CIESC Journal ›› 2021, Vol. 72 ›› Issue (1): 27-41.DOI: 10.11949/0438-1157.20201037
• Reviews and monographs • Previous Articles Next Articles
SHI Xiangcheng1,2,3(),ZHAO Zhijian1,2(),GONG Jinlong1,2,3
Received:
2020-07-25
Revised:
2020-10-05
Online:
2021-01-05
Published:
2021-01-05
Contact:
ZHAO Zhijian
通讯作者:
赵志坚
作者简介:
石向成(1996—),男,博士研究生,基金资助:
CLC Number:
SHI Xiangcheng, ZHAO Zhijian, GONG Jinlong. Application of genetic algorithm in the global structure optimization of catalytic system[J]. CIESC Journal, 2021, 72(1): 27-41.
石向成, 赵志坚, 巩金龙. 遗传算法在催化体系的全局结构优化中的应用[J]. 化工学报, 2021, 72(1): 27-41.
Fig.1 Energetic distributions of randomly generated structures for LJ38 (a) and LJ100 (b) clusters with and without symmetry(energies are shown relative to the global minimum)[29]
Fig.9 Global minimum structure of Pt8H4 on α-Al2O3(0001) (a), Pt8H5 on γ-Al2O3(100) (b), Pt8H24 on α-Al2O3(0001) (c), and Pt8H24 on γ-Al2O3(100) (d)[91]
1 | Johnston R L. Evolving better nanoparticles: genetic algorithms for optimising cluster geometries[J]. Dalton Transactions, 2003, (22): 4193-4207. |
2 | Mcleod A S, Gladden L F. Heterogeneous catalyst design using stochastic optimization algorithms[J]. Journal of Chemical Information and Computer Sciences, 2000, 40(4): 981-987. |
3 | Davis J B A, Horswell S L, Johnston R L. Application of a parallel genetic algorithm to the global optimization of gas-phase and supported gold–iridium sub-nanoalloys[J]. The Journal of Physical Chemistry C, 2016, 120(7): 3759-3765. |
4 | Liu C, Pei Y, Sun H, et al. The nucleation and growth mechanism of thiolate-protected Au nanoclusters[J]. Journal of the American Chemical Society, 2015, 137(50): 15809-15816. |
5 | Reichenbach T, Walter M, Moseler M, et al. Effects of gas-phase conditions and particle size on the properties of Cu(111)-supported ZnyOx particles revealed by global optimization and ab initio thermodynamics[J]. The Journal of Physical Chemistry C, 2019, 123(51): 30903-30916. |
6 | Fang Y, Gong X. Genetic algorithm aided density functional theory simulations unravel the kinetic nature of Au(100) in catalytic CO oxidation[J]. Chinese Chemical Letters, 2019, 30(6): 1346-1350. |
7 | Ding X L, Liao H L, Zhang Y, et al. Geometric and electronic properties of gold clusters doped with a single oxygen atom[J]. Physical Chemistry Chemical Physics, 2016, 18(41): 28960-28972. |
8 | Zandkarimi B, Alexandrova A N. Dynamics of subnanometer Pt clusters can break the scaling relationships in catalysis[J]. The Journal of Physical Chemistry Letters, 2019, 10(3): 460-467. |
9 | Zhai H, Alexandrova A N. Local fluxionality of surface-deposited cluster catalysts: the case of Pt7 on Al2O3[J]. The Journal of Physical Chemistry Letters, 2018, 9(7): 1696-1702. |
10 | Jäger M, Schäfer R, Johnston R L. First principles global optimization of metal clusters and nanoalloys[J]. Advances in Physics: X, 2018, 3(1): 1516514. |
11 | Stillinger F H. Exponential multiplicity of inherent structures[J]. Physical Review E, 1999, 59(1): 48-51. |
12 | Sierka M. Synergy between theory and experiment in structure resolution of low-dimensional oxides[J]. Progress in Surface Science, 2010, 85(9/10/11/12): 398-434. |
13 | Hartke B. Global geometry optimization of clusters using genetic algorithms[J]. The Journal of Physical Chemistry, 1993, 97(39): 9973-9976. |
14 | Deaven D M, Ho K M. Molecular geometry optimization with a genetic algorithm[J]. Physical Review Letters, 1995, 75(2): 288-291. |
15 | Froemming N S, Henkelman G. Optimizing core-shell nanoparticle catalysts with a genetic algorithm[J]. The Journal of Chemical Physics, 2009, 131(23): 234103. |
16 | Vilhelmsen L B, Hammer B. A genetic algorithm for first principles global structure optimization of supported nano structures[J]. The Journal of Chemical Physics, 2014, 141(4): 044711. |
17 | Yang H, Wong M W. Automatic conformational search of transition states for catalytic reactions using genetic algorithm[J]. The Journal of Physical Chemistry A, 2019, 123(47): 10303-10314. |
18 | Dittner M, Hartke B. Globally optimal catalytic fields – inverse design of abstract embeddings for maximum reaction rate acceleration[J]. Journal of Chemical Theory and Computation, 2018, 14(7): 3547-3564. |
19 | Hartke B. Global cluster geometry optimization by a phenotype algorithm with niches: location of elusive minima, and low-order scaling with cluster size[J]. Journal of Computational Chemistry, 1999, 20(16): 1752-1759. |
20 | Oganov A R, Glass C W. Crystal structure prediction using ab initio evolutionary techniques: principles and applications[J]. The Journal of Chemical Physics, 2006, 124(24): 244704. |
21 | Shayeghi A, Götz D, Davis J B A, et al. Pool-BCGA: a parallelised generation-free genetic algorithm for the ab initio global optimisation of nanoalloy clusters[J]. Physical Chemistry Chemical Physics, 2015, 17(3): 2104-2112. |
22 | Zhang J, Hu P, Wang H. Amorphous catalysis: machine learning driven high-throughput screening of superior active site for hydrogen evolution reaction[J]. The Journal of Physical Chemistry C, 2020, 124(19): 10483-10494. |
23 | Zhong M, Tran K, Min Y, et al. Accelerated discovery of CO2 electrocatalysts using active machine learning[J]. Nature, 2020, 581(7807): 178-183. |
24 | Bisbo M K, Hammer B. Efficient global structure optimization with a machine-learned surrogate model[J]. Physical Review Letters, 2020, 124(8): 086102. |
25 | Le T C, Winkler D A. Discovery and optimization of materials using evolutionary approaches[J]. Chemical Reviews, 2016, 116(10): 6107-6132. |
26 | Gobin O C, Schüth F. On the suitability of different representations of solid catalysts for combinatorial library design by genetic algorithms[J]. Journal of Combinatorial Chemistry, 2008, 10(6): 835-846. |
27 | Tipton W W, Hennig R G. A grand canonical genetic algorithm for the prediction of multi-component phase diagrams and testing of empirical potentials[J]. Journal of Physics: Condensed Matter, 2013, 25(49): 495401. |
28 | Rodemerck U, Baerns M, Holena M, et al. Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials[J]. Applied Surface Science, 2004, 223(1): 168-174. |
29 | Lv J, Wang Y, Zhu L, et al. Particle-swarm structure prediction on clusters[J]. The Journal of Chemical Physics, 2012, 137(8): 084104. |
30 | Lyakhov A O, Oganov A R, Stokes H T, et al. New developments in evolutionary structure prediction algorithm USPEX[J]. Computer Physics Communications, 2013, 184(4): 1172-1182. |
31 | Wang Y, Lv J, Zhu L, et al. CALYPSO: a method for crystal structure prediction[J]. Computer Physics Communications, 2012, 183(10): 2063-2070. |
32 | Zhai H, Alexandrova A N. Ensemble-average representation of Pt clusters in conditions of catalysis accessed through GPU accelerated deep neural network fitting global optimization[J]. Journal of Chemical Theory and Computation, 2016, 12(12): 6213-6226. |
33 | Hajinazar S, Sandoval E D, Cullo A J, et al. Multitribe evolutionary search for stable Cu–Pd–Ag nanoparticles using neural network models[J]. Physical Chemistry Chemical Physics, 2019, 21(17): 8729-8742. |
34 | Jennings P C, Lysgaard S, Hummelshøj J S, et al. Genetic algorithms for computational materials discovery accelerated by machine learning[J]. npj Computational Materials, 2019, 5(1): 46. |
35 | Curtis F, Li X, Rose T, et al. GAtor: a first-principles genetic algorithm for molecular crystal structure prediction[J]. Journal of Chemical Theory and Computation, 2018, 14(4): 2246-2264. |
36 | Maldonis J J, Xu Z, Song Z, et al. StructOpt: a modular materials structure optimization suite incorporating experimental data and simulated energies[J]. Computational Materials Science, 2019, 160: 1-8. |
37 | Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. |
38 | Pereira S R M, Clerc F, Farrusseng D, et al. Effect of the genetic algorithm parameters on the optimisation of heterogeneous catalysts[J]. QSAR & Combinatorial Science, 2005, 24(1): 45-57. |
39 | Yee L, Yong G, Zong-Ben X. Degree of population diversity - a perspective on premature convergence in genetic algorithms and its Markov chain analysis[J]. IEEE Transactions on Neural Networks, 1997, 8(5): 1165-1176. |
40 | Rata I, Shvartsburg A A, Horoi M, et al. Single-parent evolution algorithm and the optimization of Si clusters[J]. Physical Review Letters, 2000, 85(3): 546-549. |
41 | Jäger M, Schäfer R, Johnston R L. GIGA: a versatile genetic algorithm for free and supported clusters and nanoparticles in the presence of ligands[J]. Nanoscale, 2019, 11(18): 9042-9052. |
42 | Jørgensen M S, Groves M N, Hammer B. Combining evolutionary algorithms with clustering toward rational global structure optimization at the atomic scale[J]. Journal of Chemical Theory and Computation, 2017, 13(3): 1486-1493. |
43 | Supady A, Blum V, Baldauf C. First-principles molecular structure search with a genetic algorithm[J]. Journal of Chemical Information and Modeling, 2015, 55(11): 2338-2348. |
44 | Davis J B A, Shayeghi A, Horswell S L, et al. The Birmingham parallel genetic algorithm and its application to the direct DFT global optimisation of IrN(N = 10–20) clusters[J]. Nanoscale, 2015, 7(33): 14032-14038. |
45 | Hussein H A, Demiroglu I, Johnston R L. Application of a parallel genetic algorithm to the global optimization of medium-sized Au–Pd sub-nanometre clusters[J]. The European Physical Journal B, 2018, 91(2): 34. |
46 | Demiroglu I, Yao K, Hussein H A, et al. DFT global optimization of gas-phase subnanometer Ru–Pt clusters[J]. The Journal of Physical Chemistry C, 2017, 121(20): 10773-10780. |
47 | Hussein H A, Davis J B A, Johnston R L. DFT global optimisation of gas-phase and MgO-supported sub-nanometre AuPd clusters[J]. Physical Chemistry Chemical Physics, 2016, 18(37): 26133-26143. |
48 | Habershon S, Harris K D M, Johnston R L. Development of a multipopulation parallel genetic algorithm for structure solution from powder diffraction data[J]. Journal of Computational Chemistry, 2003, 24(14): 1766-1774. |
49 | Fan T E, Shao G F, Ji Q S, et al. A multi-populations multi-strategies differential evolution algorithm for structural optimization of metal nanoclusters[J]. Computer Physics Communications, 2016, 208: 64-72. |
50 | Jiang B, Li J, Guo H. Potential energy surfaces from high fidelity fitting of ab initio points: the permutation invariant polynomial - neural network approach[J]. International Reviews in Physical Chemistry, 2016, 35(3): 479-506. |
51 | Li H, Zhang Z, Liu Z. Application of artificial neural networks for catalysis: a review[J]. Catalysts, 2017, 7(10): 306. |
52 | Le T, Epa V C, Burden F R, et al. Quantitative structure-property relationship modeling of diverse materials properties[J]. Chemical Reviews, 2012, 112(5): 2889-2919. |
53 | Kamath A, Vargas-Hernández R A, Krems R V, et al. Neural networks vs Gaussian process regression for representing potential energy surfaces: a comparative study of fit quality and vibrational spectrum accuracy[J]. The Journal of Chemical Physics, 2018, 148(24): 241702. |
54 | Chmiela S, Tkatchenko A, Sauceda H E, et al. Machine learning of accurate energy-conserving molecular force fields[J]. Science Advances, 2017, 3(5): e1603015. |
55 | Chen X, Jørgensen M S, Li J, et al. Atomic energies from a convolutional neural network[J]. Journal of Chemical Theory and Computation, 2018, 14(7): 3933-3942. |
56 | Rupp M, Tkatchenko A, Müller K R, et al. Fast and accurate modeling of molecular atomization energies with machine learning[J]. Physical Review Letters, 2012, 108(5): 058301. |
57 | Ramakrishnan R, Dral P O, Rupp M, et al. Quantum chemistry structures and properties of 134 kilo molecules[J]. Scientific Data, 2014, 1(1): 140022. |
58 | Behler J. Constructing high-dimensional neural network potentials: a tutorial review[J]. International Journal of Quantum Chemistry, 2015, 115(16): 1032-1050. |
59 | Zhang L, Lin D Y, Wang H, et al. Active learning of uniformly accurate interatomic potentials for materials simulation[J]. Physical Review Materials, 2019, 3(2): 023804. |
60 | Kolsbjerg E L, Peterson A A, Hammer B. Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles[J]. Physical Review B, 2018, 97(19): 195424. |
61 | Sørensen K H, Jørgensen M S, Bruix A, et al. Accelerating atomic structure search with cluster regularization[J]. The Journal of Chemical Physics, 2018, 148(24): 241734. |
62 | Jørgensen M S, Larsen U F, Jacobsen K W, et al. Exploration versus exploitation in global atomistic structure optimization[J]. The Journal of Physical Chemistry A, 2018, 122(5): 1504-1509. |
63 | Grosan C, Abraham A. Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews [M]. Berlin, Heidelberg: Springer-Verlag, 2007: 1-17. |
64 | Chen P H, Shahandashti S M. Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints[J]. Automation in Construction, 2009, 18(4): 434-443. |
65 | Zacharias C R, Lemes M R, Dal Pino A. Combining genetic algorithm and simulated annealing: a molecular geometry optimization study[J]. Journal of Molecular Structure: THEOCHEM, 1998, 430: 29-39. |
66 | Cai W, Jiang H, Shao X. Global optimization of lennard-jones clusters by a parallel fast annealing evolutionary algorithm[J]. Journal of Chemical Information and Computer Sciences, 2002, 42(5): 1099-1103. |
67 | Zeiri Y. Study of the lowest energy structure of atomic clusters using a genetic algorithm[J]. Computer Physics Communications, 1997, 103(1): 28-42. |
68 | Alexandrova A N, Boldyrev A I. Search for the Lin0/+1/-1 (n = 5-7) lowest-energy structures using the ab initio gradient embedded genetic algorithm (GEGA). Elucidation of the chemical bonding in the lithium clusters[J]. Journal of Chemical Theory and Computation, 2005, 1(4): 566-580. |
69 | Dieterich J M, Hartke B. OGOLEM: global cluster structure optimisation for arbitrary mixtures of flexible molecules. A multiscaling, object-oriented approach[J]. Molecular Physics, 2010, 108(3/4): 279-291. |
70 | Vargas J A, Buendía F, Beltrán M R. New AuN (N = 27–30) lowest energy clusters obtained by means of an improved DFT–genetic algorithm methodology[J]. The Journal of Physical Chemistry C, 2017, 121(20): 10982-10991. |
71 | Yamazoe S, Koyasu K, Tsukuda T. Nonscalable oxidation catalysis of gold clusters[J]. Accounts of Chemical Research, 2013, 47(3): 816-824. |
72 | Assadollahzadeh B, Schwerdtfeger P. A systematic search for minimum structures of small gold clusters Aun (n=2–20) and their electronic properties[J]. The Journal of Chemical Physics, 2009, 131(6): 064306. |
73 | Li T X, Yin S Y, Ji Y L, et al. A genetic algorithm study on the most stable disordered and ordered configurations of Au38–55[J]. Physics Letters A, 2000, 267(5/6): 403-407. |
74 | Shayeghi A, Johnston R L, Schäfer R. Evaluation of photodissociation spectroscopy as a structure elucidation tool for isolated clusters: a case study of Ag4+ and Au4+[J]. Physical Chemistry Chemical Physics, 2013, 15(45): 19715-19723. |
75 | Gilb S, Weis P, Furche F, et al. Structures of small gold cluster cations (Aun+, n<14): ion mobility measurements versus density functional calculations[J]. The Journal of Chemical Physics, 2002, 116(10): 4094-4101. |
76 | Hong L, Wang H, Cheng J, et al. Atomic structures and electronic properties of small Au–Ag binary clusters: effects of size and composition[J]. Computational and Theoretical Chemistry, 2012, 993: 36-44. |
77 | Kang X, Li Y, Zhu M, et al. Atomically precise alloy nanoclusters: syntheses, structures, and properties[J]. Chemical Society Reviews, 2020, 49: 6443-6514. |
78 | Xie S, Tsunoyama H, Kurashige W, et al. Enhancement in aerobic alcohol oxidation catalysis of Au25 clusters by single Pd atom doping[J]. ACS Catalysis, 2012, 2(7): 1519-1523. |
79 | Liu C, Ren X, Lin F, et al. Structure of the Au23-xAgx(S-Adm)15 nanocluster and its application for photocatalytic degradation of organic pollutants[J]. Angewandte Chemie International Edition, 2019, 58(33): 11335-11339. |
80 | Heiles S, Logsdail A J, Schäfer R, et al. Dopant-induced 2D–3D transition in small Au-containing clusters: DFT-global optimisation of 8-atom Au–Ag nanoalloys[J]. Nanoscale, 2012, 4(4): 1109-1115. |
81 | Davis J B A, Horswell S L, Johnston R L. Global optimization of 8–10 atom palladium–iridium nanoalloys at the DFT level[J]. The Journal of Physical Chemistry A, 2013, 118(1): 208-214. |
82 | Heard C J, Johnston R L. A density functional global optimisation study of neutral 8-atom Cu-Ag and Cu-Au clusters[J]. The European Physical Journal D, 2013, 67(2): 34. |
83 | Luo Z, Nachammai V, Zhang B, et al. Toward understanding the growth mechanism: tracing all stable intermediate species from reduction of Au(I)–thiolate complexes to evolution of Au25 nanoclusters[J]. Journal of the American Chemical Society, 2014, 136(30): 10577-10580. |
84 | Hussein H A, Demiroglu I, Johnston R L. Application of a parallel genetic algorithm to the global optimization of medium-sized Au–Pd sub-nanometre clusters[J]. The European Physical Journal B, 2018, 91(2): 34. |
85 | Bailey M S, Wilson N T, Roberts C, et al. Structures, stabilities and ordering in Ni-Al nanoalloy clusters[J]. The European Physical Journal D - Atomic, Molecular, Optical and Plasma Physics, 2003, 25(1): 41-55. |
86 | Zhang J, Glezakou V A, Rousseau R, et al. NWPEsSe: an adaptive-learning global optimization algorithm for nanosized cluster systems[J]. Journal of Chemical Theory and Computation, 2020, 16(6): 3947-3958. |
87 | Sierka M, Todorova T K, Kaya S, et al. Interplay between theory and experiment in the quest for silica with reduced dimensionality grown on a Mo(112) surface[J]. Chemical Physics Letters, 2006, 424(1/2/3): 115-119. |
88 | Hjorth L A, Jørgen M J, Blomqvist J, et al. The atomic simulation environment—a Python library for working with atoms[J]. Journal of Physics: Condensed Matter, 2017, 29(27): 273002. |
89 | Vilhelmsen L B, Walton K S, Sholl D S. Structure and mobility of metal clusters in MOFs: Au, Pd, and AuPd clusters in MOF-74[J]. Journal of the American Chemical Society, 2012, 134(30): 12807-12816. |
90 | He Y, Liu J C, Luo L, et al. Size-dependent dynamic structures of supported gold nanoparticles in CO oxidation reaction condition[J]. Proceedings of the National Academy of Sciences, 2018, 115(30): 7700-7705. |
91 | Sun G, Alexandrova A N, Sautet P. Pt8 cluster on alumina under a pressure of hydrogen: support-dependent reconstruction from first-principles global optimization[J]. The Journal of Chemical Physics, 2019, 151(19): 194703. |
92 | Vilhelmsen L B, Hammer B. Systematic study of Au6 to Au12 gold clusters on MgO(100) F-centers using density-functional theory[J]. Physical Review Letters, 2012, 108(12): 126101. |
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