DFT的matlab源代码-ElemNet:仅从元素组成深度学习材料化学以增强材料性能预测

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DFT的matlab源代码ElemNet ElemNet是一个深层神经网络模型,仅将元素组成作为输入,并利用人工智能自动捕获基本化学成分以预测材料性能。 ElemNet可以自动学习不同元素之间的化学相互作用和相似性,这使得它甚至比传统的基于物理属性学习领域知识的机器学习模型更准确地预测训练数据集中不存在的化学系统的相图。 该存储库包含用于执行数据处理,模型训练和分析的代码,以及经过训练的模型。 如果您有大型数据集(例如OQMD),则应从头开始训练模型。 否则,对于较小的DFT计算或实验数据集,最好使用从预训练模型中进行的转移学习来训练模型,如下所示。 安装要求 重复使用这些环境的基本要求是Python 3.6.3 Jupyter环境,其中的软件包列在requirements.txt 。 某些分析需要使用,而Java需要Java JDK 1.7或更高版本。 参见[喜p文档以了解详细信息]。 源文件 培训ElemNet模型的代码以及在我们的工作中[1]产生的经过训练的模型都可以在上找到。 其他文件夹包含与为表征ElemNet而执行的不同分析相关联的脚本。 分析笔记本应该是自描述的,在其他情

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