Skip to main content
SpringerLink
Log in

Accelerated Discovery of Thermoelectric Materials Using Machine Learning

  • Chapter
  • First Online:
Artificial Intelligence for Materials Science

Part of the book series: Springer Series in Materials Science ((SSMATERIALS,volume 312))

Abstract

Optimized electronic and thermal transport properties are the key requirements for the discovery of efficient thermoelectric materials. Owing to the complex interdependence, simultaneous optimization of these properties is a non-trivial and challenging task, especially if one wants to explore the large available search space of materials. With the advent of statistical high-throughput and machine learning based approaches, several of these challenges for thermoelectrics have been addressed. The goal of this chapter is to highlight these data-assisted efforts towards accelerated development of high-performance thermoelectric materials. We will discuss the contribution of curated databases for high-throughput screening of desired electronic and thermal transport properties. The utilization of these databases will also be described for development of prediction models of transport properties, which has accelerated the discovery of highly efficient thermoelectric materials. Details of commonly used strategies and methods to select a relevant descriptor set for developing the prediction models will be covered. A new approach for selecting descriptors by analyzing the high-throughput property map will be explained. The potential of machine learning methods in relating the unrelated properties will be illustrated by establishing a connection between otherwise independent electronic and thermal transport properties. Further, for designing the highly transferable models for a single target property of interest, we will also cover localized regression based algorithmic development.

The authors declare no competing financial interests.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wood, C. (1988). Materials for thermoelectric energy conversion. Reports on Progress in Physics, 51, 459.

    Article  CAS  Google Scholar 

  2. Mahan, G., & Sofo, J. (1996). The best thermoelectric. Proceedings of the National Academy of Sciences of the United States of America, 93, 7436–7439.

    Google Scholar 

  3. Mahan, G. (1997). Good thermoelectrics. Solid State Physics, 51, 81–157.

    Article  Google Scholar 

  4. DiSalvo, F. J. (1999). Thermoelectric cooling and power generation. Science, 285, 703–706.

    Article  CAS  Google Scholar 

  5. Tritt, T. M., & Subramanian, M. (2006). Thermoelectric materials, phenomena, and applications: A bird’s eye view. MRS Bulletin, 31, 188–198.

    Article  Google Scholar 

  6. Bell, L. E. (2008). Cooling, heating, generating power, and recovering waste heat with thermoelectric systems. Science, 321, 1457–1461.

    Article  CAS  Google Scholar 

  7. Snyder, G. J., & Toberer, E. S. (2008). Complex thermoelectric materials. Nature Materials, 7, 105–114.

    Article  CAS  Google Scholar 

  8. Dehkordi, A. M., Zebarjadi, M., He, J., & Tritt, T. M. (2015). Thermoelectric power factor: Enhancement mechanisms and strategies for higher performance thermoelectric materials. Materials Science and Engineering R: Reports, 97, 1–22.

    Article  Google Scholar 

  9. Juneja, R., Pandey, T., & Singh, A. K. (2017). High thermoelectric performance in n-doped siliconbased chalcogenide Si2Te3. Chemistry of Materials, 29, 3723–3730.

    Article  CAS  Google Scholar 

  10. Xing, G., Sun, J., Li, Y., Fan, X., Zheng, W., & Singh, D. J. (2017). Electronic fitness function for screening semiconductors as thermoelectric materials. Physical Review Materials, 1, 065405.

    Article  Google Scholar 

  11. Mukherjee, M., Yumnam, G., & Singh, A. K. (2018). High thermoelectric figure of merit via tunable valley convergence coupled low thermal conductivity in AIIBIV C2V chalcopyrites. The Journal of Physical Chemistry C, 122, 29150–29157.

    Article  CAS  Google Scholar 

  12. Christensen, M., Abrahamsen, A. B., Christensen, N. B., Juranyi, F., Andersen, N. H., Lefmann, K., Andreasson, J., Bahl, C. R., & Iversen, B. B. (2008). Avoided crossing of rattler modes in thermoelectric materials. Nature Materials, 7, 811–815.

    Article  CAS  Google Scholar 

  13. Nolas, G., Cohn, J., & Slack, G. (1998). Effect of partial void filling on the lattice thermal conductivity of skutterudites. Physical Review B, 58, 164.

    Article  CAS  Google Scholar 

  14. Juneja, R., & Singh, A. K. (2019). Rattling-induced ultralow thermal conductivity leading to exceptional thermoelectric performance in AgIn5S8. ACS Applied Materials & Interfaces, 11, 33894–33900.

    Article  CAS  Google Scholar 

  15. Meng, et al. (2019). Thermal conductivity enhancement in MoS2 under extreme strain. Physical Review Letters, 122, 155901.

    Google Scholar 

  16. Lee, S., Esfarjani, K., Luo, T., Zhou, J., Tian, Z., & Chen, G. (2014). Resonant bonding leads to low lattice thermal conductivity. Nature Communications, 5, 3525.

    Article  Google Scholar 

  17. Chen, Z., Ge, B., Li, W., Lin, S., Shen, J., Chang, Y., Hanus, R., Snyder, G. J., & Pei, Y. (2017). Vacancy-induced dislocations within grains for high-performance PbSe thermoelectrics. Nature Communications, 8, 1–8.

    CAS  Google Scholar 

  18. Biswas, K., He, J., Blum, I. D., Wu, C.-I., Hogan, T. P., Seidman, D. N., Dravid, V. P., & Kanatzidis, M. G. (2012). High-performance bulk thermoelectrics with all-scale hierarchical architectures. Nature, 489, 414–418.

    Article  CAS  Google Scholar 

  19. Wei, et al. (2020). Thermodynamic routes to ultralow thermal conductivity and high thermoelectric performance. Advanced Materials, 32, 1906457.

    Google Scholar 

  20. LeSar, R. (2009). Materials informatics: An emerging technology for materials development. Statistical Analysis and Data Mining, 1, 372–374.

    Article  Google Scholar 

  21. Curtarolo, S., Hart, G. L., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013). The high-throughput highway to computational materials design. Nature Materials, 12, 191.

    Article  CAS  Google Scholar 

  22. Mueller, T., Kusne, A. G., & Ramprasad, R. (2016). Machine learning in materials science: Recent progress and emerging applications. Reviews in Computational Chemistry, 29, 186–273.

    CAS  Google Scholar 

  23. Murphy, K. P. (2012). Machine learning: A probabilistic perspective. Cambridge, MA: Massachusetts Institute of Technology Press.

    Google Scholar 

  24. Pilania, G., Wang, C., Jiang, X., Rajasekaran, S., & Ramprasad, R. (2013). Accelerating materials property predictions using machine learning. Scientific Reports, 3, 2810.

    Article  Google Scholar 

  25. Seko, A., Maekawa, T., Tsuda, K., & Tanaka, I. (2014). Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids. Physical Review B, 89, 054303.

    Article  CAS  Google Scholar 

  26. Seko, A., Takahashi, A., & Tanaka, I. (2014). Sparse representation for a potential energy surface. Physical Review B, 90, 024101.

    Article  CAS  Google Scholar 

  27. Xue, D., Balachandran, P. V., Hogden, J., Theiler, J., Xue, D., & Lookman, T. (2016). Accelerated search for materials with targeted properties by adaptive design. Nature Communications, 7, 11241.

    Article  CAS  Google Scholar 

  28. Kim, C., Pilania, G., & Ramprasad, R. (2016). From organized high-throughput data to phenomenological theory using machine learning: The example of dielectric breakdown. Chemistry of Materials, 28, 1304–1311.

    Article  CAS  Google Scholar 

  29. Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B., Ramprasad, R., Gubernatis, J., & Lookman, T. (2016). Machine learning bandgaps of double perovskites. Scientific Reports, 6, 19375.

    Article  CAS  Google Scholar 

  30. Rajan, A. C., Mishra, A., Satsangi, S., Vaish, R., Mizuseki, H., Lee, K.-R., & Singh, A. K. (2018). Machine-learning-assisted accurate band gap predictions of functionalized MXene. Chemistry of Materials, 30, 4031–4038.

    Article  CAS  Google Scholar 

  31. Mishra, A., Satsangi, S., Rajan, A. C., Mizuseki, H., Lee, K.-R., & Singh, A. K. (2019). Accelerated data-driven accurate positioning of the band edges of MXenes. The Journal of Physical Chemistry Letters, 10, 780–785.

    Article  CAS  Google Scholar 

  32. Gaultois, M. W., Oliynyk, A. O., Mar, A., Sparks, T. D., Mulholland, G. J., & Meredig, B. (2016). Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties. APL Materials, 4, 053213.

    Article  CAS  Google Scholar 

  33. Gorai, P., Gao, D., Ortiz, B., Miller, S., Barnett, S. A., Mason, T., Lv, Q., Stevanović, V., & Toberer, E. S. (2016). TE design lab: A virtual laboratory for thermoelectric material design. Computational Materials Science, 112, 368–376.

    Article  Google Scholar 

  34. Toher, C., Plata, J. J., Levy, O., De Jong, M., Asta, M., Nardelli, M. B., & Curtarolo, S. (2014). High-throughput computational screening of thermal conductivity, debye temperature, and Grüneisen parameter using a quasiharmonic debye model. Physical Review B, 90, 174107.

    Article  CAS  Google Scholar 

  35. Toher, et al. (2017). Combining the AFLOW GIBBS and elastic libraries to efficiently and robustly screen thermomechanical properties of solids. Physical Review Materials, 1, 015401.

    Google Scholar 

  36. Urban, J. J., Menon, A. K., Tian, Z., Jain, A., & Hippalgaonkar, K. (2019). New horizons in thermo-24 electric materials: Correlated electrons, organic transport, machine learning, and more. Journal of Applied Physics, 125, 180902.

    Article  CAS  Google Scholar 

  37. Wang, T., Zhang, C., Snoussi, H., & Zhang, G. (2020). Machine learning approaches for thermoelectric materials research. Advanced Functional Materials, 30, 1906041.

    Article  CAS  Google Scholar 

  38. Madsen, G. K. (2006). Automated search for new thermoelectric materials: The case of LiZnSb. Journal of the American Chemical Society, 128, 12140–12146.

    Article  CAS  Google Scholar 

  39. Wang, S., Wang, Z., Setyawan, W., Mingo, N., & Curtarolo, S. (2011). Assessing the thermoelectric properties of sintered compounds via high-throughput ab-initio calculations. Physical Review X, 1, 021012.

    Article  CAS  Google Scholar 

  40. Gaultois, M. W., Sparks, T. D., Borg, C. K., Seshadri, R., Bonificio, W. D., & Clarke, D. R. (2013). Data-driven review of thermoelectric materials: Performance and resource considerations. Chemistry of Materials, 25, 2911–2920.

    Article  CAS  Google Scholar 

  41. Carrete, J., Mingo, N., Wang, S., & Curtarolo, S. (2014). Nanograined half-heusler semiconductors as advanced thermoelectrics: An ab initio high-throughput statistical study. Advanced Functional Materials, 24, 7427–7432.

    Article  CAS  Google Scholar 

  42. Carrete, J., Li, W., Mingo, N., Wang, S., & Curtarolo, S. (2014). Finding unprecedentedly low-thermal-conductivity half-heusler semiconductors via high-throughput materials modeling. Physical Review X, 4, 011019.

    Article  CAS  Google Scholar 

  43. Chen, et al. (2016). Understanding thermoelectric properties from high-throughput calculations: Trends, insights, and comparisons with experiment. Journal of Materials Chemistry C, 4, 4414–4426.

    Google Scholar 

  44. Tabib, M. V., Løvvik, O. M., Johannessen, K., Rasheed, A., Sagvolden, E., & Rustad, A. M. (2018). Discovering thermoelectric materials using machine learning: Insights and challenges. In International Conference on Artificial Neural Networks (pp. 392–401).

    Google Scholar 

  45. Iwasaki, et al. (2019). Machine-learning guided discovery of a new thermoelectric material. Scientific Reports, 9, 2751.

    Google Scholar 

  46. Suwardi, A., Bash, D., Ng, H. K., Gomez, J. R., Repaka, D. M., Kumar, P., & Hippalgaonkar, K. (2019). Inertial effective mass as an effective descriptor for thermoelectrics via datadriven evaluation. Journal of Materials Chemistry A, 7, 23762–23769.

    Article  CAS  Google Scholar 

  47. Juneja, R., Yumnam, G., Satsangi, S., & Singh, A. K. (2019). Coupling high-throughput property map to machine learning for predicting lattice thermal conductivity. Chemistry of Materials, 31, 5145–5151.

    Article  CAS  Google Scholar 

  48. Juneja, R., & Singh, A. K. (2020). Unraveling the role of bonding chemistry in connecting electronic and thermal transport by machine learning. Journal of Materials Chemistry A, 8, 8716–8721.

    Article  CAS  Google Scholar 

  49. Mukherjee, M., Satsangi, S., & Singh, A. K. (2020). A statistical approach for the rapid prediction of electron relaxation time using elemental representatives. Chemistry of Materials, 32, 6507–6514.

    Article  CAS  Google Scholar 

  50. Seko, A., Togo, A., Hayashi, H., Tsuda, K., Chaput, L., & Tanaka, I. (2015). Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization. Physical Review Letters, 115, 205901.

    Article  CAS  Google Scholar 

  51. Seko, A., Hayashi, H., Nakayama, K., Takahashi, A., & Tanaka, I. (2017). Representation of compounds for machine-learning prediction of physical properties. Physical Review B, 95, 144110.

    Article  Google Scholar 

  52. Juneja, R., & Singh, A. K. (2020). Guided patchwork kriging to develop highly transferable thermal conductivity prediction models. Journal of Physics: Materials, 3, 024006.

    CAS  Google Scholar 

  53. Kohn, W., & Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133.

    Article  Google Scholar 

  54. Sham, L., & Schlüter, M. (1983). Density-functional theory of the energy gap. Physical Review Letters, 51, 1888.

    Article  Google Scholar 

  55. Kresse, G., & Furthmüller, J. (1996). Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Physical Review B, 54, 11169.

    Article  CAS  Google Scholar 

  56. Kresse, G., & Furthmüller, J. (1996). Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Computational Materials Science, 6, 15–50.

    Article  CAS  Google Scholar 

  57. Perdew, J. P., Burke, K., & Ernzerhof, M. (1996). Generalized gradient approximation made simple. Physical Review Letters, 77, 3865.

    Article  CAS  Google Scholar 

  58. Blöchl, P. E. (1994). Projector augmented-wave method. Physical Review B, 50, 17953.

    Article  Google Scholar 

  59. Kresse, G., & Joubert, D. (1999). From ultrasoft pseudopotentials to the projector augmented-wave method. Physical Review B, 59, 1758.

    Article  CAS  Google Scholar 

  60. Hedin, L. (1965). New method for calculating the one-particle green’s function with application to the electron-gas problem. Physical Review, 139, A796.

    Article  Google Scholar 

  61. Blaha, P., Schwarz, K., Madsen, G. K., Kvasnicka, D., Luitz, J., Laskowsji, R., Tran, F., & Marks, L. (2001). An augmented plane wave plus local orbitals program for calculating crystal properties, Techn. Universitat Wien, Austria.

    Google Scholar 

  62. Ziman, J. M. (1972). Principles of the theory of solids. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  63. Madsen, G. K., & Singh, D. J. (2006). BoltzTraP. A code for calculating band-structure dependent quantities. Computer Physics Communications, 175, 67–71.

    Article  CAS  Google Scholar 

  64. Bardeen, J., & Shockley, W. (1950). Deformation potentials and mobilities in non-polar crystals. Physical Review, 80, 72.

    Article  CAS  Google Scholar 

  65. Feynman, R. P. (1939). Forces in molecules. Physical Review, 56, 340.

    Article  CAS  Google Scholar 

  66. Baroni, S., De Gironcoli, S., Dal Corso, A., & Giannozzi, P. (2001). Phonons and related crystal properties from density-functional perturbation theory. Reviews of Modern Physics, 73, 515.

    Article  CAS  Google Scholar 

  67. Togo, A., & Tanaka, I. (2015). First principles phonon calculations in materials science. Scripta Materialia, 108, 1–5.

    Article  CAS  Google Scholar 

  68. Li, W., Carrete, J., Katcho, N. A., & Mingo, N. (2014). ShengBTE: A solver of the Boltzmann transport equation for phonons. Computer Physics Communications, 185, 1747–1758.

    Article  CAS  Google Scholar 

  69. Chaput, L., Togo, A., Tanaka, I., & Hug, G. (2013). Direct solution to the linearized phonon Boltzmann equation. Physical Review Letters, 110, 265506.

    Article  CAS  Google Scholar 

  70. Togo, A., Chaput, L., & Tanaka, I. (2015). Distributions of phonon lifetimes in Brillouin zones. Physical Review B, 91, 094306.

    Article  CAS  Google Scholar 

  71. Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Springer series in statistics, Vol. 1). New York: Springer.

    Google Scholar 

  72. Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019). Data-driven materials science: Status, challenges, and perspectives. Advanced Science, 6, 1900808.

    Article  Google Scholar 

  73. Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A. (2013). The materials project: A materials genome approach to accelerating materials innovation. APL Materials, 1, 011002.

    Article  CAS  Google Scholar 

  74. Kirklin, S., Saal, J. E., Meredig, B., Thompson, A., Doak, J. W., Aykol, M., Rühl, S., & Wolverton, C. (2015). The open quantum materials database (OQMD): Assessing the accuracy of DFT formation energies. Npj Computational Materials, 1, 15010.

    Article  CAS  Google Scholar 

  75. Curtarolo, et al. (2012). AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio Calculations. Computational Materials Science, 58, 227–235.

    Google Scholar 

  76. Draxl, C., & Scheffler, M. (2018). NOMAD: The FAIR concept for big data-driven materials science. MRS Bulletin, 43, 676–682.

    Article  Google Scholar 

  77. Huan, T. D., Mannodi-Kanakkithodi, A., Kim, C., Sharma, V., Pilania, G., & Ramprasad, R. (2016). A polymer dataset for accelerated property prediction and design. Scientific Data, 3, 160012.

    Article  CAS  Google Scholar 

  78. Choudhary, K., Kalish, I., Beams, R., & Tavazza, F. (2017). High-throughput identification and characterization of two-dimensional materials using density functional theory. Scientific Reports, 7, 5179.

    Article  CAS  Google Scholar 

  79. Ghiringhelli, L. M., Vybiral, J., Levchenko, S. V., Draxl, C., & Scheffler, M. (2015). Big data of materials science: Critical role of the descriptor. Physical Review Letters, 114, 105503.

    Article  CAS  Google Scholar 

  80. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58, 267–288.

    Google Scholar 

  81. Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M., & Ghiringhelli, L. M. (2018). SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Physical Review Materials, 2, 083802.

    Article  CAS  Google Scholar 

  82. Jolliffe, I. T. (1986). Principal component analysis (pp. 129–155). New York: Springer.

    Google Scholar 

  83. Miller, et al. (2017). Capturing anharmonicity in a lattice thermal conductivity model for high-throughput predictions. Chemistry of Materials, 29, 2494–2501.

    Google Scholar 

  84. Yan, J., Gorai, P., Ortiz, B., Miller, S., Barnett, S. A., Mason, T., Stevanovic, V., & Toberer, E. S. (2015). Material descriptors for predicting thermoelectric performance. Energy & Environmental Science, 8, 983–994.

    Article  Google Scholar 

  85. Hoffmann, R. (1987). How chemistry and physics meet in the solid state. Angewandte Chemie International, 26, 846–878.

    Article  Google Scholar 

  86. Rohrer, G. S. (2001). Structure and bonding in crystalline materials. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  87. Cox, P. A. (1987). The electronic structure and chemistry of solids (Vol. 231). Oxford: Oxford University Press.

    Google Scholar 

  88. Pauling, L. (1960). The nature of the chemical bond (Vol. 260). Ithaca: Cornell University Press.

    Google Scholar 

  89. Suchet, J. (1977). Electronegativity, ionicity, and effective atomic charges. Journal of the Electrochemical Society, 124, 30C–35C.

    Article  CAS  Google Scholar 

  90. Spitzer, D. (1970). Lattice thermal conductivity of semiconductors: A chemical bond approach. Journal of Physics and Chemistry of Solids, 31, 19–40.

    Article  CAS  Google Scholar 

  91. Mishra, S., & Ganguli, B. (2013). Effect of p-d hybridization, structural distortion and cation electronegativity on electronic properties of ZnSnX2 (X = P, As, Sb) chalcopyrite semiconductors. Journal of Solid State Chemistry, 200, 279–286.

    Article  CAS  Google Scholar 

  92. Yoodee, K., Woolley, J. C., & Sa-Yakanit, V. (1984). Effects of p-d hybridization on the valence band of I-III-VI2 chalcopyrite semiconductors. Physical Review B, 30, 5904.

    Article  CAS  Google Scholar 

  93. Miglio, A., Heinrich, C. P., Tremel, W., Hautier, G., & Zeier, W. G. (2017). Local bonding influence on the band edge and band gap formation in quaternary chalcopyrites. Advanced Science, 4, 1700080.

    Article  CAS  Google Scholar 

  94. Juneja, R., Shinde, R., & Singh, A. K. (2018). Pressure-induced topological phase transitions in CdGeSb2 and CdSnSb2. The Journal of Physical Chemistry Letters, 9, 2202–2207.

    Article  CAS  Google Scholar 

  95. Zeier, W. G., Zevalkink, A., Gibbs, Z. M., Hautier, G., Kanatzidis, M. G., & Snyder, G. J. (2016). Thinking like a chemist: Intuition in thermoelectric materials. Angewandte Chemie, 55, 6826–6841.

    Article  CAS  Google Scholar 

  96. Dronskowski, R., & Blöchl, P. E. (1993). Crystal orbital Hamilton populations (COHP): Energy-resolved visualization of chemical bonding in solids based on density-functional calculations. The Journal of Physical Chemistry, 97, 8617–8624.

    Article  CAS  Google Scholar 

  97. Deringer, V. L., Tchougréeff, A. L., & Dronskowski, R. (2011). Crystal orbital Hamilton population (COHP) analysis as projected from plane-wave basis sets. The Journal of Physical Chemistry A, 115, 5461–5466.

    Article  CAS  Google Scholar 

  98. Csató, L., & Opper, M. (2002). Sparse online Gaussian processes. Neural Computation, 14, 641–668.

    Article  Google Scholar 

  99. Quiñonero-Candela, J., & Rasmussen, C. E. (2005). A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research, 6, 1939–1959.

    Google Scholar 

  100. Snelson, E., & Ghahramani, Z. (2006). Sparse Gaussian processes using pseudo-inputs. In Advances in neural information processing systems (pp. 1257–1264). Cambridge, MA: MIT Press.

    Google Scholar 

  101. Tresp, V. (2001). Mixtures of Gaussian processes. In Advances in neural information processing systems (pp. 654–660). Cambridge, MA: MIT Press.

    Google Scholar 

  102. Rasmussen, C. E., & Ghahramani, Z. (2002). Infinite mixtures of Gaussian process experts. In Advances in neural information processing systems (pp. 881–888). Cambridge, MA: MIT Press.

    Google Scholar 

  103. Snelson, E., & Ghahramani, Z. (2007). Local and global sparse Gaussian process approximations. In Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (Vol. 2, pp. 524–531). San Juan, Puerto Rico: PMLR. http://proceedings.mlr.press/v2/snelson07a.html.

  104. Gramacy, R. B., & Lee, H. K. H. (2008). Bayesian treed Gaussian process models with an application to computer modeling. Journal of the American Statistical Association, 103, 1119–1130.

    Article  CAS  Google Scholar 

  105. Tresp, V. (2000). A Bayesian committee machine. Neural Computation, 12, 2719–2741.

    Article  CAS  Google Scholar 

  106. Das, K., & Srivastava, A. N. (2010). Block-GP: Scalable Gaussian process regression for multimodal data. In 2010 IEEE International Conference on Data Mining (pp. 791–796).

    Google Scholar 

  107. Park, C., & Huang, J. Z. (2016). Efficient computation of Gaussian process regression for large spatial data sets by patching local Gaussian processes. Journal of Machine Learning Research, 17, 1–29.

    CAS  Google Scholar 

  108. Park, C., & Apley, D. (2018). Patchwork kriging for large-scale Gaussian process regression. Journal of Machine Learning Research, 19, 269–311.

    Google Scholar 

  109. Zhang, Y., & Ling, C. (2018). A strategy to apply machine learning to small datasets in materials science. Npj Computational Materials, 4, 25.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank the Materials Research Centre, Thematic Unit of Excellence, and Supercomputer Education and Research Centre, Indian Institute of Science, for providing computing facilities. The authors acknowledge the support from Institute of Eminence (IoE) MHRD grant.

Author information

Authors and Affiliations

  1. Materials Research Centre, Indian Institute of Science, Bangalore, India

    Rinkle Juneja & Abhishek K. Singh

Authors
  1. Rinkle Juneja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhishek K. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhishek K. Singh .

Editor information

Editors and Affiliations

  1. Monash Suzhou Research Institute, Suzhou, China

    Yuan Cheng

  2. Institute of Artificial Intelligence, Beihang University, Beijing, China

    Tian Wang

  3. Institute of High Performance Computing, Singapore, Singapore

    Gang Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Juneja, R., Singh, A.K. (2021). Accelerated Discovery of Thermoelectric Materials Using Machine Learning. In: Cheng, Y., Wang, T., Zhang, G. (eds) Artificial Intelligence for Materials Science. Springer Series in Materials Science, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-68310-8_6

Download citation

Publish with us

Policies and ethics

Search

Navigation