Development of a Neuroevolution Machine Learning Potential of Pd-Cu-Ni-P Alloys
暂无分享,去创建一个
Shucheng Wang | K. Fu | Zhuangzhuang Kong | Rui Zhao | Ping Peng | Yunlei Xu | Cuilan Wu
[1] Z. Fan,et al. Anisotropic and high thermal conductivity in monolayer quasi-hexagonal fullerene: A comparative study against bulk phase fullerene , 2022, International Journal of Heat and Mass Transfer.
[2] T. Ala‐Nissila,et al. Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations , 2022, Physical Review B.
[3] Jian Sun,et al. Pressure Stabilized Lithium-Aluminum Compounds with Both Superconducting and Superionic Behaviors. , 2022, Physical review letters.
[4] Z. Fan,et al. Atomistic insights into the mechanical anisotropy and fragility of monolayer fullerene networks using quantum mechanical calculations and machine-learning molecular dynamics simulations , 2022, Extreme Mechanics Letters.
[5]
N. Boudet,et al.
Relationship between atomic structure and excellent glass forming ability in Pd
[6] J. Qiao,et al. Intrinsic Correlation between the Fraction of Liquidlike Zones and the β Relaxation in High-Entropy Metallic Glasses. , 2022, Physical review letters.
[7] Richard A. Messerly,et al. Extending machine learning beyond interatomic potentials for predicting molecular properties , 2022, Nature Reviews Chemistry.
[8] H. Bai,et al. Liquid-like atoms in dense-packed solid glasses , 2022, Nature Materials.
[9] J. Schroers,et al. Compositional dependence of the fragility in metallic glass forming liquids , 2022, Nature Communications.
[10] T. Ala‐Nissila,et al. GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations. , 2022, The Journal of chemical physics.
[11] Weihua Wang,et al. Softening in an ultrasonic-vibrated Pd-based metallic glass , 2022, Intermetallics.
[12] J. Luan,et al. In situ study on medium-range order evolution during the polyamorphous phase transition in a Pd-Ni-P nanostructured glass , 2022, Journal of Materials Science & Technology.
[13] Steven J. Plimpton,et al. LAMMPS - A flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales , 2021, Computer Physics Communications.
[14] Z. Fan. Improving the accuracy of the neuroevolution machine learning potential for multi-component systems , 2021, Journal of physics. Condensed matter : an Institute of Physics journal.
[15] T. Rabczuk,et al. First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials , 2021, Advanced materials.
[16] T. Ala‐Nissila,et al. Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport , 2021, Physical Review B.
[17] Qinghua Zhang,et al. A medium-range structure motif linking amorphous and crystalline states , 2021, Nature Materials.
[18] J. Behler. Four Generations of High-Dimensional Neural Network Potentials. , 2021, Chemical reviews.
[19] Y. Mishin. Machine-Learning Interatomic Potentials for Materials Science , 2021, SSRN Electronic Journal.
[20] W. E,et al. Phase Diagram of a Deep Potential Water Model. , 2021, Physical review letters.
[21] Michael Gastegger,et al. Machine Learning Force Fields , 2020, Chemical reviews.
[22] Volker L. Deringer,et al. A general-purpose machine-learning force field for bulk and nanostructured phosphorus , 2020, Nature Communications.
[23] Hong Wu,et al. Microstructure evolution and deformation mechanism of amorphous/crystalline high-entropy-alloy composites , 2020 .
[24] K. Yin,et al. Generality of abnormal viscosity drop on cooling of CuZr alloy melts and its structural origin , 2020, Acta Materialia.
[25] Chi Chen,et al. Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy , 2019, npj Computational Materials.
[26] J. Behler,et al. A Performance and Cost Assessment of Machine Learning Interatomic Potentials. , 2019, The journal of physical chemistry. A.
[27] N. Boudet,et al. Partial structure investigation of the traditional bulk metallic glass Pd40Ni40P20 , 2019, Physical Review B.
[28] Jian Luo,et al. Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals , 2018, Physical Review B.
[29] B. Liu,et al. Transformation induced softening and plasticity in high entropy alloys , 2018 .
[30] E Weinan,et al. Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics , 2017, Physical review letters.
[31] W. Z. Zhou,et al. Direct imaging of a first-order liquid-liquid phase transition in undercooled molten PdNiP alloys and its thermodynamic implications , 2017 .
[32] Haipeng Wang,et al. Thermophysical Properties and Atomic Distribution of Undercooled Liquid Cu , 2017 .
[33] Chang-yu Zhou,et al. Orientation and strain rate dependent tensile behavior of single crystal titanium nanowires by molecular dynamics simulations , 2017 .
[34] Volker L. Deringer,et al. Machine learning based interatomic potential for amorphous carbon , 2016, 1611.03277.
[35] Wei Chen,et al. Efficient molecular dynamics simulations with many-body potentials on graphics processing units , 2016, Comput. Phys. Commun..
[36] K. Dong,et al. A comparative study on local atomic configurations characterized by cluster-type-index method and Voronoi polyhedron method , 2016 .
[37] A. Inoue,et al. The world's biggest glassy alloy ever made , 2012 .
[38] S. Lan,et al. A metastable liquid state miscibility gap in undercooled Pd–Ni–P melts , 2012 .
[39] T. Wada,et al. Microstructure and Electrochemical Behavior of PdCuNiP Bulk Metallic Glass and Its Crystallized Alloys , 2012 .
[40] A. Hirata,et al. Structural origins of the excellent glass forming ability of Pd40Ni40P20. , 2012, Physical review letters.
[41] Tom Schaul,et al. Natural Evolution Strategies , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).
[42] F. Faupel,et al. Diffusion in bulk-metallic glass-forming Pd–Cu–Ni–P alloys: From the glass to the equilibrium melt , 2007 .
[43] O. Haruyama. Thermodynamic approach to free volume kinetics during isothermal relaxation in bulk Pd–Cu–Ni–P20 glasses , 2007 .
[44] Michele Parrinello,et al. Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.
[45] J. Bai,et al. Atomic packing and short-to-medium-range order in metallic glasses , 2006, Nature.
[46] F. Faupel,et al. Diffusion and isotope effect in bulk-metallic glass-forming Pd–Cu–Ni–P alloys from the glass to the equilibrium melt , 2003 .
[47] M. Baskes,et al. Semiempirical atomic potentials for the fcc metals Cu, Ag, Au, Ni, Pd, Pt, Al, and Pb based on first and second nearest-neighbor modified embedded atom method , 2003 .
[48] A. Inoue,et al. Deformation and Fracture Behaviors of Pd-Cu-Ni-P Glassy Alloys , 2002 .
[49] A. Meyer. Atomic transport in dense multicomponent metallic liquids , 2002, cond-mat/0206364.
[50] A. Inoue,et al. Abrupt change in heat capacity of supercooled Pd–Cu–Ni–P melt during continuous cooling , 2001 .
[51] A. Inoue,et al. Undercooled liquid-to-glass transition during continuous cooling in Pd–Cu–Ni–P alloys , 2000 .
[52] Nobuyuki Nishiyama,et al. Structural Study of Pd-Based Amorphous Alloys with Wide Supercooled Liquid Region by Anomalous X-ray Scattering , 1999 .
[53] A. Inoue,et al. Preparation and thermal stability of bulk amorphous Pd40Cu30Ni10P20 alloy cylinder of 72 mm in diameter , 1997 .
[54] Burke,et al. Generalized Gradient Approximation Made Simple. , 1996, Physical review letters.
[55] Kresse,et al. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. , 1996, Physical review. B, Condensed matter.
[56] Blöchl,et al. Projector augmented-wave method. , 1994, Physical review. B, Condensed matter.
[57] Hafner,et al. Ab initio molecular dynamics for liquid metals. , 1995, Physical review. B, Condensed matter.
[58] M. Baskes,et al. Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals , 1984 .
[59] J. L. Finney,et al. Random packings and the structure of simple liquids. I. The geometry of random close packing , 1970, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.
[60] F. Birch. Finite Elastic Strain of Cubic Crystals , 1947 .
[61] Georges Voronoi. Nouvelles applications des paramètres continus à la théorie des formes quadratiques. Deuxième mémoire. Recherches sur les parallélloèdres primitifs. , 1908 .