利用一个完全固定的传输框架来预测MXenes与混合终端的可解释的形成能量
Zihao Song1, Xiaobin Niu1, Haiyuan Chen1
1School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China. hychen@uestc.edu.cn.
我们开发了一个新的框架来预测具有混合表面的稳定MXenes,加速发现用于储能和催化材料的材料. 这种方法有效地选了数百万个结构,识别了像FO,FCl,FBr,FS和FSe这样的有前途的候选者.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 纳米技术 纳米技术
背景情况:
- MXenes是储能,传感器和催化剂的有希望的材料.
- 将MXene的特性与混合终端表面进行定制,对于性能提升至关重要.
- 鉴定具有多功能表面的稳定MXenes是具有挑战性的,因为实验和计算的限制.
研究的目的:
- 开发一个预测框架,用于MXenes与混合终端表面的形成能量.
- 为了克服从庞大的结构空间中预测稳定的MXenes的挑战.
- 为了促进各种应用的稳定MXenes的快速选.
主要方法:
- 使用全固定转移 (AFT) 框架与第一原则计算相结合.
- 由VIA和VIAA组的二进制元素终结的MXenes的预测形成能量.
- 利用在较小的数据集上训练的机器学习模型来预测更大的超级细胞.
主要成果:
- 实现了0.99的高平均R2,证明了模型的准确性和可转移性.
- 确定了元素对类型,终端组比率和分布,作为影响形成能量的关键因素.
- 精简了超过7亿个混合终结的MXene结构的选.
- 确定了前5个稳定的MXene类:FO,FCl,FBr,FS和FSe.
结论:
- AFT框架有效地从大型,不平衡的数据集中预测稳定的MXenes.
- 这项研究有助于快速发现功能性MXenes,用于存储等应用.
- 通过表面组工程可以实现优化的MXene稳定性和容量.
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