精确的晶体结构预测新的二维混合有机-无机矿
Nima Karimitari1, William J Baldwin2, Evan W Muller3
1Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States.
Journal of the American Chemical Society
|September 30, 2024
概括
研究人员开发了一种机器学习潜力 (MLIP) 和混合有机-无机矿 (HOIP) 的结构搜索. 这加快了对光吸收和发射应用的新材料的发现.
科学领域:
- 材料科学
- 计算化学
- 固态物理
背景情况:
- 低维混合有机无机矿 (HOIP) 对于光电子应用至关重要,因为它具有可调节的特性.
- 广的HOIP设计空间需要高效的计算工具来发现材料.
- 目前用于预测晶体结构的方法在计算上昂贵.
研究的目的:
- 为HOIP开发一个准确和高效的机器学习原子间潜力 (MLIP).
- 为预测二维HOIP晶体结构引入一种新的结构搜索算法.
- 为了实现潜在的HOIP材料的高通量选.
主要方法:
- 在86个实验报告的HOIP材料上训练了一种机器学习的原子间潜力 (MLIP).
- 使用密度函数理论 (DFT) 的基准对73种矿组成进行了MLIP验证.
- 开发了一种用于2D HOIP晶体结构预测的随机结构搜索算法.
主要成果:
- 与DFT计算相比,MLIP的准确性很高.
- 结合MLIP和结构搜索算法成功预测了14种已知的二维矿的晶体结构.
- 合成了一种含有*cis*-1,3-环二的新型HOIP材料,并根据MLIP预测进行验证.
结论:
- 开发的MLIP和结构搜索方法为HOIP发现提供了计算成本低且准确的方法.
- 这种方法显著加快了许多有机离子和无机层组合的选.
- 经过验证的方法为新HOIP的高效设计铺平了道路,以吸收和发射光.
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