Engineering and Materials Science

Demand-Driven Materials Design

Expand
  • Department of Mechanical Engineering, Northwestern University, Evanston 60208, Illinois, USA

Received date: 2021-02-03

  Online published: 2021-04-09

Abstract

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.

Cite this article

ZHANG Hengrui . Demand-Driven Materials Design[J]. Journal of Shanghai Jiaotong University, 2021 , 55(Sup.1) : 93 -94 . DOI: 10.16183/j.cnki.jsjtu.2021.S1.001

References

[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, GARCA-DEZ 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.
Outlines

/