Metamaterials are artificial materials designed to exhibit effective material
parameters that go beyond those found in nature. Composed of unit cells with
rich designability that are assembled into multiscale systems, they hold great
promise for realizing next-generation devices with exceptional, often exotic,
functionalities. However, the vast design space and intricate
structure-property relationships pose significant challenges in their design. A
compelling paradigm that could bring the full potential of metamaterials to
fruition is emerging: data-driven design. In this review, we provide a holistic
overview of this rapidly evolving field, emphasizing the general methodology
instead of specific domains and deployment contexts. We organize existing
research into data-driven modules, encompassing data acquisition, machine
learning-based unit cell design, and data-driven multiscale optimization. We
further categorize the approaches within each module based on shared
principles, analyze and compare strengths and applicability, explore
connections between different modules, and identify open research questions and
opportunities.

介绍了数据驱动设计范例这一概念，并将现有的研究分为数据获取、基于机器学习的单元格设计、数据驱动的多尺度优化三个模块进行了综述，分析比较了各个模块的优势和适用性，并探讨了模块之间的联系。

基于数据驱动的超材料及多尺度系统设计综述

Data-Driven Design for Metamaterials and Multiscale Systems: A Review

Data-driven design is making headway into a number of application areas,
including protein, small-molecule, and materials engineering. The design goal
is to construct an object with desired properties, such as a protein that binds
to a therapeutic target, or a superconducting material with a higher critical
temperature than previously observed. To that end, costly experimental
measurements are being replaced with calls to high-capacity regression models
trained on labeled data, which can be leveraged in an in silico search for
design candidates. However, the design goal necessitates moving into regions of
the design space beyond where such models were trained. Therefore, one can ask:
should the regression model be altered as the design algorithm explores the
design space, in the absence of new data? Herein, we answer this question in
the affirmative. In particular, we (i) formalize the data-driven design problem
as a non-zero-sum game, (ii) develop a principled strategy for retraining the
regression model as the design algorithm proceeds---what we refer to as
autofocusing, and (iii) demonstrate the promise of autofocusing empirically.

数据驱动设计正在广泛应用于蛋白质、小分子和材料工程等领域。然而，设计目标需要进入超出模型训练范围的设计空间， 因此需要针对式样寻找新的候选设计。本文提出了一种自动对焦方法来调整回归模型，以适应训练范围之外的新式样，从而更好地实现数据驱动设计目标。