Accelerated Discovery of Thermoelectric Materials Using Machine Learning

[1]  David J. Singh,et al.  BoltzTraP. A code for calculating band-structure dependent quantities , 2006, Comput. Phys. Commun..

[2]  K. Schwarz,et al.  WIEN2k: An Augmented Plane Wave Plus Local Orbitals Program for Calculating Crystal Properties , 2019 .

[3]  Anuj Kumar Goyal,et al.  Capturing Anharmonicity in a Lattice Thermal Conductivity Model for High-Throughput Predictions , 2017 .

[4]  P. Rinke,et al.  Data‐Driven Materials Science: Status, Challenges, and Perspectives , 2019, Advanced science.

[5]  Volker Tresp,et al.  Mixtures of Gaussian Processes , 2000, NIPS.

[6]  Robert B. Gramacy,et al.  Ja n 20 08 Bayesian Treed Gaussian Process Models with an Application to Computer Modeling , 2009 .

[7]  G. Kresse,et al.  From ultrasoft pseudopotentials to the projector augmented-wave method , 1999 .

[8]  Atsuto Seko,et al.  Representation of compounds for machine-learning prediction of physical properties , 2016, 1611.08645.

[9]  J. Ziman.  Principles of the Theory of Solids by J. M. Ziman , 1972 .

[10]  A. Singh,et al.  Unraveling the role of bonding chemistry in connecting electronic and thermal transport by machine learning , 2020, Journal of Materials Chemistry A.

[11]  Blöchl,et al.  Projector augmented-wave method. , 1994, Physical review. B, Condensed matter.

[12]  K. Esfarjani,et al.  Resonant bonding leads to low lattice thermal conductivity , 2014, Nature Communications.

[13]  Carl E. Rasmussen,et al.  A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..

[14]  M. Kanatzidis,et al.  High-performance bulk thermoelectrics with all-scale hierarchical architectures , 2012, Nature.

[15]  Terry M. Tritt,et al.  Thermoelectric Materials, Phenomena, and Applications: A Bird’s Eye View , 2006 .

[16]  G. Kresse,et al.  Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set , 1996 .

[17]  W. Kohn,et al.  Self-Consistent Equations Including Exchange and Correlation Effects , 1965 .

[18]  Richard LeSar,et al.  Materials informatics: An emerging technology for materials development , 2009, Stat. Anal. Data Min..

[19]  Vladan Stevanović,et al.  TE Design Lab: A virtual laboratory for thermoelectric material design , 2016 .

[20]  G. Hautier,et al.  Local Bonding Influence on the Band Edge and Band Gap Formation in Quaternary Chalcopyrites , 2017, Advanced science.

[21]  Chiho Kim,et al.  A polymer dataset for accelerated property prediction and design , 2016, Scientific Data.

[22]  Swanti Satsangi,et al.  Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene , 2018, Chemistry of Materials.

[23]  Taylor D. Sparks,et al.  Data-Driven Review of Thermoelectric Materials: Performance and Resource Considerations , 2013 .

[24]  I. Jolliffe.  Principal Components in Regression Analysis , 1986 .

[25]  Adil Rasheed,et al.  Discovering Thermoelectric Materials Using Machine Learning: Insights and Challenges , 2018, ICANN.

[26]  J. Vybíral,et al.  Big data of materials science: critical role of the descriptor. , 2014, Physical review letters.

[27]  F. Disalvo,et al.  Thermoelectric cooling and power generation , 1999, Science.

[28]  Stefano Curtarolo,et al.  Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling , 2014, 1401.2439.

[29]  Marco Buongiorno Nardelli,et al.  Combining the AFLOW GIBBS and elastic libraries to efficiently and robustly screen thermomechanical properties of solids , 2016, 1611.05714.

[30]  Laurent Chaput,et al.  Direct solution to the linearized phonon Boltzmann equation. , 2013, Physical review letters.

[31]  Atsuto Seko,et al.  Sparse representation for a potential energy surface , 2014, 1403.7995.

[32]  Chiwoo Park,et al.  Patchwork Kriging for Large-scale Gaussian Process Regression , 2017, J. Mach. Learn. Res..

[33]  Kamal Choudhary,et al.  High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory , 2017, Scientific Reports.

[34]  G. Rohrer.  Structure and Bonding in Crystalline Materials , 2001 .

[35]  Kristin A. Persson,et al.  Commentary: The Materials Project: A materials genome approach to accelerating materials innovation , 2013 .

[36]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[37]  Arunkumar Chitteth Rajan,et al.  Accelerated Data-Driven Accurate Positioning of the Band Edges of MXenes. , 2019, Journal of Physical Chemistry Letters.

[38]  Volker L. Deringer,et al.  Crystal orbital Hamilton population (COHP) analysis as projected from plane-wave basis sets. , 2011, The journal of physical chemistry. A.

[39]  D. Spitzer.  Lattice thermal conductivity of semiconductors: A chemical bond approach , 1969 .

[40]  Volker Tresp,et al.  A Bayesian Committee Machine , 2000, Neural Computation.

[41]  Madhubanti Mukherjee,et al.  High Thermoelectric Figure of Merit via Tunable Valley Convergence Coupled Low Thermal Conductivity in AIIBIVC2V Chalcopyrites , 2018, The Journal of Physical Chemistry C.

[42]  B. Ganguli,et al.  Effect of p–d hybridization, structural distortion and cation electronegativity on electronic properties of ZnSnX2 (X=P, As, Sb) chalcopyrite semiconductors , 2013 .

[43]  Chiho Kim,et al.  From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown , 2016 .

[44]  A. Singh,et al.  Guided patchwork kriging to develop highly transferable thermal conductivity prediction models , 2020, Journal of Physics: Materials.

[45]  Burke,et al.  Generalized Gradient Approximation Made Simple. , 1996, Physical review letters.

[46]  Stefano Curtarolo,et al.  Assessing the Thermoelectric Properties of Sintered Compounds via High-Throughput Ab-Initio Calculations , 2011 .

[47]  Chris Dames,et al.  Correspondence: Reply to ‘The experimental requirements for a photon thermal diode’ , 2017, Nature Communications.

[48]  Wu Li,et al.  ShengBTE: A solver of the Boltzmann transport equation for phonons , 2014, Comput. Phys. Commun..

[49]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[50]  L. Pauling.  The Nature Of The Chemical Bond , 1939 .

[51]  Ashutosh Kumar Singh,et al.  Rattling-Induced Ultra-low Thermal Conductivity Leading to Exceptional Thermoelectric Performance in AgIn5S8. , 2019, ACS applied materials & interfaces.

[52]  Tim Mueller,et al.  Machine Learning in Materials Science , 2016 .

[53]  Ashutosh Kumar Singh,et al.  Thermal Conductivity Enhancement in MoS_{2} under Extreme Strain. , 2019, Physical review letters.

[54]  Claudia Draxl,et al.  NOMAD: The FAIR concept for big data-driven materials science , 2018, MRS Bulletin.

[55]  R. Hoffmann.  How Chemistry and Physics Meet in the Solid State , 1987 .

[56]  Hong Kuan Ng,et al.  Inertial effective mass as an effective descriptor for thermoelectrics via data-driven evaluation , 2019, Journal of Materials Chemistry A.

[57]  CsatóLehel,et al.  Sparse on-line Gaussian processes , 2002 .

[58]  P. A. Cox.  The Electronic Structure And Chemistry Of Solids , 1987 .

[59]  Vladan Stevanović,et al.  Material descriptors for predicting thermoelectric performance , 2015 .

[60]  Hichem Snoussi,et al.  Machine Learning Approaches for Thermoelectric Materials Research , 2019, Advanced Functional Materials.

[61]  Lehel Csató,et al.  Sparse On-Line Gaussian Processes , 2002, Neural Computation.

[62]  George S. Nolas,et al.  Effect of partial void filling on the lattice thermal conductivity of skutterudites , 1998 .

[63]  Kamalika Das,et al.  Block-GP: Scalable Gaussian Process Regression for Multimodal Data , 2010, 2010 IEEE International Conference on Data Mining.

[64]  Muratahan Aykol,et al.  The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies , 2015 .

[65]  Cheng Soon Ong,et al.  Multivariate spearman's ρ for aggregating ranks using copulas , 2016 .

[66]  Sanguthevar Rajasekaran,et al.  Accelerating materials property predictions using machine learning , 2013, Scientific Reports.

[67]  Marco Buongiorno Nardelli,et al.  High-throughput computational screening of thermal conductivity, Debye temperature, and Grüneisen parameter using a quasiharmonic Debye model , 2014, 1407.7789.

[68]  Taylor D. Sparks,et al.  Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties , 2016 .

[69]  G. Rohrer.  Structure and Bonding in Crystalline Materials: Index , 2001 .

[70]  A. Bhardwaj,et al.  In situ click chemistry generation of cyclooxygenase-2 inhibitors , 2017, Nature Communications.

[71]  L. Hedin.  NEW METHOD FOR CALCULATING THE ONE-PARTICLE GREEN'S FUNCTION WITH APPLICATION TO THE ELECTRON-GAS PROBLEM , 1965 .

[72]  Atsuto Seko,et al.  Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids , 2013, 1310.1546.

[73]  LeSarRichard.  Materials informatics: An emerging technology for materials development , 2009 .

[74]  Kim Lefmann,et al.  Avoided crossing of rattler modes in thermoelectric materials. , 2008, Nature materials.

[75]  Akanksha K. Menon,et al.  New horizons in thermoelectric materials: Correlated electrons, organic transport, machine learning, and more , 2019, Journal of Applied Physics.

[76]  Zhiqun Lin,et al.  Thermodynamic Routes to Ultralow Thermal Conductivity and High Thermoelectric Performance , 2020, Advanced materials.

[77]  G. Mahan,et al.  The best thermoelectric. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[78]  Zoubin Ghahramani,et al.  Local and global sparse Gaussian process approximations , 2007, AISTATS.

[79]  Yuma Iwasaki,et al.  Machine-learning guided discovery of a new thermoelectric material , 2019, Scientific Reports.

[80]  Marco Buongiorno Nardelli,et al.  AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations , 2012 .

[81]  Atsuto Seko,et al.  Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization. , 2015, Physical review letters.

[82]  C Wood,et al.  Materials for thermoelectric energy conversion , 1988 .

[83]  K. Yoodee,et al.  Effects of p-d hybridization on the valence band of I-III-VI 2 chalcopyrite semiconductors , 1984 .

[84]  L. Bell.  Cooling, Heating, Generating Power, and Recovering Waste Heat with Thermoelectric Systems , 2008, Science.

[85]  Ian T. Jolliffe,et al.  Principal Component Analysis , 1986, Springer Series in Statistics.

[86]  Marco Buongiorno Nardelli,et al.  The high-throughput highway to computational materials design. , 2013, Nature materials.

[87]  G. Madsen,et al.  Automated search for new thermoelectric materials: the case of LiZnSb. , 2006, Journal of the American Chemical Society.

[88]  A. Singh,et al.  Coupling the High-Throughput Property Map to Machine Learning for Predicting Lattice Thermal Conductivity , 2019, Chemistry of Materials.

[89]  James Theiler,et al.  Accelerated search for materials with targeted properties by adaptive design , 2016, Nature Communications.

[90]  Carl E. Rasmussen,et al.  Infinite Mixtures of Gaussian Process Experts , 2001, NIPS.

[91]  M. Zebarjadi,et al.  Thermoelectric power factor: Enhancement mechanisms and strategies for higher performance thermoelectric materials , 2015 .

[92]  Bryce Meredig,et al.  A recommendation engine for suggesting unexpected thermoelectric chemistries , 2015, 1502.07635.

[93]  David J. Singh,et al.  Electronic fitness function for screening semiconductors as thermoelectric materials , 2017, 1708.04499.

[94]  G. Pilania,et al.  Machine learning bandgaps of double perovskites , 2016, Scientific Reports.

[95]  A. Singh,et al.  A Statistical Approach for the Rapid Prediction of Electron Relaxation Time Using Elemental Representatives , 2020 .

[96]  Ying Zhang,et al.  A strategy to apply machine learning to small datasets in materials science , 2018, npj Computational Materials.

[97]  Anubhav Jain,et al.  Understanding thermoelectric properties from high-throughput calculations: trends, insights, and comparisons with experiment , 2016 .

[98]  G. J. Snyder,et al.  Vacancy-induced dislocations within grains for high-performance PbSe thermoelectrics , 2017, Nature Communications.

[99]  Richard Dronskowski,et al.  Crystal orbital Hamilton populations (COHP): energy-resolved visualization of chemical bonding in solids based on density-functional calculations , 1993 .

[100]  M. Schlüter,et al.  Density-Functional Theory of the Energy Gap , 1983 .

[101]  J. Bardeen,et al.  Deformation Potentials and Mobilities in Non-Polar Crystals , 1950 .

[102]  Isao Tanaka,et al.  Distributions of phonon lifetimes in Brillouin zones , 2015, 1501.00691.

[103]  I. Tanaka,et al.  First principles phonon calculations in materials science , 2015, 1506.08498.

[104]  G. J. Snyder,et al.  Complex thermoelectric materials. , 2008, Nature materials.

[105]  Chiwoo Park,et al.  Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes , 2016, J. Mach. Learn. Res..

[106]  Stefano de Gironcoli,et al.  Phonons and related crystal properties from density-functional perturbation theory , 2000, cond-mat/0012092.

[107]  Kresse,et al.  Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. , 1996, Physical review. B, Condensed matter.

[108]  Stefano Curtarolo,et al.  SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates , 2017, Physical Review Materials.

[109]  Zoubin Ghahramani,et al.  Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.

[110]  S. Curtarolo,et al.  Nanograined Half‐Heusler Semiconductors as Advanced Thermoelectrics: An Ab Initio High‐Throughput Statistical Study , 2014, 1408.5859.

[111]  Geoffroy Hautier,et al.  Thinking Like a Chemist: Intuition in Thermoelectric Materials. , 2016, Angewandte Chemie.

[112]  T. Pandey,et al.  High Thermoelectric Performance in n-Doped Silicon-Based Chalcogenide Si2Te3 , 2017 .

[113]  Richard Alan Lesar.  Materials informatics: An emerging technology for materials development , 2009 .

[114]  R. Feynman.  Forces in Molecules , 1939 .