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.

翻译：先进材料的快速设计是一个具有重大科学意义的研究课题。传统的“正向”材料设计范式涉及对多个候选材料进行评估，以确定最符合目标性能的最佳候选材料。然而，深度学习领域的最新进展催生了“逆向”设计范式在先进材料中应用的可能性——在该范式中，提供目标性能的模型能够直接找到最佳候选材料。作为一个相对较新的概念，目前仍需系统评估这两种范式在实际应用中的表现。因此，本研究的目的是直接定量比较正向设计与逆向设计建模范式。我们通过两个具有不同目标和约束条件的难熔高熵合金设计案例，将逆向设计方法与局部正向搜索、高通量筛选和多目标优化等其他正向方案进行了对比。