计算加速的实验材料表征——借鉴高通量模拟工作流
Computationally accelerated experimental materials characterization -- drawing inspiration from high-throughput simulation workflows
AI总结：
本研究在pyiron框架中实现主动学习循环，直接连接实验设备，结合DFT先验知识与文献挖掘预测，加速实验材料表征，实现实验与模拟数据统一管理。
摘要：
计算材料科学日益受益于数据管理、自动化及基于算法的决策制定，用于模拟材料性质和行为。实验材料科学也通过将“机器学习”纳入材料发现活动而迅速变化。然而，自动化、可重复性、数据溯源以及管理数据的可重用性等明显优势，在实验领域并未广泛实现。我们展示了在pyiron（一个专为高通量模拟设计的框架）中实现的一个主动学习循环，该循环直接与实验测量设备接口，作为将实验与模拟数据整合于同一框架的示范。除了主动学习方法带来的加速外，实验表征的额外加速还通过利用密度泛函理论模拟的先验知识以及基于词嵌入相关性从文献挖掘中获得的成分-性质预测来实现。通过将所有领域的数据整合于同一框架，材料表征和材料发现活动中此前未被利用且急需的加速潜力得以释放。
英文摘要：
Computational materials science increasingly benefits from data management, automation, and algorithm-based decision-making for the simulation of material properties and behavior. Experimental materials science also changes rapidly by incorporation of `machine learning' in materials discovery campaigns. The obvious benefits which include automation, reproducibility, data provenance, and reusability of managed data, however, is not widely available in the experimental domain. We present an implementation of a Active Learning loop with a direct interface to an experimental measurement device in pyiron, a framework designed for high-throughput simulations, as demonstrator how to combine experimental and simulated data in one framework. Apart from the acceleration provided by the active learning approach, additional acceleration of the experimental characterization is achieved by using prior knowledge from density functional theory simulations as well as composition-property predictions from literature mining using correlations in word embeddings. With data from all domains in the same framework, a heretofore untapped and much-needed potential for the acceleration of materials characterization and materials discovery campaigns becomes available.
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。
