Natl Sci Open
Volume 3, Number 2, 2024
Special Topic: AI for Chemistry
Article Number 20230088
Number of page(s) 14
Section Chemistry
Published online 20 March 2024
  • Jena P, Sun Q. Super atomic clusters: Design rules and potential for building blocks of materials. Chem Rev 2018; 118: 5755-5870. [Article] [Google Scholar]
  • Nair AS, Pathak B. Computational strategies to address the catalytic activity of nanoclusters. REs Comput Mol Sci 2021; 11: e1508. [Article] [Google Scholar]
  • Heiz U, Landman U. Nanocatalysis. Berlin, Heidelberg: Springer, 2007. [Google Scholar]
  • Saptarshi SR, Duschl A, Lopata AL. Interaction of nanoparticles with proteins: Relation to bio-reactivity of the nanoparticle. J Nanobiotechnol 2013; 11: 26. [Article] [Google Scholar]
  • Salata OV. Applications of nanoparticles in biology and medicine. J Nanobiotechnol 2004; 2: 3. [Article] [Google Scholar]
  • Zhuang ZH, Chen W. Application of atomically precise metal nanoclusters in electrocatalysis. J Electrochem, 2021; 27: 125. [Google Scholar]
  • Fichthorn KA, Yan T. Shapes and shape transformations of solution-phase metal particles in the sub-nanometer to nanometer size range: Progress and challenges. J Phys Chem C 2021; 125: 3668-3679. [Article] [Google Scholar]
  • Coquet R, Howard KL, Willock DJ. Theory and simulation in heterogeneous gold catalysis. Chem Soc Rev 2008; 37: 2046-2076. [Article] [Google Scholar]
  • Zhang J, Glezakou V. Global optimization of chemical cluster structures: Methods, applications, and challenges. nt J Quantum Chem 2021; 121: e26553. [Article] [Google Scholar]
  • Shnoudeh AJ, Hamad I, Abdo RW, et al. Synthesis, characterization, and applications of metal nanoparticles. Biomater Bionanotechnol 2019: 527-612. [Google Scholar]
  • Wei JQ, Chen XD, Li SZ. Electrochemical syntheses of nanomaterials and small molecules for electrolytic hydrogen production. J Electrochem 2022; 28: 2214012. [Google Scholar]
  • Doye JPK, Wales DJ. Global minima for transition metal clusters described by Sutton-Chen potentials. New J Chem 1998; 22: 733-744. [Article] [Google Scholar]
  • Wei GF, Liu ZP. Subnano Pt particles from a first-principles stochastic surface walking global search. J Chem Theor Comput 2016; 12: 4698-4706. [Article] [Google Scholar]
  • Vargas A, Santarossa G, Iannuzzi M, et al. Fluxionality of gold nanoparticles investigated by Born-Oppenheimer molecular dynamics. Phys Rev B 2009; 80: 195421. [Article] [Google Scholar]
  • Avanesian T, Dai S, Kale MJ, et al. Quantitative and atomic-scale view of CO-induced Pt nanoparticle surface reconstruction at saturation coverage via DFT calculations coupled with in situ TEM and IR. J Am Chem Soc 2017; 139: 4551-4558. [Article] [Google Scholar]
  • Pavan L, Rossi K, Baletto F. Metallic nanoparticles meet metadynamics. J Chem Phys 2015; 143: 184304. [Article] [Google Scholar]
  • Sun JJ, Cheng J. Solid-to-liquid phase transitions of sub-nanometer clusters enhance chemical transformation. Nat Commun 2019; 10: 5400. [Article] [Google Scholar]
  • Gong FQ, Guo YX, Fan QY, et al. Dynamic catalysis of sub-nanometer metal clusters in oxygen dissociation. Next Nanotechnol 2023; 1: 100002. [Article] [Google Scholar]
  • Lloyd LD, Johnston RL. Theoretical analysis of 17-19-atom metal clusters using many-body potentials. J Chem Soc Dalton Trans 2000; 307-316. [Article] [Google Scholar]
  • Lee MS, Chacko S, Kanhere DG. First-principles investigation of finite-temperature behavior in small sodium clusters. J Chem Phys 2005; 123: 164310. [Article] [Google Scholar]
  • Jiang W, Zhang Y, Zhang L, et al. Accurate deep potential model for the Al-Cu-Mg alloy in the full concentration space. Chin Phys B 2021; 30: 050706. [Article] [Google Scholar]
  • Guedes-Sobrinho D, Wang W, Hamilton IP, et al. (Meta-)stability and core-shell dynamics of gold nanoclusters at finite temperature. J Phys Chem Lett 2019; 10: 685-692. [Article] [Google Scholar]
  • Fonseca Guerra C, Snijders JG, te Velde G, et al. Towards an order-N DFT method. Theor Chem Accounts 1998; 99: 391-403. [Article] [Google Scholar]
  • Behler J. Perspective: Machine learning potentials for atomistic simulations. J Chem Phys 2016; 145: 170901. [Article] [Google Scholar]
  • Behler J, Csányi G. Machine learning potentials for extended systems: A perspective. Eur Phys J B 2021; 94: 142. [Article] [Google Scholar]
  • Chen L, Tian Y, Hu X, et al. A universal machine learning framework for electrocatalyst innovation: A case study of discovering alloys for hydrogen evolution reaction. Adv Funct Mater 2022; 32: 2208418. [Article] [Google Scholar]
  • Tuo P, Ye XB, Pan BC. A machine learning based deep potential for seeking the low-lying candidates of Al clusters. J Chem Phys 2020; 152: 114105. [Article] [Google Scholar]
  • Wang X, Wang H, Luo Q, et al. Structural and electrocatalytic properties of copper clusters: A study via deep learning and first principles. J Chem Phys 2022; 157: 074304. [Article] [Google Scholar]
  • Stark WJ, Stoessel PR, Wohlleben W, et al. Industrial applications of nanoparticles. Chem Soc Rev 2015; 44: 5793-5805. [Article] [Google Scholar]
  • Paz-Borbón LO, López-Martínez A, Garzón IL, et al. 2D-3D structural transition in sub-nanometer PtN clusters supported on CeO2 (111). Phys Chem Chem Phys 2017; 19: 17845-17855. [Article] [Google Scholar]
  • Zeng J, Zhang D, Lu D, et al. DeePMD-kit v2: A software package for deep potential models. J Chem Phys 2023; 159: 054801. [Article] [Google Scholar]
  • Behler J, Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett 2007; 98: 146401. [Article] [Google Scholar]
  • Hjorth LA, Jørgen MJ, Blomqvist J, et al. The atomic simulation environment—A Python library for working with atoms. J Phys-Condens Matter 2017; 29: 273002. [Article] [Google Scholar]
  • Gehrke R, Reuter K. Assessing the efficiency of first-principles basin-hopping sampling. Phys Rev B 2009; 79: 085412. [Article] [Google Scholar]
  • Zhang Y, Wang H, Chen W, et al. DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models. Comput Phys Commun 2020; 253: 107206. [Article] [Google Scholar]
  • Huang J, Zhang L, Wang H, et al. Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors. J Chem Phys 2021; 154: 094703. [Article] [Google Scholar]
  • Zhuang YB, Bi RH, Cheng J. Resolving the odd-even oscillation of water dissociation at rutile TiO2(110)-water interface by machine learning accelerated molecular dynamics. J Chem Phys 2022; 157: 164701. [Article] [Google Scholar]
  • Zhang L, Han J, Wang H, et al. End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems. In: Proceedings of the 32nd Conference on Neural Information Processing Systems. Montreal, 2018. [Google Scholar]
  • Thompson AP, Aktulga HM, Berger R, et al. LAMMPS-A flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput Phys Commun 2022; 271: 108171. [Article] [Google Scholar]
  • Hutter J, Iannuzzi M, Schiffmann F, et al. CP2K: Atomistic simulations of condensed matter systems. REs Comput Mol Sci 2014; 4: 15-25. [Article] [Google Scholar]
  • Vande VJ, Krack M, Mohamed F, et al. Quickstep: Fast and accurate density functional calculations using a mixed Gaussian and plane waves approach. Comput Phys Commun 2005; 167: 103-128. [Article] [Google Scholar]
  • Perdew JP, Burke K, Ernzerhof M. Generalized gradient approximation made simple. Phys Rev Lett 1996; 77: 3865-3868. [Article] [Google Scholar]
  • Grimme S, Antony J, Ehrlich S, et al. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J Chem Phys 2010; 132: 154104. [Article] [Google Scholar]
  • Hartwigsen C, Goedecker S, Hutter J. Relativistic separable dual-space Gaussian pseudopotentials from H to Rn. Phys Rev B 1998; 58: 3641-3662. [Article] [Google Scholar]
  • Vande VJ, Hutter J. Gaussian basis sets for accurate calculations on molecular systems in gas and condensed phases. J Chem Phys 2007; 127: 114105. [Article] [Google Scholar]
  • Jain A, Ong SP, Hautier G, et al. Commentary: The materials project: A materials genome approach to accelerating materials innovation. APL Mater 2013; 1: 011002. [Article] [Google Scholar]
  • Wales DJ, Doye JPK, Dullweber A, et al. The cambridge cluster database. [Google Scholar]
  • Bartók AP, Kondor R, Csányi G. On representing chemical environments. Phys Rev B 2013; 87: 184115. [Article] [Google Scholar]
  • Zhang L, Wang H, Car R, et al. Phase diagram of a deep potential water model. Phys Rev Lett 2021; 126: 236001. [Article] [Google Scholar]
  • Pinheiro M, Ge F, Ferré N, et al. Choosing the right molecular machine learning potential. Chem Sci 2021; 12: 14396-14413. [Article] [Google Scholar]
  • Valsson O, Parrinello M. Thermodynamical description of a quasi-first-order phase transition from the well-tempered ensemble. J Chem Theor Comput 2013; 9: 5267-5276. [Article] [Google Scholar]
  • Hansen K. Statistical Physics of Nanoparticles in the Gas Phase. Vol. 2. Cham: Springer, 2013. [Google Scholar]
  • Zhao SJ, Wang SQ, Cheng DY, et al. Three distinctive melting mechanisms in isolated nanoparticles. J Phys Chem B 2001; 105: 12857-12860. [Article] [Google Scholar]
  • Schmidt M, Haberland H. Phase transitions in clusters. Comptes Rendus Physique 2002; 3: 327-340. [Article] [Google Scholar]
  • Zhang X, Li B, Liu HX, et al. Atomic simulation of melting and surface segregation of ternary Fe-Ni-Cr nanoparticles. Appl Surf Sci 2019; 465: 871-879. [Article] [Google Scholar]
  • Foster DM, Pavloudis T, Kioseoglou J, et al. Atomic-resolution imaging of surface and core melting in individual size-selected Au nanoclusters on carbon. Nat Commun 2019; 10: 2583. [Article] [Google Scholar]
  • Zhang D, Bi H, Dai FZ, et al. DPA-1: Pretraining of attention-based deep potential model for molecular simulation. arXiv: [Google Scholar]

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