Application of Deep-Learning Potential in Simulating the Structural and Physical Characteristics of Platinum
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Abstract
The deep potential (DP) is an innovative approach based on deep learning that uses ab initio calculation data derived from density functional theory (DFT), to create high-accuracy potential functions for various materials. Pl... | Find, read and cite all the research you need on Tech Science Press
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OpenAlex topics: Electron and X-Ray Spectroscopy Techniques · Machine Learning in Materials Science · Surface and Thin Film Phenomena
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References (65)
Show source-provided references
1. Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials
2. An empirical potential function of diatomic molecules10.1016/0375-9601(95)00552-E ↗
3. The effect of empirical potential functions on modeling of amorphous carbon using molecular dynamics method10.1016/j.apsusc.2013.09.073 ↗
4. Comparative study of empirical internuclear potential functions10.1103/RevModPhys.34.239 ↗
5. Comparison of semi-empirical potential functions for silicon and germanium10.1103/PhysRevB.47.7686 ↗
6. A new semiempirical potential function for hydrogen bonds and its possible use in studying the DNA molecule10.1016/0022-2860(93)80126-G ↗
7. On the consistent calculation of lattice dynamics using semiempirical potential functions10.1016/0301-0104(75)80113-3 ↗
8. Assessment of embedding schemes in a hybrid machine learning/classical potentials (ML/MM) approach10.1021/acs.jcim.4c00478 ↗
9. Machine-learning interatomic potentials for materials science10.1016/j.actamat.2021.116980 ↗
10. Cross-platform hyperparameter optimization for machine learning interatomic potentials10.1063/5.0155618 ↗
11. Liu K, Ni Z, Zhou Z, Tan S, Zou X, Xing H, et al. Molecular modeling with machine-learned universal potential functions. 2021. doi:10.48550/arXiv.2103.04162.
12. Development of a machine learning potential for the study of crack propagation in titanium10.1016/j.ijpvp.2021.104514 ↗
13. Mechanical properties of Mo-Re alloy based on first-principles and machine learning potential function
14. Predicting effective thermal conductivity of HGM composite using ML
15. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics10.1103/PhysRevLett.120.143001 ↗
16. Deep potential: a general representation of a many-body potential energy surface10.4208/cicp.OA-2017-0213 ↗
17. DeePMD-kit: a deep learning package for many-body potential energy representation and molecular dynamics10.1016/j.cpc.2018.03.016 ↗
18. Correlations of equilibrium properties and electronic structure of pure metals10.3390/ma12182932 ↗
19. Rapid acquisition of liquid thermophysical properties from pure metals to quaternary alloys by proposing a machine learning strategy10.1063/5.0160046 ↗
20. Ag(111) and its doped surfaces for heavy metal atoms removal from wastewater: DFT calculation analysis10.1016/j.jwpe.2023.104002 ↗
21. A generalizable machine learning potential of Ag-Au nanoalloys and its application to surface reconstruction, segregation and diffusion10.1088/1361-651X/ac4002 ↗
22. Accurate interatomic potential for the nucleation in liquid Ti-Al binary alloy developed by deep neural network learning method10.1016/j.commatsci.2022.111843 ↗
23. The thermoelectric performance of new structure SnSe studied by quotient graph and deep learning potential10.1016/j.mtener.2021.100665 ↗
24. Accurate Deep Potential model for the Al–Cu–Mg alloy in the full concentration space10.1088/1674-1056/abf134 ↗
25. Gartner · Proc Nat Acad Sci · 2020 · 117 · 2604010.1073/pnas.2015440117 ↗
26. Generative deep learning for targeted compound design10.1021/acs.jcim.0c01496 ↗
27. Atomic structure of liquid refractory Nb5Si3 intermetallic compound alloy based upon deep neural network potential10.1063/5.0067157 ↗
28. Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds10.1103/PhysRevB.100.174101 ↗
29. Pt nanoparticles electrodeposited on an ultrathin nitrogen-rich underlayer for methanol sensors10.1021/acsanm.4c01675 ↗
30. Recent advances in the development of single atom catalysts for oxygen evolution reaction10.1016/j.ijhydene.2024.08.026 ↗
31. Prediction descriptor for catalytic activity of platinum nanoparticles/metal-organic framework composites10.1021/acsami.1c10140 ↗
32. Synthesis and catalytic activity of platinum/porous silicon nanocomposites10.1007/s11172-016-1591-9 ↗
33. Electrochemical promotion of Pt impregnated catalyst for the treatment of automotive exhaust emissions10.1007/s10800-008-9559-6 ↗
34. Functionalized platinum nanoparticles with biomedical applications10.3390/ijms23169404 ↗
35. Elkoua · Opt Quantum Electron · 2022 · 54 · 100710.1007/s11082-022-03999-9 ↗
36. Belhamra · J Supercond Nov Magn · 2022 · 35 · 361310.1007/s10948-022-06405-3 ↗
37. DP-GEN: a concurrent learning platform for the generation of reliable deep learning based potential energy models10.1016/j.cpc.2020.107206 ↗
38. DeePMD-kit v2: a software package for deep potential models10.1063/5.0155600 ↗
39. Accurate and transferable machine learning potential for molecular dynamics simulation of sodium silicate glasses10.1021/acs.jctc.3c01115 ↗
40. Nonlocal electronic correlations in the cohesive properties of high-pressure hydrogen solids10.1021/acs.jpclett.9b03716 ↗
41. A phase field-based framework for electro-chemo-mechanical fracture: crack-contained electrolytes, chemical reactions and stabilisation10.1016/j.cma.2023.116235 ↗
42. Theoretical study of the crystal structure, stability, and properties of phases in the V-N system
43. Mechanical and tribological properties of layered materials under high pressure: assessing the importance of many-body dispersion effects10.1021/acs.jctc.9b00908 ↗
44. Mechanical properties, pozzolanic activity and volume stability of copper slag-filled cementitious materials
45. Rational design and synthesis of one-dimensional platinum-based nanostructures for oxygen-reduction electrocatalysis10.1016/S1872-2067(21)63862-7 ↗
46. Elastic constants of single-crystal Pt measured up to 20 GPa based on inelastic X-ray scattering: implication for the establishment of an equation of state10.1016/j.crte.2018.11.003 ↗
47. Integration of machine learning approaches for accelerated discovery of transition-metal dichalcogenides as Hg sensing materials10.1016/j.apenergy.2019.113651 ↗
48. Interface, compression and formation energies of bimetallic Ag/Pt(111) and Ag/Pd(111) surfaces from first-principles10.1021/acs.jpcc.4c02619 ↗
49. Exploring stereographic surface energy maps of cubic metals via an effective pair-potential approach10.1103/PhysRevB.93.035434 ↗
50. Equilibrium crystal shape of Ni from first principles10.1021/jp404569m ↗
51. Smirnov · Phys Rev B · 2021 · 103 · 4107
52. Amorphized defective fullerene with a single-atom platinum for room-temperature hydrogen storage
53. Deep potential for a face-centered cubic Cu system at finite temperatures10.1039/D2CP02758E ↗
54. Molecular dynamics simulations of the shear and tensile mechanical properties of rare-earth metal erbium based on deep-learning potential
55. Research on Cu-Sn machine learning interatomic potential with active learning strategy10.1016/j.commatsci.2024.113450 ↗
56. Anomalous self-diffusion in a single crystal of α-zirconium10.1016/0022-3115(84)90030-8 ↗
57. Bulk and interface boundary diffusion in group IV hexagonal close-packed metals and alloys10.1007/s11661-002-0143-0 ↗
58. Balogh · Physical metallurgy · 2014 · 38710.1016/B978-0-444-53770-6.00005-8 ↗
59. Intrinsic self-diffusion and substitutional Al diffusion in α-Ti10.1016/S1359-6454(97)00078-5 ↗
60. A systematic first-principles study of computational parameters affecting self-diffusion coefficients in FCC Ag, Cu, and Ni10.1007/s11669-022-00991-4 ↗
61. Konorev · Comput Mater Sci · 2021 · 192 · 11033710.1016/j.commatsci.2021.110337 ↗
62. Resistivity, Seebeck coefficient, and thermal conductivity of platinum at high pressure and temperature10.1103/PhysRevB.100.214302 ↗
63. Thermal conductivity of platinum and periclase under extreme conditions of pressure and temperature10.1080/08957959.2023.2193892 ↗
64. Menéndez-Proupin · Phys Rev B · 2007 · 76 · 411710.1103/PhysRevB.76.054117 ↗
65. Rationalizing phonon dispersion for lattice thermal conductivity of solids10.1093/nsr/nwy097 ↗