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实验形成焓的金属间化合物相和其他无机化合物。

Experimental formation enthalpies for intermetallic phases and other inorganic compounds.

机构信息

Illinois Institute of Technology, Department of Mechanical, Materials and Aerospace Engineering, Chicago, Illinois 60616, USA.

Illinois Institute of Technology, Thermal Processing Technology Center, Chicago, Illinois 60616, USA.

出版信息

Sci Data. 2017 Oct 24;4:170162. doi: 10.1038/sdata.2017.162.

DOI:10.1038/sdata.2017.162
PMID:29064466
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5654376/
Abstract

The standard enthalpy of formation of a compound is the energy associated with the reaction to form the compound from its component elements. The standard enthalpy of formation is a fundamental thermodynamic property that determines its phase stability, which can be coupled with other thermodynamic data to calculate phase diagrams. Calorimetry provides the only direct method by which the standard enthalpy of formation is experimentally measured. However, the measurement is often a time and energy intensive process. We present a dataset of enthalpies of formation measured by high-temperature calorimetry. The phases measured in this dataset include intermetallic compounds with transition metal and rare-earth elements, metal borides, metal carbides, and metallic silicides. These measurements were collected from over 50 years of calorimetric experiments. The dataset contains 1,276 entries on experimental enthalpy of formation values and structural information. Most of the entries are for binary compounds but ternary and quaternary compounds are being added as they become available. The dataset also contains predictions of enthalpy of formation from first-principles calculations for comparison.

摘要

化合物的标准生成焓是指形成化合物时与组成元素相关的能量。标准生成焓是一个基本的热力学性质,决定了其相稳定性,可以与其他热力学数据结合起来计算相图。量热法是唯一能够直接测量标准生成焓的实验方法。然而,测量通常是一个耗时耗力的过程。我们提供了一个通过高温量热法测量的生成焓数据集。该数据集中测量的相包括过渡金属和稀土元素的金属间化合物、金属硼化物、金属碳化物和金属硅化物。这些测量值来自 50 多年的量热实验。该数据集包含 1276 条关于实验生成焓值和结构信息的条目。大多数条目是针对二元化合物的,但随着三元和四元化合物的出现,也在不断增加。该数据集还包含了基于第一性原理计算的生成焓预测值,以供比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd0/5654376/86685d6ad47e/sdata2017162-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd0/5654376/4168c8a010e2/sdata2017162-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd0/5654376/4da46b36f708/sdata2017162-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd0/5654376/86685d6ad47e/sdata2017162-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd0/5654376/4168c8a010e2/sdata2017162-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd0/5654376/4da46b36f708/sdata2017162-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd0/5654376/86685d6ad47e/sdata2017162-f3.jpg

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Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning.利用深度迁移学习,通过整合计算数据和实验数据来增强材料性能预测。
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