转移GW贝特-萨尔佩特方程激发能量的学习
Dario Baum1, Arno Förster1, Lucas Visscher1
1Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam De Boelelaan 1108 1081 HZ Amsterdam The Netherlands l.visscher@vu.nl.
Chemical science
|March 4, 2026
概括
转移学习有效地弥合了电子结构计算中的数据差距. 在低准确度数据上预训练图形神经网络可以提高使用有限的高准确度数据的预测.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 电子结构计算的机器学习面临着挑战,因为大量的低保真数据 (例如,时间依赖密度函数理论) 和稀缺的高保真数据 (例如,多体扰动理论) 之间的不平衡.
- 这种数据差异限制了机器学习模型在预测复杂分子性质方面的准确性和适用性.
研究的目的:
- 调查转移学习在克服电子结构计算中的数据稀缺问题方面的有效性.
- 使用多忠度数据开发准确的准粒子和激发能量的预测模型.
主要方法:
- 利用在密度函数理论 (DFT) 和时间依赖 DFT (TDDFT) 属性上预训练的图形神经网络.
- 精细调整了预训练模型,使用准粒子自相一致GW (qsGW) 和qsGW-贝特-萨尔佩特方程 (BSE) 计算的有限数据.
- 在各种化学数据集中评估了全模型和仅读取微调策略.
主要成果:
- 预训显著提高了准粒子和激发能量的预测的准确性.
- 转移学习减少了对昂贵的高保真性qsGW数据的依赖.
- 这种方法减轻了大型预测异常值,即使对于与微调集中的分子不相似的分子.
结论:
- 多忠度转移学习是一种有效的策略,可以在电子结构计算中弥合低忠度和高忠度数据之间的差距.
- 这种方法将高可靠性方法的预测能力扩展到更广泛的化学空间.
- 这些发现使得分子性质的计算预测更准确,更具成本效益.
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