使用部分监督学习对晶体的基于结构的合成性预测
在PubMed上查看摘要
概括
此摘要是机器生成的。预测材料合成是一项挑战. 使用图形卷积神经网络的新机器学习模型准确地预测了可合成性 (CLscore),改善了仅仅热力学稳定性之外的材料发现.
科学领域
- 材料科学
- 计算化学
- 机器学习
背景情况
- 预测无机材料的合成性对于加速材料的发现至关重要.
- 热力学分解稳定性是一个常见但有限的预测因素,通常产生太多的候选物质或缺失的元稳定物质.
- 材料的合成能力是一个复杂的现象,受热力学稳定性以外的因素的影响.
研究的目的
- 开发一种机器学习模型来量化无机材料合成的概率.
- 与传统热力学方法相比,提高合成性预测的准确性.
- 为合理的材料设计提供数据驱动的指标,并减少实验探索空间.
主要方法
- 实施了部分监督学习方法,使用积极和无标签 (PU) 学习.
- 使用图形卷积神经网络作为分类器来生成晶体相似度得分 (CLscore).
- 在大量实验报告的材料中训练并验证模型,包括最近的发现.
主要成果
- 该模型在来自材料项目的9356种材料的测试组中实现了87.4%的真正预测准确度.
- 对新报告的材料 (2015-2019) 的验证显示真实阳性率为86.2%.
- 通过CLScore,可以捕捉到超出E_hull能力的结构动机,其中71%的最高分数的虚拟材料已先前合成.
结论
- 开发的机器学习模型 (CLscore) 有效地量化了材料的合成能力.
- 这种数据驱动的指标显著提高了材料发现的高通量虚拟选和生成模型.
- 通过减少实验性搜索空间, CLscore 便于更合理的材料设计.
相关概念视频
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