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Volumn 12, Issue 12, 2016, Pages 6213-6226

Ensemble-Average Representation of Pt Clusters in Conditions of Catalysis Accessed through GPU Accelerated Deep Neural Network Fitting Global Optimization

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EID: 85006049734     PISSN: 15499618     EISSN: 15499626     Source Type: Journal    
DOI: 10.1021/acs.jctc.6b00994     Document Type: Article
Times cited : (132)

References (61)
  • 1
    • 77956829762 scopus 로고    scopus 로고
    • Platinum and non-platinum nanomaterials for the molecular oxygen reduction reaction
    • Alonso-Vante, N. Platinum and non-platinum nanomaterials for the molecular oxygen reduction reaction ChemPhysChem 2010, 11, 2732-2744 10.1002/cphc.200900817
    • (2010) ChemPhysChem , vol.11 , pp. 2732-2744
    • Alonso-Vante, N.1
  • 2
    • 84943148794 scopus 로고    scopus 로고
    • Alloying Pt Sub-nano-clusters with Boron: Sintering Preventative and Coke Antagonist?
    • Dadras, J.; Jimenez-Izal, E.; Alexandrova, A. N. Alloying Pt Sub-nano-clusters with Boron: Sintering Preventative and Coke Antagonist? ACS Catal. 2015, 5, 5719-5727 10.1021/acscatal.5b01513
    • (2015) ACS Catal. , vol.5 , pp. 5719-5727
    • Dadras, J.1    Jimenez-Izal, E.2    Alexandrova, A.N.3
  • 3
    • 84959019065 scopus 로고    scopus 로고
    • Observable Electrochemical Oxidation of Carbon Promoted by Platinum Nanoparticles
    • Kou, Z.; Cheng, K.; Wu, H.; Sun, R.; Guo, B.; Mu, S. Observable Electrochemical Oxidation of Carbon Promoted by Platinum Nanoparticles ACS Appl. Mater. Interfaces 2016, 8, 3940-3947 10.1021/acsami.5b11086
    • (2016) ACS Appl. Mater. Interfaces , vol.8 , pp. 3940-3947
    • Kou, Z.1    Cheng, K.2    Wu, H.3    Sun, R.4    Guo, B.5    Mu, S.6
  • 4
    • 27744594711 scopus 로고    scopus 로고
    • 0/+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
    • 0/+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. Chem. Theory Comput. 2005, 1, 566-580 10.1021/ct050093g
    • (2005) J. Chem. Theory Comput. , vol.1 , pp. 566-580
    • Alexandrova, A.N.1    Boldyrev, A.I.2
  • 5
    • 78649901966 scopus 로고    scopus 로고
    • n clusters: Microsolvation of the hydrogen atom via molecular ab initio gradient embedded genetic algorithm (GEGA)
    • n clusters: Microsolvation of the hydrogen atom via molecular ab initio gradient embedded genetic algorithm (GEGA) J. Phys. Chem. A 2010, 114, 12591-12599 10.1021/jp1092543
    • (2010) J. Phys. Chem. A , vol.114 , pp. 12591-12599
    • Alexandrova, A.N.1
  • 6
    • 84916891481 scopus 로고    scopus 로고
    • CLUSTER: Searching for Unique Low Energy Minima of Structures Using a Novel Implementation of a Genetic Algorithm
    • Kanters, R. P. F.; Donald, K. J. CLUSTER: Searching for Unique Low Energy Minima of Structures Using a Novel Implementation of a Genetic Algorithm J. Chem. Theory Comput. 2014, 10, 5729-5737 10.1021/ct500744k
    • (2014) J. Chem. Theory Comput. , vol.10 , pp. 5729-5737
    • Kanters, R.P.F.1    Donald, K.J.2
  • 8
    • 33744478421 scopus 로고    scopus 로고
    • Larger water clusters with edges and corners on their way to ice: Structural trends elucidated with an improved parallel evolutionary algorithm
    • Bandow, B.; Hartke, B. Larger water clusters with edges and corners on their way to ice: Structural trends elucidated with an improved parallel evolutionary algorithm J. Phys. Chem. A 2006, 110, 5809-5822 10.1021/jp060512l
    • (2006) J. Phys. Chem. A , vol.110 , pp. 5809-5822
    • Bandow, B.1    Hartke, B.2
  • 9
    • 34247539565 scopus 로고    scopus 로고
    • Global minimum structure searches via particle swarm optimization
    • Call, S. T.; Zubarev, D. Y.; Boldyrev, A. I. Global minimum structure searches via particle swarm optimization J. Comput. Chem. 2007, 28, 1177-1186 10.1002/jcc.20621
    • (2007) J. Comput. Chem. , vol.28 , pp. 1177-1186
    • Call, S.T.1    Zubarev, D.Y.2    Boldyrev, A.I.3
  • 10
    • 84978864099 scopus 로고    scopus 로고
    • Firefly Algorithm for Structural Search
    • Avendaño-Franco, G.; Romero, A. H. Firefly Algorithm for Structural Search J. Chem. Theory Comput. 2016, 12, 3416-3428 10.1021/acs.jctc.5b01157
    • (2016) J. Chem. Theory Comput. , vol.12 , pp. 3416-3428
    • Avendaño-Franco, G.1    Romero, A.H.2
  • 14
    • 84929191562 scopus 로고    scopus 로고
    • AFFCK: Adaptive Force-Field-Assisted ab initio Coalescence Kick method for global minimum search
    • Zhai, H.; Ha, H.-A.; Alexandrova, A. N. AFFCK: Adaptive Force-Field-Assisted ab initio Coalescence Kick method for global minimum search J. Chem. Theory Comput. 2015, 11, 2385-2393 10.1021/acs.jctc.5b00065
    • (2015) J. Chem. Theory Comput. , vol.11 , pp. 2385-2393
    • Zhai, H.1    Ha, H.-A.2    Alexandrova, A.N.3
  • 15
    • 0141947503 scopus 로고
    • Potential energy surfaces for macromolecules. A neural network technique
    • Sumpter, B. G.; Noid, D. W. Potential energy surfaces for macromolecules. A neural network technique Chem. Phys. Lett. 1992, 192, 455-462 10.1016/0009-2614(92)85498-Y
    • (1992) Chem. Phys. Lett. , vol.192 , pp. 455-462
    • Sumpter, B.G.1    Noid, D.W.2
  • 16
    • 49149107508 scopus 로고    scopus 로고
    • Parameterization of analytic interatomic potential functions using neural networks
    • Malshe, M.; Narulkar, R.; Raff, L. M.; Hagan, M.; Bukkapatnam, S.; Komanduri, R. Parameterization of analytic interatomic potential functions using neural networks J. Chem. Phys. 2008, 129, 044111 10.1063/1.2957490
    • (2008) J. Chem. Phys. , vol.129 , pp. 044111
    • Malshe, M.1    Narulkar, R.2    Raff, L.M.3    Hagan, M.4    Bukkapatnam, S.5    Komanduri, R.6
  • 17
    • 75249087503 scopus 로고    scopus 로고
    • 2 + H on an ab Initio Potential-Energy Surface Obtained Using Neural Network Methods with Both Potential and Gradient Accuracy Determination
    • 2 + H on an ab Initio Potential-Energy Surface Obtained Using Neural Network Methods with Both Potential and Gradient Accuracy Determination J. Phys. Chem. A 2010, 114, 45-53 10.1021/jp907507z
    • (2010) J. Phys. Chem. A , vol.114 , pp. 45-53
    • Le, H.M.1    Raff, L.M.2
  • 18
    • 84903362821 scopus 로고    scopus 로고
    • Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems
    • Li, J.; Jiang, B.; Guo, H. Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems J. Chem. Phys. 2013, 139, 204103 10.1063/1.4832697
    • (2013) J. Chem. Phys. , vol.139 , pp. 204103
    • Li, J.1    Jiang, B.2    Guo, H.3
  • 19
    • 79953856961 scopus 로고    scopus 로고
    • Atom-centered symmetry functions for constructing high-dimensional neural network potentials
    • Behler, J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials J. Chem. Phys. 2011, 134, 074106 10.1063/1.3553717
    • (2011) J. Chem. Phys. , vol.134 , pp. 074106
    • Behler, J.1
  • 20
    • 84936800620 scopus 로고    scopus 로고
    • Constructing high-dimensional neural network potentials: A tutorial review
    • Behler, J. Constructing high-dimensional neural network potentials: A tutorial review Int. J. Quantum Chem. 2015, 115, 1032-1050 10.1002/qua.24890
    • (2015) Int. J. Quantum Chem. , vol.115 , pp. 1032-1050
    • Behler, J.1
  • 21
    • 79961106334 scopus 로고    scopus 로고
    • High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
    • Artrith, N.; Morawietz, T.; Behler, J. High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide Phys. Rev. B: Condens. Matter Mater. Phys. 2011, 83, 153101 10.1103/PhysRevB.83.153101
    • (2011) Phys. Rev. B: Condens. Matter Mater. Phys. , vol.83 , pp. 153101
    • Artrith, N.1    Morawietz, T.2    Behler, J.3
  • 22
    • 84940996633 scopus 로고    scopus 로고
    • Global minimization of gold clusters by combining neural network potentials and the basin-hopping method
    • Ouyang, R.; Xie, Y.; Jiang, D. Global minimization of gold clusters by combining neural network potentials and the basin-hopping method Nanoscale 2015, 7, 14817-14821 10.1039/C5NR03903G
    • (2015) Nanoscale , vol.7 , pp. 14817-14821
    • Ouyang, R.1    Xie, Y.2    Jiang, D.3
  • 24
    • 33748257982 scopus 로고    scopus 로고
    • A random-sampling high dimensional model representation neural network for building potential energy surfaces
    • Manzhos, S.; Carrington, T. A random-sampling high dimensional model representation neural network for building potential energy surfaces J. Chem. Phys. 2006, 125, 084109 10.1063/1.2336223
    • (2006) J. Chem. Phys. , vol.125 , pp. 084109
    • Manzhos, S.1    Carrington, T.2
  • 25
    • 57649225620 scopus 로고    scopus 로고
    • Using neural networks, optimized coordinates, and high-dimensional model representations to obtain a vinyl bromide potential surface
    • Manzhos, S.; Carrington, T. Using neural networks, optimized coordinates, and high-dimensional model representations to obtain a vinyl bromide potential surface J. Chem. Phys. 2008, 129, 224104 10.1063/1.3021471
    • (2008) J. Chem. Phys. , vol.129 , pp. 224104
    • Manzhos, S.1    Carrington, T.2
  • 26
    • 69349090197 scopus 로고    scopus 로고
    • Learning Deep Architectures for AI
    • Bengio, Y. Learning Deep Architectures for AI Found. Trends Mach. Learn. 2009, 2, 1-127 10.1561/2200000006
    • (2009) Found. Trends Mach. Learn. , vol.2 , pp. 1-127
    • Bengio, Y.1
  • 31
    • 0011596225 scopus 로고
    • Approximate single-valued representations of multivalued potential energy surfaces
    • Murrell, J. N.; Carter, S. Approximate single-valued representations of multivalued potential energy surfaces J. Phys. Chem. 1984, 88, 4887-4891 10.1021/j150665a016
    • (1984) J. Phys. Chem. , vol.88 , pp. 4887-4891
    • Murrell, J.N.1    Carter, S.2
  • 32
    • 27744577658 scopus 로고
    • Modeling solid-state chemistry: Interatomic potentials for multicomponent systems
    • Tersoff, J. Modeling solid-state chemistry: Interatomic potentials for multicomponent systems Phys. Rev. B: Condens. Matter Mater. Phys. 1989, 39, 5566-5568 10.1103/PhysRevB.39.5566
    • (1989) Phys. Rev. B: Condens. Matter Mater. Phys. , vol.39 , pp. 5566-5568
    • Tersoff, J.1
  • 38
    • 0031345518 scopus 로고    scopus 로고
    • Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
    • Zhu, C.; Byrd, R. H.; Lu, P.; Nocedal, J. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization ACM Trans. Math. Softw. 1997, 23, 550-560 10.1145/279232.279236
    • (1997) ACM Trans. Math. Softw. , vol.23 , pp. 550-560
    • Zhu, C.1    Byrd, R.H.2    Lu, P.3    Nocedal, J.4
  • 39
    • 3342932276 scopus 로고    scopus 로고
    • Total and Local Quadratic Indices of the Molecular Pseudograph’s Atom Adjacency Matrix: Applications to the Prediction of Physical Properties of Organic Compounds
    • Ponce, Y. M. Total and Local Quadratic Indices of the Molecular Pseudograph’s Atom Adjacency Matrix: Applications to the Prediction of Physical Properties of Organic Compounds Molecules 2003, 8, 687-726 10.3390/80900687
    • (2003) Molecules , vol.8 , pp. 687-726
    • Ponce, Y.M.1
  • 40
    • 40949156135 scopus 로고    scopus 로고
    • Periodic trends in the geometric structures of 13-atom metal clusters
    • Sun, Y.; Zhang, M.; Fournier, R. Periodic trends in the geometric structures of 13-atom metal clusters Phys. Rev. B 2008, 77, 075435 10.1103/PhysRevB.77.075435
    • (2008) Phys. Rev. B , vol.77 , pp. 075435
    • Sun, Y.1    Zhang, M.2    Fournier, R.3
  • 41
    • 84865796611 scopus 로고    scopus 로고
    • Particle-swarm structure prediction on clusters
    • Lv, J.; Wang, Y.; Zhu, L.; Ma, Y. Particle-swarm structure prediction on clusters J. Chem. Phys. 2012, 137, 084104 10.1063/1.4746757
    • (2012) J. Chem. Phys. , vol.137 , pp. 084104
    • Lv, J.1    Wang, Y.2    Zhu, L.3    Ma, Y.4
  • 42
    • 43049106458 scopus 로고    scopus 로고
    • Improved real-space genetic algorithm for crystal structure and polymorph prediction
    • Abraham, N. L.; Probert, M. I. J. Improved real-space genetic algorithm for crystal structure and polymorph prediction Phys. Rev. B: Condens. Matter Mater. Phys. 2008, 77, 134117 10.1103/PhysRevB.77.134117
    • (2008) Phys. Rev. B: Condens. Matter Mater. Phys. , vol.77 , pp. 134117
    • Abraham, N.L.1    Probert, M.I.J.2
  • 43
    • 84874438960 scopus 로고    scopus 로고
    • Even, G. Ed. Cambridge University Press: Cambridge
    • Even, S. Graph Algorithms; Even, G., Ed.; Cambridge University Press: Cambridge, 2011; pp 46-48.
    • (2011) Graph Algorithms , pp. 46-48
    • Even, S.1
  • 44
    • 85005946990 scopus 로고    scopus 로고
    • TURBOMOLE V6.6 2014, a development of University of Karlsruhe and Forschungszentrum Karlsruhe GmbH, 1989-2007, TURBOMOLE GmbH, since. Available from (accessed Nov 14, 2016).
    • TURBOMOLE V6.6 2014, a development of University of Karlsruhe and Forschungszentrum Karlsruhe GmbH, 1989-2007, TURBOMOLE GmbH, since 2007. Available from http://www.turbomole.com (accessed Nov 14, 2016).
    • (2007)
  • 45
    • 4243402296 scopus 로고
    • Efficient molecular numerical integration schemes
    • Treutler, O.; Ahlrichs, R. Efficient molecular numerical integration schemes J. Chem. Phys. 1995, 102, 346 10.1063/1.469408
    • (1995) J. Chem. Phys. , vol.102 , pp. 346
    • Treutler, O.1    Ahlrichs, R.2
  • 46
    • 0347319419 scopus 로고    scopus 로고
    • Comparative assessment of a new nonempirical density functional: Molecules and hydrogen-bonded complexes
    • Staroverov, V. N.; Scuseria, G. E.; Tao, J.; Perdew, J. P. Comparative assessment of a new nonempirical density functional: Molecules and hydrogen-bonded complexes J. Chem. Phys. 2003, 119, 12129-12137 10.1063/1.1626543
    • (2003) J. Chem. Phys. , vol.119 , pp. 12129-12137
    • Staroverov, V.N.1    Scuseria, G.E.2    Tao, J.3    Perdew, J.P.4
  • 47
    • 84907993096 scopus 로고    scopus 로고
    • Hybrid Density Functionals for Clusters of Late Transition Metals: Assessing Energetic and Structural Properties
    • Soini, T. M.; Genest, A.; Nikodem, A.; Rosch, N. Hybrid Density Functionals for Clusters of Late Transition Metals: Assessing Energetic and Structural Properties J. Chem. Theory Comput. 2014, 10, 4408-4416 10.1021/ct500703q
    • (2014) J. Chem. Theory Comput. , vol.10 , pp. 4408-4416
    • Soini, T.M.1    Genest, A.2    Nikodem, A.3    Rosch, N.4
  • 48
    • 26244461462 scopus 로고    scopus 로고
    • Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy
    • Weigend, F.; Ahlrichs, R. Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy Phys. Chem. Chem. Phys. 2005, 7, 3297-3305 10.1039/b508541a
    • (2005) Phys. Chem. Chem. Phys. , vol.7 , pp. 3297-3305
    • Weigend, F.1    Ahlrichs, R.2
  • 49
    • 84919912886 scopus 로고    scopus 로고
    • Structure, vibrational, and optical properties of platinum cluster: a density functional theory approach
    • Singh, N. B.; Sarkar, U. Structure, vibrational, and optical properties of platinum cluster: a density functional theory approach J. Mol. Model. 2014, 20, 2537 10.1007/s00894-014-2537-5
    • (2014) J. Mol. Model. , vol.20 , pp. 2537
    • Singh, N.B.1    Sarkar, U.2
  • 50
    • 43949093830 scopus 로고    scopus 로고
    • Evolution of atomic and electronic structure of Pt clusters: Planar, layered, pyramidal, cage, cubic, and octahedral growth
    • Kumar, V.; Kawazoe, Y. Evolution of atomic and electronic structure of Pt clusters: Planar, layered, pyramidal, cage, cubic, and octahedral growth Phys. Rev. B: Condens. Matter Mater. Phys. 2008, 77, 205418 10.1103/PhysRevB.77.205418
    • (2008) Phys. Rev. B: Condens. Matter Mater. Phys. , vol.77 , pp. 205418
    • Kumar, V.1    Kawazoe, Y.2
  • 52
    • 84912523919 scopus 로고    scopus 로고
    • Structure of Small Platinum Clusters Revised
    • Winczewski, S.; Rybicki, J. Structure of Small Platinum Clusters Revised Comput. Methods Sci. Technol. 2011, 17, 75-85 10.12921/cmst.2011.17.01.75-85
    • (2011) Comput. Methods Sci. Technol. , vol.17 , pp. 75-85
    • Winczewski, S.1    Rybicki, J.2
  • 53
    • 2442537377 scopus 로고    scopus 로고
    • Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
    • Kresse, G.; Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set Phys. Rev. B: Condens. Matter Mater. Phys. 1996, 54, 11169-11186 10.1103/PhysRevB.54.11169
    • (1996) Phys. Rev. B: Condens. Matter Mater. Phys. , vol.54 , pp. 11169-11186
    • Kresse, G.1    Furthmüller, J.2
  • 55
    • 4243943295 scopus 로고    scopus 로고
    • Generalized Gradient Approximation Made Simple
    • Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized Gradient Approximation Made Simple Phys. Rev. Lett. 1996, 77, 3865-3868 10.1103/PhysRevLett.77.3865
    • (1996) Phys. Rev. Lett. , vol.77 , pp. 3865-3868
    • Perdew, J.P.1    Burke, K.2    Ernzerhof, M.3
  • 56
    • 84874892421 scopus 로고    scopus 로고
    • Ab initio random structure search for 13-atom clusters of fcc elements
    • Chou, J. P.; Hsing, C. R.; Wei, C. M.; Cheng, C.; Chang, C. M. Ab initio random structure search for 13-atom clusters of fcc elements J. Phys.: Condens. Matter 2013, 25, 125305 10.1088/0953-8984/25/12/125305
    • (2013) J. Phys.: Condens. Matter , vol.25 , pp. 125305
    • Chou, J.P.1    Hsing, C.R.2    Wei, C.M.3    Cheng, C.4    Chang, C.M.5
  • 59
    • 85005960004 scopus 로고    scopus 로고
    • MOLPRO, version 2015.1, a package of ab initio programs. See (accessed Nov 14, 2016).
    • Werner, H.-J.; Knowles, P. J.; Knizia, G.; Manby, F. R.; Schütz, M. et al.. MOLPRO, version 2015.1, a package of ab initio programs. See http://www.molpro.net (accessed Nov 14, 2016).
    • Werner, H.-J.1    Knowles, P.J.2    Knizia, G.3    Manby, F.R.4    Schütz, M.5
  • 60
    • 84963762071 scopus 로고    scopus 로고
    • Energy landscapes for a machine learning application to series data
    • Ballard, A. J.; Stevenson, J. D.; Das, R.; Wales, D. J. Energy landscapes for a machine learning application to series data J. Chem. Phys. 2016, 144, 124119 10.1063/1.4944672
    • (2016) J. Chem. Phys. , vol.144 , pp. 124119
    • Ballard, A.J.1    Stevenson, J.D.2    Das, R.3    Wales, D.J.4


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