Fetching the paper…
Reading the bibliography…
We develop a neuroevolution-potential (NEP) framework for generating neural network based machine-learning potentials.
V.I. Fedorov and V.I. Machuev, “Thermal Conductivity of PbTe, SnTe and GeTe in the solid and liquid phases,” Sov. Phys. Solid State USSR 11
1969
Earlier work this paper cites.
A. A. El-Sharkawy, A. M. Abou El-Azm, M. I. Kenawy, A. S. Hillal, and H. M. Abu-Basha, “Thermophysical properties of polycrystalline PbS, PbSe, and PbTe in the temperature range 300–700 K,” International Journal of Thermophysics 4
1983
Earlier work this paper cites.
Frank H. Stillinger and Thomas A. Weber, “Computer simulation of local order in condensed phases of silicon,” Phys. Rev. B 31
1985
Earlier work this paper cites.
J. Tersoff, “Modeling solid-state chemistry: Interatomic potentials for multicomponent systems,” Phys. Rev. B 39
1989
Earlier work this paper cites.
P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50
1994
Earlier work this paper cites.
Steve Plimpton, “Fast Parallel Algorithms for Short-Range Molecular Dynamics,” Journal of Computational Physics 117
1995
Earlier work this paper cites.
G. Kresse and J. Furthmüller, “Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,” Phys. Rev. B 54
1996
Earlier work this paper cites.
John P. Perdew, Kieron Burke, and Matthias Ernzerhof, “Generalized Gradient Approximation Made Simple,” Phys. Rev. Lett. 77
1996
Earlier work this paper cites.
Xin Yao, “Evolving artificial neural networks,” Proceedings of the IEEE 87
1999
Earlier work this paper cites.
G. Kresse and D. Joubert, “From ultrasoft pseudopotentials to the projector augmented-wave method,” Phys. Rev. B 59
1999
Earlier work this paper cites.
Jörg Behler and Michele Parrinello, “Generalized neural-network representation of high-dimensional potential-energy surfaces,” Phys. Rev. Lett. 98
2007
Earlier work this paper cites.
Paolo Giannozzi, Stefano Baroni, Nicola Bonini, Matteo Calandra, Roberto Car, Carlo Cavazzoni, Davide Ceresoli, Guido L Chiarotti, Matteo Cococcioni, Ismaila Dabo, Andrea Dal Corso, Stefano de Gironcoli, Stefano Fabris, Guido Fratesi, Ralph Gebauer, Uwe Gerstmann, Christos Gougoussis, Anton Kokalj, Michele Lazzeri, Layla Martin-Samos, Nicola Marzari, Francesco Mauri, Riccardo Mazzarello, Stefano Paolini, Alfredo Pasquarello, Lorenzo Paulatto, Carlo Sbraccia, Sandro Scandolo, Gabriele Sclauzero, Ari P Seitsonen, Alexander Smogunov, Paolo Umari, and Renata M Wentzcovitch, “QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials,” Journal of Physics: Condensed Matter 21
2009
Earlier work this paper cites.
Albert P. Bartók, Mike C. Payne, Risi Kondor, and Gábor Csányi, “Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons,” Phys. Rev. Lett. 104
2010
Earlier work this paper cites.
Tobias Glasmachers, Tom Schaul, Sun Yi, Daan Wierstra, and Jürgen Schmidhuber, “Exponential natural evolution strategies,” in Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation , GECCO ’10 (Association for Computing Machinery, New York, NY, USA, 2010) p. 393–400
2010
Earlier work this paper cites.
Tom Schaul, Tobias Glasmachers, and Jürgen Schmidhuber, “High dimensions and heavy tails for natural evolution strategies,” in Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation , GECCO ’11 (Association for Computing Machinery, New York, NY, USA, 2011) pp. 845–852
2011
Earlier work this paper cites.
Jorg Behler, “Atom-centered symmetry functions for constructing high-dimensional neural network potentials,” The Journal of Chemical Physics 134
2011
Earlier work this paper cites.
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O. Anatole von Lilienfeld, “Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning,” Phys. Rev. Lett. 108
2012
Earlier work this paper cites.
Gabriele C. Sosso, Davide Donadio, Sebastiano Caravati, Jörg Behler, and Marco Bernasconi, “Thermal transport in phase-change materials from atomistic simulations,” Phys. Rev. B 86
2012
Earlier work this paper cites.
Zheyong Fan, Topi Siro, and Ari Harju, “Accelerated molecular dynamics force evaluation on graphics processing units for thermal conductivity calculations,” Computer Physics Communications 184
2013
Earlier work this paper cites.
Albert P. Bartók, Risi Kondor, and Gábor Csányi, “On representing chemical environments,” Phys. Rev. B 87
2013
Earlier work this paper cites.
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, and Jürgen Schmidhuber, “Natural evolution strategies,” Journal of Machine Learning Research 15
2014
Earlier work this paper cites.
Xiaoliang Zhang, Han Xie, Ming Hu, Hua Bao, Shengying Yue, Guangzhao Qin, and Gang Su, “Thermal conductivity of silicene calculated using an optimized Stillinger-Weber potential,” Phys. Rev. B 89
2014
Earlier work this paper cites.
A.P. Thompson, L.P. Swiler, C.R. Trott, S.M. Foiles, and G.J. Tucker, “Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials,” Journal of Computational Physics 285
2015
Earlier work this paper cites.
Zheyong Fan, Luiz Felipe C. Pereira, Hui-Qiong Wang, Jin-Cheng Zheng, Davide Donadio, and Ari Harju, “Force and heat current formulas for many-body potentials in molecular dynamics simulations with applications to thermal conductivity calculations,” Phys. Rev. B 92
2015
Earlier work this paper cites.
Maxime Gill-Comeau and Laurent J. Lewis, “Heat conductivity in graphene and related materials: A time-domain modal analysis,” Phys. Rev. B 92
2015
Earlier work this paper cites.
Martin Schlipf and François Gygi, “Optimization algorithm for the generation of ONCV pseudopotentials,” Computer Physics Communications 196
2015
Earlier work this paper cites.
Davide Campi, Davide Donadio, Gabriele C. Sosso, Jörg Behler, and Marco Bernasconi, “Electron-phonon interaction and thermal boundary resistance at the crystal-amorphous interface of the phase change compound GeTe,” Journal of Applied Physics 117
2015
Earlier work this paper cites.
Xiaokun Gu and Ronggui Yang, “First-principles prediction of phononic thermal conductivity of silicene: A comparison with graphene,” Journal of Applied Physics 117
2015
Earlier work this paper cites.
Jörg Behler, “Perspective: Machine learning potentials for atomistic simulations,” The Journal of Chemical Physics 145
2016
Earlier work this paper cites.
Alexander V. Shapeev, “Moment tensor potentials: A class of systematically improvable interatomic potentials,” Multiscale Modeling & Simulation 14
2016
Cited alongside, same era.
Alireza Khorshidi and Andrew A. Peterson, “Amp: A modular approach to machine learning in atomistic simulations,” Computer Physics Communications 207
2016
Cited alongside, same era.
Y. D. Kuang, L. Lindsay, S. Q. Shi, and G. P. Zheng, “Tensile strains give rise to strong size effects for thermal conductivities of silicene, germanene and stanene,” Nanoscale 8
2016
Cited alongside, same era.
Han Xie, Tao Ouyang, Éric Germaneau, Guangzhao Qin, Ming Hu, and Hua Bao, “Large tunability of lattice thermal conductivity of monolayer silicene via mechanical strain,” Phys. Rev. B 93
2016
Cited alongside, same era.
Bo Peng, Hao Zhang, Hezhu Shao, Yuanfeng Xu, Gang Ni, Rongjun Zhang, and Heyuan Zhu, “Phonon transport properties of two-dimensional group-IV materials from ab initio calculations,” Phys. Rev. B 94
Pavel Korotaev, Ivan Novoselov, Aleksey Yanilkin, and Alexander Shapeev, “Accessing thermal conductivity of complex compounds by machine learning interatomic potentials,” Phys. Rev. B 100
2019
Later among the works it cites.
Xiaokun Gu and C.Y. Zhao, “Thermal conductivity of single-layer MoS 2(1-x)
2019
Later among the works it cites.
Emi Minamitani, Masayoshi Ogura, and Satoshi Watanabe, “Simulating lattice thermal conductivity in semiconducting materials using high-dimensional neural network potential,” Applied Physics Express 12
2019
Later among the works it cites.
Hasan Babaei, Ruiqiang Guo, Amirreza Hashemi, and Sangyeop Lee, “Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies,” Phys. Rev. Materials 3
2019
Later among the works it cites.
Tim Mueller, Alberto Hernandez, and Chuhong Wang, “Machine learning for interatomic potential models,” The Journal of Chemical Physics 152
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
Wei Lv and Asegun Henry, “Direct calculation of modal contributions to thermal conductivity via Green-Kubo modal analysis,” New Journal of Physics 18
2016
Cited alongside, same era.
K. Sääskilahti, J. Oksanen, J. Tulkki, A. J. H. McGaughey, and S. Volz, “Vibrational mean free paths and thermal conductivity of amorphous silicon from non-equilibrium molecular dynamics simulations,” AIP Advances 6
2016
Cited alongside, same era.
Nongnuch Artrith, Alexander Urban, and Gerbrand Ceder, “Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species,” Phys. Rev. B 96
2017
Cited alongside, same era.
Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E, “DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics,” Computer Physics Communications 228
2018
Cited alongside, same era.
Ke Xu, Zheyong Fan, Jicheng Zhang, Ning Wei, and Tapio Ala-Nissila, “Thermal transport properties of single-layer black phosphorus from extensive molecular dynamics simulations,” Modelling and Simulation in Materials Science and Engineering 26
2018
Cited alongside, same era.
M. Gastegger, L. Schwiedrzik, M. Bittermann, F. Berzsenyi, and P. Marquetand, “wACSF–Weighted atom-centered symmetry functions as descriptors in machine learning potentials,” The Journal of Chemical Physics 148
2018
Cited alongside, same era.
Albert P. Bartók, James Kermode, Noam Bernstein, and Gábor Csányi, “Machine learning a general-purpose interatomic potential for silicon,” Phys. Rev. X 8
2018
Cited alongside, same era.
2020
Later among the works it cites.
Zheyong Fan and Alex Gabourie, “brucefan1983/gpumd: Gpumd-v2.5.1,” (2020)
2020
Later among the works it cites.
V. Zaverkin and J. Kästner, “Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials,” Journal of Chemical Theory and Computation 16
2020
Later among the works it cites.
C. van der Oord, G. Dusson, G. Csányi, and C. Ortner, “Regularised atomic body-ordered permutation-invariant polynomials for the construction of interatomic potentials,” Machine Learning: Science and Technology 1
2020
Later among the works it cites.
Lauri Himanen, Marc O.J. Jäger, Eiaki V. Morooka, Filippo Federici Canova, Yashasvi S. Ranawat, David Z. Gao, Patrick Rinke, and Adam S. Foster, “Dscribe: Library of descriptors for machine learning in materials science,” Computer Physics Communications 247
2020
Later among the works it cites.
Volker L. Deringer, Miguel A. Caro, and Gábor Csányi, “A general-purpose machine-learning force field for bulk and nanostructured phosphorus,” Nature Communications 11
2020
Later among the works it cites.
Patrick Rowe, Volker L. Deringer, Piero Gasparotto, Gábor Csányi, and Angelos Michaelides, “An accurate and transferable machine learning potential for carbon,” The Journal of Chemical Physics 153
2020
Later among the works it cites.
Claudia Mangold, Shunda Chen, Giuseppe Barbalinardo, Jörg Behler, Pascal Pochet, Konstantinos Termentzidis, Yang Han, Laurent Chaput, David Lacroix, and Davide Donadio, “Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of Mn x
2020
Later among the works it cites.
Bohayra Mortazavi, Evgeny V Podryabinkin, Ivan S Novikov, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, and Alexander V Shapeev, “Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials,” Journal of Physics: Materials 3
2020
Later among the works it cites.
Kohei Shimamura, Yusuke Takeshita, Shogo Fukushima, Akihide Koura, and Fuyuki Shimojo, “Computational and training requirements for interatomic potential based on artificial neural network for estimating low thermal conductivity of silver chalcogenides,” The Journal of Chemical Physics 153
2020
Later among the works it cites.
Alejandro Rodriguez, Yinqiao Liu, and Ming Hu, “Spatial density neural network force fields with first-principles level accuracy and application to thermal transport,” Phys. Rev. B 102
2020
Later among the works it cites.
Janine George, Geoffroy Hautier, Albert P. Bartók, Gábor Csányi, and Volker L. Deringer, “Combining phonon accuracy with high transferability in Gaussian approximation potential models,” The Journal of Chemical Physics 153
2020
Later among the works it cites.
Yuan-Bin Liu, Jia-Yue Yang, Gong-Ming Xin, Lin-Hua Liu, Gábor Csányi, and Bing-Yang Cao, “Machine learning interatomic potential developed for molecular simulations on thermal properties of β \beta -Ga 2 O 3 ,” The Journal of Chemical Physics 153
2020
Later among the works it cites.
Y. Mishin, “Machine-learning interatomic potentials for materials science,” Acta Materialia 214
2021
Closest in time.
Ivan S Novikov, Konstantin Gubaev, Evgeny V Podryabinkin, and Alexander V Shapeev, “The MLIP package: moment tensor potentials with MPI and active learning,” Machine Learning: Science and Technology 2
2021
Closest in time.
Alexander J. Gabourie, Zheyong Fan, Tapio Ala-Nissila, and Eric Pop, “Spectral decomposition of thermal conductivity: Comparing velocity decomposition methods in homogeneous molecular dynamics simulations,” Phys. Rev. B 103
2021
Closest in time.
Howard Yanxon, David Zagaceta, Binh Tang, David S Matteson, and Qiang Zhu, “PyXtal_FF: a python library for automated force field generation,” Machine Learning: Science and Technology 2
2021
Closest in time.
Heikki Muhli, Xi Chen, Albert P. Bartók, Patricia Hernández-León, Gábor Csányi, Tapio Ala-Nissila, and Miguel A. Caro, “Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C 60 {\mathrm{C}}_{60} ,” Phys. Rev. B 104
2021
Closest in time.
Zheyong Fan, “Inputs and outputs of nep in gpumd,” (2021)
2021
Closest in time.
Zezhu Zeng, Cunzhi Zhang, Yi Xia, Zheyong Fan, Chris Wolverton, and Yue Chen, “Nonperturbative phonon scatterings and the two-channel thermal transport in Tl 3 VSe 4 {\mathrm{Tl}}_{3}{\mathrm{VSe}}_{4} ,” Phys. Rev. B 103
2021
Closest in time.
2021
Closest in time.
Kohei Shimamura, Yusuke Takeshita, Shogo Fukushima, Akihide Koura, and Fuyuki Shimojo, “Estimating thermal conductivity of α \alpha -Ag 2 Se using ANN potential with Chebyshev descriptor,” Chemical Physics Letters 778
2021
Closest in time.
Huan Liu, Xin Qian, Hua Bao, Changying Zhao, and Xiaokun Gu, “High-temperature phonon transport properties of SnSe from machine-learning interatomic potential,” Journal of Physics: Condensed Matter (2021)
2021
Closest in time.
Bohayra Mortazavi, Evgeny V. Podryabinkin, Ivan S. Novikov, Timon Rabczuk, Xiaoying Zhuang, and Alexander V. Shapeev, “Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution,” Computer Physics Communications 258
2021
Closest in time.