Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies

Abstract

In the post-Moore’s law era, the progress of electronics relies on discovering superior semiconductor materials and optimizing device fabrication. Computational methods, augmented by emerging data-driven strategies, offer a promising alternative to the traditional trial-and-error approach. In this Perspective, we highlight data-driven computational frameworks for enhancing semiconductor discovery and device development by elaborating on their advances in exploring the materials design space, predicting semiconductor properties and optimizing device fabrication, with a concluding discussion on the challenges and opportunities in these areas.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematics showing semiconductor material design and electronic device optimization.
Fig. 2: Summary of data-driven strategies for material design space exploration.
Fig. 3: ML-assisted computational framework for semiconductor functionalities and electronic devices.
Fig. 4: Two fundamental research paradigms in data-driven material growth experimental studies.

Similar content being viewed by others

References

  1. Waldrop, M. M. The chips are down for Moore’s law. Nature 530, 144–147 (2016).

    Article  Google Scholar 

  2. Powell, J. R. The quantum limit to Moore’s law. Proc. IEEE 96, 1247–1248 (2008).

    Article  Google Scholar 

  3. Vorwerk, C., Sheng, N., Govoni, M., Huang, B. & Galli, G. Quantum embedding theories to simulate condensed systems on quantum computers. Nat. Comput. Sci. 2, 424–432 (2022).

    Article  Google Scholar 

  4. Schuman, C. D. et al. Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2, 10–19 (2022).

    Article  Google Scholar 

  5. Akinwande, D. et al. Graphene and two-dimensional materials for silicon technology. Nature 573, 507–518 (2019).

    Article  Google Scholar 

  6. Li, W. et al. Approaching the quantum limit in two-dimensional semiconductor contacts. Nature 613, 274–279 (2023).

    Article  Google Scholar 

  7. Zhang, S. et al. Minimizing buried interfacial defects for efficient inverted perovskite solar cells. Science 380, 404–409 (2023).

    Article  Google Scholar 

  8. Luo, J. et al. Efficient and stable emission of warm-white light from lead-free halide double perovskites. Nature 563, 541–545 (2018).

    Article  Google Scholar 

  9. Yang, H. et al. Two-dimensional materials prospects for non-volatile spintronic memories. Nature 606, 663–673 (2022).

    Article  Google Scholar 

  10. Schram, T., Sutar, S., Radu, I. & Asselberghs, I. Challenges of wafer-scale integration of 2D semiconductors for high-performance transistor circuits. Adv. Mater. 34, 2109796 (2022).

    Article  Google Scholar 

  11. Wang, R. et al. A review of perovskites solar cell stability. Adv. Funct. Mater. 29, 1808843 (2019).

    Article  Google Scholar 

  12. Sabatier, P. C. Past and future of inverse problems. J. Math. Phys. 41, 4082–4124 (2000).

    Article  MathSciNet  Google Scholar 

  13. Zunger, A. Inverse design in search of materials with target functionalities. Nat. Rev. Chem. 2, 0121 (2018).

    Article  Google Scholar 

  14. Peng, J. et al. Human- and machine-centred designs of molecules and materials for sustainability and decarbonization. Nat. Rev. Mater. 7, 991–1009 (2022).

    Article  Google Scholar 

  15. Yang, D. et al. Functionality-directed screening of Pb-free hybrid organic–inorganic perovskites with desired intrinsic photovoltaic functionalities. Chem. Mater. 29, 524–538 (2017).

    Article  Google Scholar 

  16. Zhao, X.-G. et al. Design of lead-free inorganic halide perovskites for solar cells via cation-transmutation. J. Am. Chem. Soc. 139, 2630–2638 (2017).

    Article  Google Scholar 

  17. Liu, Z. et al. Computational functionality-driven design of semiconductors for optoelectronic applications. InfoMat 2, 879–904 (2020).

    Article  Google Scholar 

  18. Liao, T. W. & Li, G. Metaheuristic-based inverse design of materials—a survey. J. Materiomics 6, 414–430 (2020).

    Article  Google Scholar 

  19. Peng, H. et al. Li-doped Cr2MnO4: a new p-type transparent conducting oxide by computational materials design. Adv. Funct. Mater. 23, 5267–5276 (2013).

    Article  Google Scholar 

  20. Schmidt, J. et al. Machine-learning-assisted determination of the global zero-temperature phase diagram of materials. Adv. Mater. 35, 2210788 (2023).

    Article  Google Scholar 

  21. Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023).

    Article  Google Scholar 

  22. Kulik, H. J. et al. Roadmap on machine learning in electronic structure. Electron. Struct. 4, 023004 (2022).

    Article  Google Scholar 

  23. Yan, D., Smith, A. D. & Chen, C.-C. Structure prediction and materials design with generative neural networks. Nat. Comput. Sci. 3, 572–574 (2023).

    Article  Google Scholar 

  24. Anstine, D. M. & Isayev, O. Generative models as an emerging paradigm in the chemical sciences. J. Am. Chem. Soc. 145, 8736–8750 (2023).

    Article  Google Scholar 

  25. Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023).

    Article  Google Scholar 

  26. Szymanski, N. J. et al. Toward autonomous design and synthesis of novel inorganic materials. Mater. Horiz. 8, 2169–2198 (2021).

    Article  Google Scholar 

  27. Luo, S., Li, T., Wang, X., Faizan, M. & Zhang, L. High-throughput computational materials screening and discovery of optoelectronic semiconductors. Wiley Interdiscip. Rev. Comput. Mol. Sci. 11, e1489 (2021).

    Article  Google Scholar 

  28. Curtarolo, S. et al. The high-throughput highway to computational materials design. Nat. Mater. 12, 191–201 (2013).

    Article  Google Scholar 

  29. Han, D. et al. Design of high-performance lead-free quaternary antiperovskites for photovoltaics via ion type inversion and anion ordering. J. Am. Chem. Soc. 143, 12369–12379 (2021).

    Article  Google Scholar 

  30. Li, Y. et al. Design of organic–inorganic hybrid heterostructured semiconductors via high-throughput materials screening for optoelectronic applications. J. Am. Chem. Soc. 144, 16656–16666 (2022).

    Article  Google Scholar 

  31. He, X., Singh, D. J., Boon-on, P., Lee, M.-W. & Zhang, L. Dielectric behavior as a screen in rational searches for electronic materials: metal pnictide sulfosalts. J. Am. Chem. Soc. 140, 18058–18065 (2018).

    Article  Google Scholar 

  32. Zhao, X.-G. et al. Cu–In halide perovskite solar absorbers. J. Am. Chem. Soc. 139, 6718–6725 (2017).

    Article  Google Scholar 

  33. Zhao, X.-G. et al. JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics. Sci. Bull. 66, 1973–1985 (2021).

    Article  Google Scholar 

  34. Mounet, N. et al. Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nat. Nanotechnol. 13, 246–252 (2018).

    Article  Google Scholar 

  35. Pizzi, G., Cepellotti, A., Sabatini, R., Marzari, N. & Kozinsky, B. AiiDA: automated interactive infrastructure and database for computational science. Comput. Mater. Sci. 111, 218–230 (2016).

    Article  Google Scholar 

  36. Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15, 1120–1127 (2016).

    Article  Google Scholar 

  37. Merchant, A. et al. Scaling deep learning for materials discovery. Nature 624, 80–85 (2023).

    Article  Google Scholar 

  38. Oganov, A. R., Pickard, C. J., Zhu, Q. & Needs, R. J. Structure prediction drives materials discovery. Nat. Rev. Mater. 4, 331–348 (2019).

    Article  Google Scholar 

  39. Zhang, L., Luo, J.-W., Saraiva, A., Koiller, B. & Zunger, A. Genetic design of enhanced valley splitting towards a spin qubit in silicon. Nat. Commun. 4, 2396 (2013).

    Article  Google Scholar 

  40. Zhang, Y.-Y., Gao, W., Chen, S., Xiang, H. & Gong, X.-G. Inverse design of materials by multi-objective differential evolution. Comput. Mater. Sci. 98, 51–55 (2015).

    Article  Google Scholar 

  41. Allahyari, Z. & Oganov, A. R. in Handbook of Materials Modeling (eds Andreoni, W. & Yip, S.) 2777–2790 (Springer, 2020).

  42. Li, C.-N., Liang, H.-P., Zhang, X., Lin, Z. & Wei, S.-H. Graph deep learning accelerated efficient crystal structure search and feature extraction. npj Comput. Mater. 9, 176 (2023).

    Article  Google Scholar 

  43. Cheng, G., Gong, X.-G. & Yin, W.-J. Crystal structure prediction by combining graph network and optimization algorithm. Nat. Commun. 13, 1492 (2022).

    Article  Google Scholar 

  44. Kruglov, I. A. et al. Crystal structure prediction at finite temperatures. npj Comput. Mater. 9, 197 (2023).

    Article  Google Scholar 

  45. Kang, S. et al. Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials. npj Comput. Mater. 8, 108 (2022).

    Article  Google Scholar 

  46. Wang, X. et al. Data-driven prediction of complex crystal structures of dense lithium. Nat. Commun. 14, 2924 (2023).

    Article  Google Scholar 

  47. Hong, C. et al. Training machine-learning potentials for crystal structure prediction using disordered structures. Phys. Rev. B 102, 224104 (2020).

    Article  Google Scholar 

  48. Hwang, S. et al. Stability and equilibrium structures of unknown ternary metal oxides explored by machine-learned potentials. J. Am. Chem. Soc. 145, 19378–19386 (2023).

    Article  Google Scholar 

  49. Wei, L. et al. TCSP: a template-based crystal structure prediction algorithm for materials discovery. Inorg. Chem. 61, 8431–8439 (2022).

    Article  Google Scholar 

  50. Kusaba, M., Liu, C. & Yoshida, R. Crystal structure prediction with machine learning-based element substitution. Comput. Mater. Sci. 211, 111496 (2022).

    Article  Google Scholar 

  51. Wang, H.-C., Botti, S. & Marques, M. A. L. Predicting stable crystalline compounds using chemical similarity. npj Comput. Mater. 7, 12 (2021).

    Article  Google Scholar 

  52. Choubisa, H. et al. Interpretable discovery of semiconductors with machine learning. npj Comput. Mater. 9, 117 (2023).

    Article  Google Scholar 

  53. Zhao, R., Xing, B., Mu, H., Fu, Y. & Zhang, L. Evaluation of performance of machine learning methods in mining structure–property data of halide perovskite materials. Chin. Phys. B 31, 056302 (2022).

    Article  Google Scholar 

  54. Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M. & Ghiringhelli, L. M. SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys. Rev. Mater. 2, 083802 (2018).

    Article  Google Scholar 

  55. Bartel, C. J. et al. New tolerance factor to predict the stability of perovskite oxides and halides. Sci. Adv. 5, eaav0693 (2019).

    Article  Google Scholar 

  56. Purcell, T. A. R., Scheffler, M., Ghiringhelli, L. M. & Carbogno, C. Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence. npj Comput. Mater. 9, 112 (2023).

    Article  Google Scholar 

  57. Kim, S., Noh, J., Gu, G. H., Aspuru-Guzik, A. & Jung, Y. Generative adversarial networks for crystal structure prediction. ACS Cent. Sci. 6, 1412–1420 (2020).

    Article  Google Scholar 

  58. Zhao, Y. et al. High-throughput discovery of novel cubic crystal materials using deep generative neural networks. Adv. Sci. 8, 2100566 (2021).

    Article  Google Scholar 

  59. Zhao, Y. et al. Physics guided deep learning for generative design of crystal materials with symmetry constraints. npj Comput. Mater. 9, 38 (2023).

    Article  Google Scholar 

  60. Ren, Z. et al. An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties. Matter 5, 314–335 (2022).

    Article  Google Scholar 

  61. Xie, T., Fu, X., Ganea, O.-E., Barzilay, R. & Jaakkola, T. S. Crystal diffusion variational autoencoder for periodic material generation. In International Conference on Learning Representations (2022).

  62. Fu, N. et al. Material transformers: deep learning language models for generative materials design. Mach. Learn. Sci. Technol. 4, 015001 (2023).

    Article  Google Scholar 

  63. Zeni, C. et al. MatterGen: a generative model for inorganic materials design. Preprint at https://arxiv.org/abs/2312.03687 (2023).

  64. Lyngby, P. & Thygesen, K. S. Data-driven discovery of 2D materials by deep generative models. npj Comput. Mater. 8, 232 (2022).

    Article  Google Scholar 

  65. Moustafa, H., Lyngby, P. M., Mortensen, J. J., Thygesen, K. S. & Jacobsen, K. W. Hundreds of new, stable, one-dimensional materials from a generative machine learning model. Phys. Rev. Mater. 7, 014007 (2023).

    Article  Google Scholar 

  66. Wines, D., Xie, T. & Choudhary, K. Inverse design of next-generation superconductors using data-driven deep generative models. J. Phys. Chem. Lett. 14, 6630–6638 (2023).

    Article  Google Scholar 

  67. Siriwardane, E. M. D., Zhao, Y. & Hu, J. Data-driven deep generative design of stable spintronic materials. CrystEngComm 25, 6017–6029 (2023).

    Article  Google Scholar 

  68. Langer, M. F., Goeßmann, A. & Rupp, M. Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning. npj Comput. Mater. 8, 41 (2022).

    Article  Google Scholar 

  69. Behler, J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys. 134, 074106 (2011).

    Article  Google Scholar 

  70. Bartók, A. P., Kondor, R. & Csányi, G. On representing chemical environments. Phys. Rev. B 87, 184115 (2013).

    Article  Google Scholar 

  71. Grisafi, A., Wilkins, D. M., Csányi, G. & Ceriotti, M. Symmetry-adapted machine learning for tensorial properties of atomistic systems. Phys. Rev. Lett. 120, 036002 (2018).

    Article  Google Scholar 

  72. Eickenberg, M., Exarchakis, G., Hirn, M. & Mallat, S. Solid harmonic wavelet scattering: predicting quantum molecular energy from invariant descriptors of 3D electronic densities. In Advances in Neural Information Processing Systems Vol. 30, 6543–6552 (Curran Associates, 2017).

  73. Musaelian, A. et al. Learning local equivariant representations for large-scale atomistic dynamics. Nat. Commun. 14, 579 (2023).

    Article  Google Scholar 

  74. Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018).

    Article  Google Scholar 

  75. Chen, C., Ye, W., Zuo, Y., Zheng, C. & Ong, S. P. Graph networks as a universal machine learning framework for molecules and crystals. Chem. Mater. 31, 3564–3572 (2019).

    Article  Google Scholar 

  76. Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).

    Article  Google Scholar 

  77. Chen, C. & Ong, S. P. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. 2, 718–728 (2022).

    Article  Google Scholar 

  78. Batatia, I., Kovacs, D. P., Simm, G., Ortner, C. & Csanyi, G. MACE: higher order equivariant message passing neural networks for fast and accurate force fields. Adv. Neural Inf. Process. Syst. 35, 11423–11436 (2022).

    Google Scholar 

  79. Choudhary, K. et al. Unified graph neural network force-field for the periodic table: solid state applications. Digit. Discov. 2, 346–355 (2023).

    Article  Google Scholar 

  80. Deringer, V. L. et al. Gaussian process regression for materials and molecules. Chem. Rev. 121, 10073–10141 (2021).

    Article  Google Scholar 

  81. Thomas, N. et al. Tensor field networks: rotation- and translation-equivariant neural networks for 3D point clouds. Preprint at https://arxiv.org/abs/1802.08219 (2018).

  82. Gong, X. et al. General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian. Nat. Commun. 14, 2848 (2023).

    Article  Google Scholar 

  83. Zhong, Y., Yu, H., Su, M., Gong, X. & Xiang, H. Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids. npj Comput. Mater. 9, 182 (2023).

    Article  Google Scholar 

  84. Zhang, X. et al. Artificial intelligence for science in quantum, atomistic, and continuum systems. Preprint at https://arxiv.org/abs/2307.08423 (2023).

  85. Liu, S. et al. Symmetry-informed geometric representation for molecules, proteins, and crystalline materials. Adv. Neural Inf. Process. Syst. 36, 66084–66101 (2023).

    Google Scholar 

  86. Rautela, M., Senthilnath, J., Monaco, E. & Gopalakrishnan, S. Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations. Compos. Struct. 291, 115579 (2022).

    Article  Google Scholar 

  87. Guo, Z. et al. Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity. npj Comput. Mater. 9, 95 (2023).

    Article  Google Scholar 

  88. Knøsgaard, N. R. & Thygesen, K. S. Representing individual electronic states for machine learning GW band structures of 2D materials. Nat. Commun. 13, 468 (2022).

    Article  Google Scholar 

  89. Loftis, C., Yuan, K., Zhao, Y., Hu, M. & Hu, J. Lattice thermal conductivity prediction using symbolic regression and machine learning. J. Phys. Chem. A 125, 435–450 (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

  91. Jaafreh, R., Kang, Y. S. & Hamad, K. Lattice thermal conductivity: an accelerated discovery guided by machine learning. ACS Appl. Mater. Interfaces 13, 57204–57213 (2021).

    Article  Google Scholar 

  92. Zhu, T. et al. Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics. Energy Environ. Sci. 14, 3559–3566 (2021).

    Article  Google Scholar 

  93. Luo, Y., Li, M., Yuan, H., Liu, H. & Fang, Y. Predicting lattice thermal conductivity via machine learning: a mini review. npj Comput. Mater. 9, 4 (2023).

    Article  Google Scholar 

  94. Ladygin, V. V., Korotaev, P. Y., Yanilkin, A. V. & Shapeev, A. V. Lattice dynamics simulation using machine learning interatomic potentials. Comput. Mater. Sci. 172, 109333 (2020).

    Article  Google Scholar 

  95. Li, R. et al. A deep neural network interatomic potential for studying thermal conductivity of β-Ga2O3. Appl. Phys. Lett. 117, 152102 (2020).

    Article  Google Scholar 

  96. Babaei, H., Guo, R., Hashemi, A. & Lee, S. Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies. Phys. Rev. Mater. 3, 074603 (2019).

    Article  Google Scholar 

  97. Qian, X., Peng, S., Li, X., Wei, Y. & Yang, R. Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon. Mater. Today Phys. 10, 100140 (2019).

    Article  Google Scholar 

  98. Verdi, C., Karsai, F., Liu, P., Jinnouchi, R. & Kresse, G. Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials. npj Comput. Mater. 7, 156 (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

  100. Na, G. S., Jang, S. & Chang, H. Predicting thermoelectric properties from chemical formula with explicitly identifying dopant effects. npj Comput. Mater. 7, 106 (2021).

    Article  Google Scholar 

  101. Antunes, L. M., Butler, K. T. & Grau-Crespo, R. Predicting thermoelectric transport properties from composition with attention-based deep learning. Mach. Learn. Sci. Technol. 4, 015037 (2023).

    Article  Google Scholar 

  102. Pimachev, A. K. & Neogi, S. First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning. npj Comput. Mater. 7, 93 (2021).

    Article  Google Scholar 

  103. Li, H. et al. Deep-learning density functional perturbation theory. Phys. Rev. Lett. 132, 096401 (2024).

    Article  Google Scholar 

  104. Ozaki, T., Nishio, K. & Kino, H. Efficient implementation of the nonequilibrium Green function method for electronic transport calculations. Phys. Rev. B 81, 035116 (2010).

    Article  Google Scholar 

  105. Bürkle, M. et al. Deep-learning approach to first-principles transport simulations. Phys. Rev. Lett. 126, 177701 (2021).

    Article  Google Scholar 

  106. Dral, P. O. & Barbatti, M. Molecular excited states through a machine learning lens. Nat. Rev. Chem. 5, 388–405 (2021).

    Article  Google Scholar 

  107. Westermayr, J. & Marquetand, P. Machine learning for electronically excited states of molecules. Chem. Rev. 121, 9873–9926 (2021).

    Article  Google Scholar 

  108. Zhuo, Y., Mansouri Tehrani, A. & Brgoch, J. Predicting the band gaps of inorganic solids by machine learning. J. Phys. Chem. Lett. 9, 1668–1673 (2018).

    Article  Google Scholar 

  109. Li, W. et al. Predicting band gaps and band-edge positions of oxide perovskites using density functional theory and machine learning. Phys. Rev. B 106, 155156 (2022).

    Article  MathSciNet  Google Scholar 

  110. Zhang, L. et al. Accurate band gap prediction based on an interpretable Δ-machine learning. Mater. Today Commun. 33, 104630 (2022).

    Article  Google Scholar 

  111. Zauchner, M. G., Horsfield, A. & Lischner, J. Accelerating GW calculations through machine-learned dielectric matrices. npj Comput. Mater. 9, 184 (2023).

    Article  Google Scholar 

  112. Dong, S. S., Govoni, M. & Galli, G. Machine learning dielectric screening for the simulation of excited state properties of molecules and materials. Chem. Sci. 12, 4970–4980 (2021).

    Article  Google Scholar 

  113. Chen, C., Zuo, Y., Ye, W., Li, X. & Ong, S. P. Learning properties of ordered and disordered materials from multi-fidelity data. Nat. Comput. Sci. 1, 46–53 (2021).

    Article  Google Scholar 

  114. Ramakrishnan, R., Dral, P. O., Rupp, M. & von Lilienfeld, O. A. Big data meets quantum chemistry approximations: the Δ-machine learning approach. J. Chem. Theory Comput. 11, 2087–2096 (2015).

    Article  Google Scholar 

  115. Wang, S. et al. Effective lifetime of non-equilibrium carriers in semiconductors from non-adiabatic molecular dynamics simulations. Nat. Comput. Sci. 2, 486–493 (2022).

    Article  Google Scholar 

  116. Zhang, Z., Wang, J., Zhang, Y., Xu, J. & Long, R. Charge recombination dynamics in a metal halide perovskite simulated by nonadiabatic molecular dynamics combined with machine learning. J. Phys. Chem. Lett. 13, 10734–10740 (2022).

    Article  Google Scholar 

  117. Akimov, A. V. Extending the time scales of nonadiabatic molecular dynamics via machine learning in the time domain. J. Phys. Chem. Lett. 12, 12119–12128 (2021).

    Article  Google Scholar 

  118. Wang, B., Chu, W., Tkatchenko, A. & Prezhdo, O. V. Interpolating nonadiabatic molecular dynamics hamiltonian with artificial neural networks. J. Phys. Chem. Lett. 12, 6070–6077 (2021).

    Article  Google Scholar 

  119. Niu, H., Bonati, L., Piaggi, P. M. & Parrinello, M. Ab initio phase diagram and nucleation of gallium. Nat. Commun. 11, 2654 (2020).

    Article  Google Scholar 

  120. Zhang, D., Yi, P., Lai, X., Peng, L. & Li, H. Active machine learning model for the dynamic simulation and growth mechanisms of carbon on metal surface. Nat. Commun. 15, 344 (2024).

    Article  Google Scholar 

  121. Xu, M. et al. Machine learning driven synthesis of few-layered WTe2 with geometrical control. J. Am. Chem. Soc. 143, 18103–18113 (2021).

    Article  Google Scholar 

  122. Lu, M. et al. Machine learning-assisted synthesis of two-dimensional materials. ACS Appl. Mater. Interfaces 15, 1871–1878 (2023).

    Article  Google Scholar 

  123. Zhang, J., Wang, F., Shenoy, V. B., Tang, M. & Lou, J. Towards controlled synthesis of 2D crystals by chemical vapor deposition (CVD). Mater. Today 40, 132–139 (2020).

    Article  Google Scholar 

  124. Volk, A. A. et al. AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning. Nat. Commun. 14, 1403 (2023).

    Article  Google Scholar 

  125. Beckham, J. L. et al. Machine learning guided synthesis of flash graphene. Adv. Mater. 34, 2106506 (2022).

    Article  Google Scholar 

  126. Bhowmik, S. & Govind Rajan, A. Chemical vapor deposition of 2D materials: a review of modeling, simulation, and machine learning studies. iScience 25, 103832 (2022).

    Article  Google Scholar 

  127. Tao, H. et al. Nanoparticle synthesis assisted by machine learning. Nat. Rev. Mater. 6, 701–716 (2021).

    Article  Google Scholar 

  128. Costine, A., Delsa, P., Li, T., Reinke, P. & Balachandran, P. V. Data-driven assessment of chemical vapor deposition grown MoS2 monolayer thin films. J. Appl. Phys. 128, 235303 (2020).

    Article  Google Scholar 

  129. Dahl, J. C., Wang, X., Huang, X., Chan, E. M. & Alivisatos, A. P. Elucidating the weakly reversible Cs–Pb–Br perovskite nanocrystal reaction network with high-throughput maps and transformations. J. Am. Chem. Soc. 142, 11915–11926 (2020).

    Article  Google Scholar 

  130. Han, Y. et al. Machine-learning-driven synthesis of carbon dots with enhanced quantum yields. ACS Nano 14, 14761–14768 (2020).

    Article  Google Scholar 

  131. Balachandran, P. V., Xue, D., Theiler, J., Hogden, J. & Lookman, T. Adaptive strategies for materials design using uncertainties. Sci. Rep. 6, 19660 (2016).

    Article  Google Scholar 

  132. Schmidt, J., Marques, M. R. G., Botti, S. & Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 5, 83 (2019).

    Article  Google Scholar 

  133. Lookman, T., Balachandran, P. V., Xue, D. & Yuan, R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput. Mater. 5, 21 (2019).

    Article  Google Scholar 

  134. Ohkubo, I. et al. Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning. Mater. Today Phys. 16, 100296 (2021).

    Article  Google Scholar 

  135. Salley, D. et al. A nanomaterials discovery robot for the Darwinian evolution of shape programmable gold nanoparticles. Nat. Commun. 11, 2771 (2020).

    Article  Google Scholar 

  136. Liow, C. H. et al. Machine learning assisted synthesis of lithium-ion batteries cathode materials. Nano Energy 98, 107214 (2022).

    Article  Google Scholar 

  137. Li, J. et al. Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab. Nat. Commun. 11, 2046 (2020).

    Article  Google Scholar 

  138. Li, Y.-F. & Liu, Z.-P. Smallest stable Si/SiO2 interface that suppresses quantum tunneling from machine-learning-based global search. Phys. Rev. Lett. 128, 226102 (2022).

    Article  Google Scholar 

  139. Chen, X. et al. Wafer-scale functional circuits based on two dimensional semiconductors with fabrication optimized by machine learning. Nat. Commun. 12, 5953 (2021).

    Article  Google Scholar 

  140. Liu, Z. et al. Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing. Joule 6, 834–849 (2022).

    Article  Google Scholar 

  141. Liu, W. et al. Machine learning enables intelligent screening of interface materials towards minimizing voltage losses for p–i–n type perovskite solar cells. J. Energy Chem. 83, 128–137 (2023).

    Article  Google Scholar 

  142. Yoo, J. et al. Machine-learning based TCAD optimization method for next generation BCD process development. In 2021 33rd International Symposium on Power Semiconductor Devices and ICs (ISPSD) 279–282 (IEEE, 2021).

  143. Zhao, Z. & Cai, Z. A review of intelligent design for test based on machine learning. In 2023 International Symposium of Electronics Design Automation (ISEDA) 116–120 (IEEE, 2023).

  144. Amuru, D. et al. AI/ML algorithms and applications in VLSI design and technology. Integration 93, 102048 (2023).

    Article  Google Scholar 

  145. Kim, T. & Behdinan, K. Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review. J. Intell. Manuf. 34, 3215–3247 (2023).

    Article  Google Scholar 

  146. Gopakumar, A. M., Balachandran, P. V., Xue, D., Gubernatis, J. E. & Lookman, T. Multi-objective optimization for materials discovery via adaptive design. Sci. Rep. 8, 3738 (2018).

    Article  Google Scholar 

  147. Tan, C. et al. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning—ICANN 2018 (eds Kůrková, V. et al.) 270–279 (Springer, 2018).

  148. Gawlikowski, J. et al. A survey of uncertainty in deep neural networks. Artif. Intell. Rev. 56, 1513–1589 (2023).

    Article  Google Scholar 

  149. Tolborg, K., Klarbring, J., M. Ganose, A. & Walsh, A. Free energy predictions for crystal stability and synthesisability. Digit. Discov. 1, 586–595 (2022).

    Article  Google Scholar 

  150. Dotson, J. J. et al. Data-driven multi-objective optimization tactics for catalytic asymmetric reactions using bisphosphine ligands. J. Am. Chem. Soc. 145, 110–121 (2023).

    Article  Google Scholar 

Download references

Acknowledgements

L.Z. acknowledges funding support from the National Key Research and Development Program of China (grant number 2022YFA1402500) and the National Natural Science Foundation of China (grant numbers 62125402 and 62321166653).

Author information

Authors and Affiliations

Authors

Contributions

L.Z. led the preparation, writing and editing of this Perspective. J.X. contributed most of the text and figures. Y.Z. and Z.L. assisted in writing and figure preparation. X.W. and M.F. reviewed and refined the paper. All authors contributed to discussions and feedback.

Corresponding authors

Correspondence to Xinjiang Wang or Lijun Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Computational Science thanks Senthilnath Jayavelu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Table 1

An overview of representative data-driven-method-empowered studies of semiconductor material design and device optimization research. This table summarizes the representative studies from this Perspective, highlighting their main discovery, data-driven method, public dataset and access link.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, J., Zhou, Y., Faizan, M. et al. Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies. Nat Comput Sci 4, 322–333 (2024). https://doi.org/10.1038/s43588-024-00632-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-024-00632-5

Search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics