新的研究范式有望为目前材料科学与工程面临的挑战提供解决方案.本文回顾材料基因工程领域的研究进展，并展望其在材料设计中的作用.提出将材料整合到工程设计流程中，在计算和数据驱动方法的基础上，实现材料的“按需设计”，从而推动材料的研发和工程应用.
Emerging paradigms provide a promising solution to current challenges faced by materials science and engineering. In this paper, the progress in materials genome engineering is reviewed, and its facilitation of the materials design is disscussed. Based on the computational and data-driven methodologies, materials could be integrated into the engineering design cycle to realize demand-driven materials design, so as to accelerate the discovery and application of materials.
［1］CORREA-BAENA J P, HIPPALGAONKAR K, VAN DUREN J, et al. Accelerating materials development via automation, machine learning, and high-performance computing［J］. Joule, 2018, 2(8): 1410-1420.
［2］BOYD P G, CHIDAMBARAM A, GARCA-DEZ E, et al. Data-driven design of metal-organic frameworks for wet flue gas CO2 capture［J］. Nature, 2019, 576(7786): 253-256.
［3］WANG Y Q, IYER A, CHEN W, et al. Featureless adaptive optimization accelerates functional electronic materials design［J］. Applied Physics Reviews, 2020, 7(4): 041403.
［4］SANCHEZ-LENGELING B, ASPURU-GUZIK A. Inverse molecular design using machine learning: Generative models for matter engineering［J］. Science, 2018, 361(6400): 360-365.
［5］SIMM G, PINSLER R, HERNANDEZ-LOBATO J M. Reinforcement learning for molecular design guided by quantum mechanics ［C］∥Proceedings of the 37th International Conference on Machine Learning. Vienna, Austria: PMLR, 2020: 8959-8969.
