine materials discovery in surface science. J Chem Inf Model
2018;58:2392–400. DOI
48. Tanemura M, Ogawa T, Ogita N. A new algorithm for three-dimensional voronoi tessellation. J Comput Phys 1983;51:191–207. DOI
49. Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C. Materials design and discovery with high-throughput density functional theory:
the open quantum materials database (OQMD). JOM 2013;65:1501–9. DOI
50. Li Z, Wang S, Chin WS, Achenie LE, Xin H. High-throughput screening of bimetallic catalysts enabled by machine learning. J Mate
Chem A 2017;5:24131–8. DOI
51. Calle-Vallejo F, Loffreda D, Koper MTM, Sautet P. Introducing structural sensitivity into adsorption-energy scaling relations by means
of coordination numbers. Nat Chem 2015;7:403–10. DOI
52. Cao Z, Dan Y, Xiong Z, et al. Convolutional neural networks for crystal material property prediction using hybrid orbital-field matrix and
magpie descriptors. Crystals 2019;9:191. DOI
53. Friedman JH. Stochastic gradient boosting. Comput Stat Data An 2002;38:367–78. DOI
54. Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inform Theory 1967;13:21–7. DOI
55. Breiman L. Random forests. Mach Learn 2001;45:5–32. DOI
56. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR); 2016 Jun 27-30; Las Vegas, USA. IEEE; 2016. pp. 770–8. DOI
57. Schütt KT, Sauceda HE, Kindermans PJ, Tkatchenko A, Müller KR. Schnet - a deep learning architecture for molecules and materials. J
Chem Phys 2018;148:241722. DOI

