Computational Physics, AI & Machine‑Learning in Materials/Device Design
Keywords:
Computational Physics, Machine Learning, Artificial Intelligence, Materials Design, Device Design, Density Functional Theory, Molecular Dynamics, Physics-Informed Neural Networks, Surrogate Modeling, Inverse Design, High-Throughput ScreeningAbstract
The integration of computational physics, artificial intelligence (AI), and machine learning (ML) is transforming the design and discovery of advanced materials and devices. Computational physics techniques such as density functional theory, molecular dynamics, and finite element analysis provide accurate predictions of material properties and device behavior at multiple scales. However, these simulations are often computationally expensive and time-consuming. AI and ML methods complement these approaches by enabling data-driven modeling, surrogate simulations, inverse design, and high-throughput screening, thereby accelerating the exploration of vast material and device design spaces. Physics-informed machine learning further ensures that predictions remain consistent with underlying physical laws, bridging the gap between computational accuracy and computational efficiency. The design and discovery of advanced materials and devices are increasingly driven by the integration of computational physics with artificial intelligence (AI) and machine learning (ML). Computational physics provides a rigorous framework for modeling material behavior and device performance based on fundamental physical laws, but high-fidelity simulations are often computationally intensive and time-consuming. AI and ML complement these methods by enabling data-driven predictions, rapid screening, and inverse design, significantly accelerating the exploration of complex material and device design spaces.
This interdisciplinary approach has facilitated high-throughput materials discovery, optimization of device architectures, and autonomous closed-loop design workflows, where simulations, experiments, and AI models iteratively inform each other. Emerging trends include generative AI for materials and device design, physics-informed ML, multi-scale and multi-fidelity modeling, quantum-enhanced simulations, and sustainable, responsible design practices. Despite challenges in data availability, interpretability, and scalability, the synergy of computational physics and AI/ML is poised to transform materials science and device engineering, offering faster, smarter, and more efficient pathways for innovation.
How to cite this article:
Chemaoi P J. Computational Physics, AI & Machine‑Learning in Materials/Device Design. J Adv Res Appl Phy Appl 2025; 3(2): 10-14.
References
Gubernatis, J. E. & Lookman, T. (2018). Machine learning in materials design and discovery: Examples from the present and suggestions for the future. Physical Review Materials, 2, 120301.
Guo, K., Yang, Z., Yu, CH. & Buehler, M. J. (2021). Artificial intelligence and machine learning in design of mechanical materials. Materials Horizons, 8, 11531172.
Lee, J., Park, D., Lee, M., Lee, H., Park, K., Lee, I. & Ryu, S. (2023). Machine learning‑based inverse design methods considering data characteristics and design space size in
materials design and manufacturing: a review. Materials Horizons, 10, 54365456.
Shang, W., et al. (2024). A review on machine learningguided design of energy materials. Progress in Energy, 6(4), 042005.
Damewood, J., Karaguesian, J. R., Lunger, J. R., Tan, A. R., Xie, M., Peng, J. & GómezBombarelli, R. (2023). Representations of Materials for Machine Learning. arXiv preprint.
