COMPARING FORWARD AND INVERSE DESIGN PARADIGMS: A CASE STUDY ON REFRACTORY HIGH-ENTROPY ALLOYS

作     者：Debnath, Arindam Li, Wenjie Ahn, Marcia Shang, Shunli Liu, Zi-Kui Raman, Lavanya Krajewski, Adam M. Lin, Shuang Beese, Allison M. Reinhart, Wesley F. 

作者机构：Department of Materials Science and Engineering Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Department of Mechanical Engineering Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Institute for Computational and Data Sciences Pennsylvania State University University ParkPA16802 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Refractory materials 

摘      要：The rapid design of advanced materials is a topic of great scientific interest. The conventional, forward paradigm of materials design involves evaluating multiple candidates to determine the best candidate that matches the target properties. However, recent advances in the field of deep learning have given rise to the possibility of an inverse design paradigm for advanced materials, wherein a model provided with the target properties is able to find the best candidate. Being a relatively new concept, there remains a need to systematically evaluate how these two paradigms perform in practical applications. Therefore, the objective of this study is to directly, quantitatively compare the forward and inverse design modeling paradigms. We do so by considering two case studies of refractory high-entropy alloy design with different objectives and constraints and comparing the inverse design method to other forward schemes like localized forward search, high throughput screening, and multi objective optimization. Copyright © 2023, The Authors. All rights reserved.