摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的研究结果在一份新的报告中讨论。根据NewsRx记者从德国耶拿发回的新闻报道，研究表明：“在利用机器学习算法预测玻璃性质的能力方面，我们系统地研究了由密度泛函理论模拟得到的AB ini TiO描述符的性能，并与传统的组分描述符进行了比较。为此，我们使用了两个数据集：一个广泛的、公开的数据库，用于研究各种氧化物玻璃，以及一个小型的内部数据集，涵盖了从金属到非金属材料无机玻璃的B Roader收集。这项研究的财政支持者包括联邦教育与研究部(BMBF)、联邦教育与研究部(BMBF)、卡尔蔡斯基金会、德国研究基金会(DFG)。我们的新闻编辑引用了Friedrich-Schiller-University Jena的研究，“对于较大的数据集，从头算描述器与成分描述器RS相比，在保持接近Y等价的预测性能的同时，在较小的数据集中，从头算描述器和成分描述器的组合显示了预测精度的提高。”从头算得到的DESC激射器的性能明显优于组分描述符，为在数据有限的情况下改进玻璃性能预测提供了一个有价值的工具。从头算导出的描述符不仅计算成本低，而且可以在训练组合空间之外进行外推，而且有助于模型解释。简单而有效的描述符：从头算导出的描述符改进了机器学习模型在小数据集中预测玻璃性质的性能。它们提供降维，并允许超出训练集初始组合空间的预测。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Jena , Germany, by NewsRx correspondents, research stated, "The performance of ab ini tio descriptors derived from density functional theory simulations is systematic ally investigated in comparison to traditional compositional descriptors for the ability to predict glass properties utilizing machine learning algorithms. Two datasets are used for this purpose: an extensive, publicly available database in volving a wide range of oxide glasses, and a small in-house dataset covering a b roader collection of inorganic glasses from metallic to non-metallic materials." Financial supporters for this research include Federal Ministry of Education & Research (BMBF), Federal Ministry of Education & Research (BMBF), Carl Zeiss Foundation, German Research Foundation (DFG). Our news editors obtained a quote from the research from Friedrich-Schiller-Univ ersity Jena, "For the larger dataset, it was demonstrated that ab initio descrip tors offer a substantial reduction in input dimensionality while retaining nearl y equivalent predictive performance when compared to the compositional descripto rs. The combination of ab initio and compositional descriptors showed an improve ment in prediction accuracy. For the smaller dataset, the ab initio-derived desc riptors performed significantly better than the compositional descriptors, provi ding a valuable tool to improve glass property prediction in settings where the availability of data is limited. Furthermore, ab initio-derived descriptors are not only computationally inexpensive and allow extrapolation beyond the training composition space but also facilitate model interpretation. Simple yet effectiv e descriptors: ab initio-derived descriptors improve the performance of machine learning models for predicting glass properties in small datasets. They provide dimensionality reduction and enable predictions beyond the initial compositional space of the training set."
NETL
NSTL