Atomic-scale simulations in multi-component alloys and compounds: A review on advances in interatomic potential

[1]  Siddarth K. Achar,et al.  Using Machine Learning Potentials to Explore Interdiffusion at Metal-Chalcogenide Interfaces. , 2022, ACS applied materials & interfaces.

[2]  Yunqing Tang,et al.  Dynamic response of high-entropy alloys to ballistic impact , 2022, Science advances.

[3]  Bo Wen,et al.  Grain boundary segregation induced strong UHTCs at elevated temperatures: a universal mechanism from conventional UHTCs to high entropy UHTCs , 2022, Journal of Materials Science & Technology.

[4]  Ying Zhang,et al.  Solving oxygen embrittlement of refractory high-entropy alloy via grain boundary engineering , 2022, Materials Today.

[5]  R. Ryltsev,et al.  Deep machine learning potentials for multicomponent metallic melts: development, predictability and compositional transferability , 2021, Journal of Molecular Liquids.

[6]  A. Shapeev,et al.  Efficient prediction of elastic properties of Ti0.5Al0.5N at elevated temperature using machine learning interatomic potential , 2021, Thin Solid Films.

[7]  Yong-Wei Zhang,et al.  Simultaneously enhancing the ultimate strength and ductility of high-entropy alloys via short-range ordering , 2021, Nature Communications.

[8]  Volker L. Deringer,et al.  Gaussian Process Regression for Materials and Molecules , 2021, Chemical reviews.

[9]  S. Ong,et al.  Atomistic simulations of dislocation mobility in refractory high-entropy alloys and the effect of chemical short-range order , 2021, Nature Communications.

[10]  Xin Chen,et al.  Machine learning enhanced empirical potentials for metals and alloys , 2021 .

[11]  J. Behler,et al.  Neural Network Potentials: A Concise Overview of Methods. , 2021, Annual review of physical chemistry.

[12]  Yanchun Zhou,et al.  Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential , 2021 .

[13]  Chun-Yan Yu,et al.  Atomistic simulation of chemical short-range order in HfNbTaZr high entropy alloy based on a newly-developed interatomic potential , 2021 .

[14]  Jinzhe Zeng,et al.  Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation , 2020, Nature Communications.

[15]  Y. Lysogorskiy,et al.  Optimized interatomic potential for study of structure and phase transitions in Si-Au and Si-Al systems , 2020 .

[16]  Christoph Ortner,et al.  Atomic permutationally invariant polynomials for fitting molecular force fields , 2020, Mach. Learn. Sci. Technol..

[17]  Yu Xie,et al.  Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery. , 2020, The journal of physical chemistry letters.

[18]  E. Ma,et al.  Unusual activated processes controlling dislocation motion in body-centered-cubic high-entropy alloys , 2020, Proceedings of the National Academy of Sciences.

[19]  Volker L. Deringer,et al.  Combining phonon accuracy with high transferability in Gaussian approximation potential models. , 2020, The Journal of chemical physics.

[20]  Yanchun Zhou,et al.  Theoretical prediction on thermal and mechanical properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by deep learning potential , 2020 .

[21]  F. Mocanu,et al.  Quench-rate and size-dependent behaviour in glassy Ge2Sb2Te5 models simulated with a machine-learned Gaussian approximation potential , 2020, Journal of Physics D: Applied Physics.

[22]  A. Thompson,et al.  Explicit Multi-element Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems. , 2020, The journal of physical chemistry. A.

[23]  J. Cairney,et al.  Observation of hydrogen trapping at dislocations, grain boundaries, and precipitates , 2020, Science.

[24]  Chi Chen,et al.  Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy , 2019, npj Computational Materials.

[25]  K. Khoo,et al.  Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys , 2019, Journal of Alloys and Compounds.

[26]  Volker L. Deringer,et al.  Machine Learning Interatomic Potentials as Emerging Tools for Materials Science , 2019, Advanced materials.

[27]  F. Mocanu,et al.  Revealing the intrinsic nature of the mid-gap defects in amorphous Ge2Sb2Te5 , 2019, Nature Communications.

[28]  Y. Ikeda,et al.  Ab initio vibrational free energies including anharmonicity for multicomponent alloys , 2019, npj Computational Materials.

[29]  Markus Meuwly,et al.  PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. , 2019, Journal of chemical theory and computation.

[30]  W. Curtin,et al.  First-principles-based prediction of yield strength in the RhIrPdPtNiCu high-entropy alloy , 2019, npj Computational Materials.

[31]  Klaus-Robert Müller,et al.  sGDML: Constructing accurate and data efficient molecular force fields using machine learning , 2018, Comput. Phys. Commun..

[32]  Technology,et al.  Moment tensor potentials as a promising tool to study diffusion processes , 2018, Computational Materials Science.

[33]  Dierk Raabe,et al.  Enhanced strength and ductility in a high-entropy alloy via ordered oxygen complexes , 2018, Nature.

[34]  Fritz Körmann,et al.  Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials , 2018, npj Computational Materials.

[35]  A. Shapeev,et al.  Improving accuracy of interatomic potentials: more physics or more data? A case study of silica , 2018, Materials Today Communications.

[36]  E Weinan,et al.  Solving many-electron Schrödinger equation using deep neural networks , 2018, J. Comput. Phys..

[37]  M. Nastasi,et al.  (Hf 0.2 Zr 0.2 Ta 0.2 Nb 0.2 Ti 0.2 )C high‐entropy ceramics with low thermal conductivity , 2018, Journal of the American Ceramic Society.

[38]  B. Liu,et al.  Twinning-governed plastic deformation in a thin film of body-centred cubic nanocrystalline ternary alloys at low temperature , 2017 .

[39]  E Weinan,et al.  DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics , 2017, Comput. Phys. Commun..

[40]  Noam Bernstein,et al.  Machine learning unifies the modeling of materials and molecules , 2017, Science Advances.

[41]  C. Tasan,et al.  Metastable high-entropy dual-phase alloys overcome the strength–ductility trade-off , 2016, Nature.

[42]  Jörg Behler,et al.  Constructing high‐dimensional neural network potentials: A tutorial review , 2015 .

[43]  Gábor Csányi,et al.  Gaussian approximation potentials: A brief tutorial introduction , 2015, 1502.01366.

[44]  Peter K. Liaw,et al.  Understanding the Cu-Zn brass alloys using a short-range-order cluster model: significance of specific compositions of industrial alloys , 2014, Scientific Reports.

[45]  Christian Trott,et al.  Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials , 2014, J. Comput. Phys..

[46]  R. Ritchie,et al.  A fracture-resistant high-entropy alloy for cryogenic applications , 2014, Science.

[47]  J Behler,et al.  Representing potential energy surfaces by high-dimensional neural network potentials , 2014, Journal of physics. Condensed matter : an Institute of Physics journal.

[48]  A. Strandlie,et al.  Detailed atomistic insight into the β″ phase in Al–Mg–Si alloys , 2014 .

[49]  A. Roberts,et al.  Grain size stabilization of nanocrystalline copper at high temperatures by alloying with tantalum , 2013 .

[50]  Peter Gumbsch,et al.  Bond order potentials for fracture, wear, and plasticity , 2012 .

[51]  A. Rajendran,et al.  An angular-dependent embedded atom method (A-EAM) interatomic potential to model thermodynamic and mechanical behavior of Al/Si composite materials , 2012 .

[52]  J. Behler Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. , 2011, Physical chemistry chemical physics : PCCP.

[53]  D. Miracle,et al.  Mechanical properties of Nb25Mo25Ta25W25 and V20Nb20Mo20Ta20W20 refractory high entropy alloys , 2011 .

[54]  J. Behler Atom-centered symmetry functions for constructing high-dimensional neural network potentials. , 2011, The Journal of chemical physics.

[55]  S. Schmauder,et al.  Atomistic simulations of solid solution strengthening of α-iron , 2011 .

[56]  Nicolas Castin,et al.  Ternary Fe–Cu–Ni many-body potential to model reactor pressure vessel steels: First validation by simulated thermal annealing , 2009 .

[57]  Y. Hiwatari,et al.  MD simulation of martensitic transformations in TiNi alloys with MEAM , 2007 .

[58]  Y. Mishin,et al.  Angular-dependent interatomic potential for tantalum , 2006 .

[59]  Michael J. Mehl,et al.  Phase stability in the Fe–Ni system: Investigation by first-principles calculations and atomistic simulations , 2005 .

[60]  Haydn N. G. Wadley,et al.  Analytic bond-order potentials for multicomponent systems , 2004 .

[61]  B. Liu,et al.  LETTER TO THE EDITOR: Lattice stability of some Ni-Ti alloy phases versus their chemical composition and disordering , 2000 .

[62]  H. W. Zandbergen,et al.  Structure Determination of Mg5Si6 Particles in Al by Dynamic Electron Diffraction Studies , 1997 .

[63]  D. Farkas,et al.  Atomistic simulations in ternary Ni - Ti - Al alloys , 1996 .

[64]  S. Srikanth,et al.  Thermodynamic properties of Cu–Ni alloys: measurements and assessment , 1989 .

[65]  M. Finnis,et al.  A simple empirical N-body potential for transition metals , 1984 .

[66]  M. Gaune-Escard,et al.  Enthalpies of Formation of Ag — Si, Au — Si and Ag — Au — Si Liquid Alloys at 1423 K , 1983 .

[67]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[68]  B. Predel,et al.  Thermodynamische untersuchung der systeme aluminium-antimon und aluminium-gold , 1970 .

[69]  Xiaolei Wu,et al.  Deformation twinning in nanocrystalline materials , 2012 .

[70]  A. Yazawa,et al.  Thermodynamic Studies of the Liquid Aluminum Alloy Systems , 1970 .

[71]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..