探索光电子材料的自动驾驶实验室
Exploring self-driving labs for optoelectronic materials
AI总结：
本文提出了一种以探索为导向的自动驾驶实验室，用于生成数据驱动的科学数据，通过系统扰动热力学和动力学合成变量，测量缺陷敏感可观测性，以改进光电子材料的合成-性质关系理解。
摘要：
自动驾驶实验室（SDLs）通过结合自动化与机器学习引导的实验选择，有潜力变革材料科学的实验方法。到目前为止，大多数SDLs都是优化驱动的，旨在快速收敛性能指标，通过在平台特定的替代模型中嵌入多个机理层。此类方法在过程调节方面表现出色，但对合成-性质关系背后的物理机制提供有限的见解。本文提出了一种互补的范式：探索驱动或科学驱动的SDL，其主要目的是生成用于数据驱动科学的数据。我们以无机光电子材料为例，论证缺陷物理是合成条件与功能性质之间机理联系的核心，为设计合适的SDL提供了基础。由于缺陷群体及其空间组织通常无法直接解析，也不能完全从第一性原理预测，SDL的任务是生成数据集，在其中系统扰动热力学和动力学合成变量，并并行测量缺陷敏感可观测性。基于此基础，我们提出了一套科学SDL的设计原则，使其能够接近光电子材料的物理，从而生成可转移和可重用的数据集，提供根本性的见解。我们以Cu2ZnSn(S,Se)4作为案例研究，既展示缺陷意识材料探索任务的规模，也突出当前范式的不足。我们提出，经过适当设计的SDLs能够生成必要的结构数据集，以促进机理推断并推进合成意识材料设计。
英文摘要：
Self-driving laboratories (SDLs), by combining automation with machine learning-guided experiment selection, have the potential to transform experimental materials science. To date, most SDLs have been optimisation-driven, designed to rapidly converge on performance metrics, by embedding multiple mechanistic layers within platform-specific surrogate models. Such approaches excel at process tuning yet offer limited insight into the underlying physics governing synthesis-property relationships. Here we articulate a complementary paradigm: the exploration-driven, or scientific, SDL, whose primary purpose is the generation of data for data-driven science. We exemplify this concept for the case of inorganic optoelectronic materials, arguing that defect physics, which forms the central mechanistic link between synthesis conditions and functional properties, provides the foundation for designing a suitable SDL. Because defect populations and their spatial organisation cannot generally be resolved directly - nor fully predicted from first principles - the task of the SDL is to generate datasets in which thermodynamic and kinetic synthesis variables are systematically perturbed and defect-sensitive observables measured in parallel. From this basis, we propose a set of design principles for scientific SDLs that will enable them to operate close to the physics of optoelectronic materials, thereby generating transferrable and reusable datasets offering radical insight. We use Cu2ZnSn(S,Se)4 as a case study, both to show the scale of the task of defect-aware materials exploration as well to highlight as the deficiencies in the current paradigm. We propose that properly designed SDLs can generate the structured datasets necessary to enable mechanistic inference and advance synthesis-aware materials design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
