摘要
由机器人与机器学习每日新闻的新闻记者兼新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsRx编辑来自德国朱里奇的消息，这项研究指出，“确定、理解和预测所谓的结构-性质关系是许多科学领域的一项重要任务，如化学、生物学、气象学、物理学、工程学和其他学科。”这项研究的财政支持者包括Forschungszentrum Julich Gmbh；欧盟绳索研究理事会。新闻记者从Forschungsszentrum J Ulich GmbH的研究中获得了一句话：“结构是指物质、材料或物质的空间分布，而性质是一种由此产生的特征，通常取决于结构的空间细节。传统上，正演模拟模型被用于此类任务。最近，各种机器学习算法已被应用于这些科学领域以增强和加速仿真模型或作为代理模型。我们基于材料科学领域的两个不同数据集，开发并研究了六种机器学习技术的应用：来自预测磁畴形成的二维Ising模型的数据和来自Cahn-Hilliard模型的代表双相微观结构演变的数据。我们分析了所有模型的准确性和鲁棒性，并阐明了它们性能差异的原因。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news originating from Julich, Germany, by NewsRx editors, the research stated, “Determining, understanding, and predicting the so -called structure-property relation is an important task in many scientific disc iplines, such as chemistry, biology, meteorology, physics, engineering, and mate rials science.” Financial supporters for this research include Forschungszentrum Julich Gmbh; Eu ropean Research Council. The news journalists obtained a quote from the research from Forschungszentrum J ulich GmbH: “Structure refers to the spatial distribution of, e.g., substances, material, or matter in general, while property is a resulting characteristic tha t usually depends in a non-trivial way on spatial details of the structure. Trad itionally, forward simulations models have been used for such tasks. Recently, s everal machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. In this work , we develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science: data from a two-dimensional Ising model for predicting the formation of magnetic domains and data representing the evolution of dual-phase microstructures from the Cahn- Hilliard model. We analyze the accuracy and robustness of all models and elucida te the reasons for the differences in their performances.”
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