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The phonon density-of-states (DOS) summarizes the lattice vibrational modes supported by a structure, and gives access to rich information about the material's stability, thermodynamic constants, and thermal transport coefficients.
Isotope scattering of dispersive phonons in Ge
Shin-ichiro Tamura · 1983
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Isotope Scattering of large wave-vector phonons in GaS and InSb: Deformation dipole and overlap-shell models
Shin-ichiro Tamura · 1984
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Prediction of heat capacities of solid inorganic salts from group contributions
A. T. M. Golam Mostafa, James M. Eakman, Mark M. Montoya, and Stephen L. Yarbro · 1996
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Effect of Phonon Dispersion on Transport Properties of Single-Crystalline Dielectrics. Electronic and Photonic Packaging, Electrical Systems Design and Photonics, and Nanotechnology
Mehdi Asheghi, Wenjun Liu, and K. E. Goodson · 2004
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Applications of local crystal structure measures in experiment and simulation
G. J. Ackland and A. P. Jones · 2006
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Vibrational thermodynamics of materials
Brent Fultz · 2010
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Application of neumann–kopp rule for the estimation of heat capacity of mixed oxides
J. Leitner, P. Voňka, D. Sedmidubský, and P. Svoboda · 2010
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Phonon engineering through crystal chemistry
Eric S Toberer, Alex Zevalkink, and G Jeffrey Snyder · 2011
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Dispersion considerations affecting phonon- mass impurity scattering rates
Patrick E Hopkins · 2011
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First principles phonon calculations in materials science
Atsushi Togo and Isao Tanaka · 2015
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First principles phonon calculations in materials science
A Togo and I Tanaka · 2015
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Prediction errors of molecular machine learning models lower than hybrid dft error
Felix A. Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S. Schoenholz, George E. Dahl, Oriol Vinyals, Steven Kearnes, Patrick F. Riley, and O. Anatole von Lilienfeld · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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How chemical composition alone can predict vibrational free energies and entropies of solids
Fleur Legrain, Jesús Carrete, Ambroise van Roekeghem, Stefano Curtarolo, and Natalio Mingo · 2017
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High-throughput density-functional perturbation theory phonons for inorganic materials
Guido Petretto, Shyam Dwaraknath, Henrique P.C. Miranda, Donald Winston, Matteo Giantomassi, Michiel J. Van Setten, Xavier Gonze, Kristin A. Persson, Geoffroy Hautier, and Gian Marco Rignanese · 2018
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Using the callaway model to deduce relevant phonon scattering processes: The importance of phonon dispersion
Matthias Schrade and Terje G. Finstad · 2018
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Rationalizing phonon dispersion for lattice thermal conductivity of solids
Zhiwei Chen, Xinyue Zhang, Siqi Lin, Lidong Chen, and Yanzhong Pei · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C. Grossman · 2018
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Schnet – a deep learning architecture for molecules and materials
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller · 2018
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Vibrational entropy stabilizes distorted half-heusler structures
Shuping Guo, Shashwat Anand, Yongsheng Zhang, and G. Jeffrey Snyder · 2020
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Predicting vibrational entropy of fcc solids uniquely from bond chemistry using machine learning
Anus Manzoor and Dilpuneet S. Aidhy · 2020
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Analytical models of phonon–point-defect scattering
Ramya Gurunathan, Riley Hanus, Maxwell Dylla, Ankita Katre, and G. Jeffrey Snyder · 2020
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Magnetically driven phonon instability enables the metal–insulator transition in h-fes
Dipanshu Bansal, Jennifer L. Niedziela, Stuart Calder, Tyson Lanigan-Atkins, Ryan Rawl, Ayman H. Said, Douglas L. Abernathy, Alexander I. Kolesnikov, Haidong Zhou, and Olivier Delaire · 2020
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Thermal transport in defective and disordered materials
Riley Hanus, Ramya Gurunathan, Lucas Lindsay, Matthias T. Agne, Jingjing Shi, Samuel Graham, and G. Jeffrey Snyder · 2021
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C. Grossman · 2018
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Heat capacity of Mg3Sb2, Mg3Bi2, and their alloys at high temperature
Matthias T. Agne, Kazuki Imasato, Shashwat Anand, Kathleen Lee, Sabah K. Bux, Alex Zevalkink, Alexander J.E. Rettie, Duck Young Chung, Mercouri G. Kanatzidis, and G. Jeffrey Snyder · 2018
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First-principles study of vibrational entropy effects on the pbte-srte phase diagram
Xia Hua, Shiqiang Hao, and Chris Wolverton · 2018
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Machine learning prediction of heat capacity for solid inorganics
Steven K. Kauwe, Jake Graser, Antonio Vazquez, and Taylor D. Sparks · 2018
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almaBTE: A solver of the space-time dependent Boltzmann transport equation for phonons in structured materials
Jesús Carrete, Bjorn Vermeersch, Ankita Katre, Ambroise van Roekeghem, Tao Wang, Georg K.H. Madsen, and Natalio Mingo · 2019
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Graph networks as a universal machine learning framework for molecules and crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong · 2019
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Convergence and machine learning predictions of monkhorst-pack k-points and plane-wave cut-off in high-throughput dft calculations
Kamal Choudhary and Francesca Tavazza · 2019
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Atomistic line graph neural network for improved materials property predictions
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Benchmarking graph neural networks for materials chemistry
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Materials representation and transfer learning for multi-property prediction
Shufeng Kong, Dan Guevarra, Carla P. Gomes, and John M. Gregoire · 2021
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Direct prediction of phonon density of states with euclidean neural networks
Zhantao Chen, Nina Andrejevic, Tess Smidt, Zhiwei Ding, Qian Xu, Yen-Ting Chi, Quynh T. Nguyen, Ahmet Alatas, Jing Kong, and Mingda Li · 2021
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New phase transitions driven by soft phonon modes for cspbbr3: Density functional theory study
Raouia Ben Sadok, Dalila Hammoutène, and Neculai Plugaru · 2021
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Accelerating materials-space exploration by mapping materials properties via artificial intelligence: The case of the lattice thermal conductivity, 2022
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Recent advances and applications of deep learning methods in materials science
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Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings
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Prediction of the electron density of states for crystalline compounds with atomistic line graph neural networks (alignn)
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