使用机器学习方法,快速准确地预测中轴性磁性异位性 (Axial Magnetic Anisotropy) ......II) 复合体
Henrique C Silva Junior1, Heloisa N S Menezes1, Glaucio B Ferreira1
1Instituto de Química, Universidade Federal Fluminense, Niterói, Rio de Janeiro 24020-141, Brazil.
Inorganic chemistry
|September 7, 2023
概括
深度神经网络 (DNN) 准确地估计单离子磁铁的磁性异构性 (D),与高级计算方法相匹配. 这种人工智能方法比传统的密度函数理论 (DFT) 计算快得多,准确得多.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 在单离子磁体中估计磁性异构性 (D) 是具有挑战性的,因为复杂的多引用电子结构.
- 对D的准确预测对于设计先进的磁性材料至关重要.
研究的目的:
- 开发一种快速而准确的方法,用于预测单离子磁铁的轴性磁性异构性 (D).
- 为了利用深度神经网络 (DNN) 进行磁性异性质计算.
主要方法:
- 一个数据库的电子数据从超过33,000 (II) 化合物被策划 (UFF1).
- 深度神经网络 (DNN) 在密度函数理论 (DFT) 数据上进行训练.
- 对DNN的性能进行了对比,与完整的活性空间自相一致场 (CASSCF) 计算进行了对比.
主要成果:
- 与CASSCF相比,DNN在预测D值方面取得了很高的准确性,R2为0.906,平均绝对误差为18.1厘米-1.
- DNN方法的准确性是DFT方法的11倍.
- DNN方法比传统的计算方法快7700倍.
结论:
- 深度神经网络为计算单离子磁铁中的磁性异性质提供了一个高度准确和高效的替代方案.
- 这种人工智能驱动的方法显示了在更大,更复杂的分子中预测异性质的潜力.
- 这些发现为加速发现新型磁性材料铺平了道路.
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