By empowering systems to carry out tasks like decision-making and problem-solving that call for human-like intellect, artificial intelligence (AI) is revolutionizing technology. A key enabler of this progress is functional materials, designed through materials engineering to meet the needs of high-performance technologies like semiconductors, sensors, and memory storage. These materials are vital in ensuring AI systems operate efficiently by providing the computational power, energy efficiency, and sensory capabilities required. The significance of functional materials using AI lies in how AI-driven methods, such as density functional theory (DFT) and deep learning, accelerate the discovery, design, and optimization of materials. AI allows researchers to predict and engineer materials at the atomic level, leading to innovations like graphene and gallium nitride for high-performance GPUs, low-power transistors for energy-efficient devices, and thermoelectric materials for sustainable energy solutions. These breakthroughs are essential for AI applications in industries like autonomous vehicles, healthcare, and energy. AI also enhances sensor development, enabling real-time applications, such as autonomous navigation, wearable health devices, and robotics. The significance of functional materials in AI is also seen in their impact on system performance. Semiconductors enable parallel processing for deep learning tasks, optical materials support LiDAR systems for navigation purposes, and advanced memory storage technologies ensure efficient data handling. The integration of AI and materials engineering accelerates the creation of intelligent, sustainable, and high-performance technologies, driving innovations that benefit sectors like healthcare, energy, and autonomous systems, ultimately advancing societal progress.

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1. Chen C, Zuo Y, Ye W, Li X, Deng Z, Ong SP. A critical review of machine learning of energy materials. Adv Energy Mater. 2020;10(8):1903242.
2. Gupta V, Choudhary K, Tavazza F, Campbell C, Liao WK, Choudhary A, et al. Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data. Nat Commun. 2021;12(1):6595.
3. Choubisa H, Todorović P, Pina JM, Parmar DH, Li Z, Voznyy O, et al. Interpretable discovery of new semiconductors with machine learning. arXiv preprint arXiv:2101.04383. 2021.
4. Omee SS, Louis SY, Fu N, Wei L, Dey S, Dong R, et al. Scalable deeper graph neural networks for high-performance materials property prediction. Patterns. 2022;3(5).
5. Ward L, Agrawal A, Choudhary A, Wolverton C. A general-purpose machine learning framework for predicting properties of inorganic materials. Comput Mater. 2016;2(1):1–7.
6. Xie T, Grossman JC. Hierarchical visualization of materials space with graph convolutional neural networks. J Chem Phys. 2018;149(17).
7. Gormley AJ, Webb MA. Machine learning in combinatorial polymer chemistry. Nat Rev Mater. 2021;6(8):642–644.
8. Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science. 2018;361(6400):360–365.
9. Mirza M. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. 2014.
10. Yang JM, Lee JH, Jung YK, Kim SY, Kim JH, Kim SG, et al. Mixed‐dimensional formamidinium bismuth iodides featuring In‐situ formed type‐I band structure for convolution neural networks. Adv Sci. 2022;9(14):2200168.

By empowering systems to carry out tasks like decision-making and problem-solving that call for human-like intellect, artificial intelligence (AI) is revolutionizing technology. A key enabler of this progress is functional materials, designed through materials engineering to meet the needs of high-performance technologies like semiconductors, sensors, and memory storage. These materials are vital in ensuring AI systems operate efficiently by providing the computational power, energy efficiency, and sensory capabilities required. The significance of functional materials using AI lies in how AI-driven methods, such as density functional theory (DFT) and deep learning, accelerate the discovery, design, and optimization of materials. AI allows researchers to predict and engineer materials at the atomic level, leading to innovations like graphene and gallium nitride for high-performance GPUs, low-power transistors for energy-efficient devices, and thermoelectric materials for sustainable energy solutions. These breakthroughs are essential for AI applications in industries like autonomous vehicles, healthcare, and energy. AI also enhances sensor development, enabling real-time applications, such as autonomous navigation, wearable health devices, and robotics. The significance of functional materials in AI is also seen in their impact on system performance. Semiconductors enable parallel processing for deep learning tasks, optical materials support LiDAR systems for navigation purposes, and advanced memory storage technologies ensure efficient data handling. The integration of AI and materials engineering accelerates the creation of intelligent, sustainable, and high-performance technologies, driving innovations that benefit sectors like healthcare, energy, and autonomous systems, ultimately advancing societal progress.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Chen C, Zuo Y, Ye W, Li X, Deng Z, Ong SP. A critical review of machine learning of energy materials. Adv Energy Mater. 2020;10(8):1903242.
2. Gupta V, Choudhary K, Tavazza F, Campbell C, Liao WK, Choudhary A, et al. Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data. Nat Commun. 2021;12(1):6595.
3. Choubisa H, Todorović P, Pina JM, Parmar DH, Li Z, Voznyy O, et al. Interpretable discovery of new semiconductors with machine learning. arXiv preprint arXiv:2101.04383. 2021.
4. Omee SS, Louis SY, Fu N, Wei L, Dey S, Dong R, et al. Scalable deeper graph neural networks for high-performance materials property prediction. Patterns. 2022;3(5).
5. Ward L, Agrawal A, Choudhary A, Wolverton C. A general-purpose machine learning framework for predicting properties of inorganic materials. Comput Mater. 2016;2(1):1–7.
6. Xie T, Grossman JC. Hierarchical visualization of materials space with graph convolutional neural networks. J Chem Phys. 2018;149(17).
7. Gormley AJ, Webb MA. Machine learning in combinatorial polymer chemistry. Nat Rev Mater. 2021;6(8):642–644.
8. Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science. 2018;361(6400):360–365.
9. Mirza M. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. 2014.
10. Yang JM, Lee JH, Jung YK, Kim SY, Kim JH, Kim SG, et al. Mixed‐dimensional formamidinium bismuth iodides featuring In‐situ formed type‐I band structure for convolution neural networks. Adv Sci. 2022;9(14):2200168.