GW-贝瑟-萨尔皮特方程激发能的迁移学习
Transfer learning of GW-Bethe-Salpeter Equation excitation energies
AI总结：
本文通过迁移学习方法，利用预训练的图神经网络在有限的高保真数据下提升对准粒子和激发能的预测精度，扩展多体预测的化学空间范围。
摘要：
在机器学习用于电子结构计算中，持续存在的挑战是低保真数据（如DFT或TDDFT结果）与高保真数据（如多体微扰理论标签）之间的明显不平衡。我们证明迁移学习是弥合这一差距的有效途径：在DFT和TDDFT属性上预训练的图神经网络可以通过有限的qsGW和qsGW-BSE数据进行微调，以获得准粒子和激发能的准确预测。在化学多样性测试集上评估全模型和仅读出微调，我们发现预训练提高了准确性，减少了对昂贵的qsGW数据的依赖，并且即使对于比微调期间看到的分子更大或化学上不同的分子，也能减轻大规模预测异常值。我们的结果表明，多保真度迁移学习可以显著扩展多体水平预测在化学空间中的范围。
英文摘要：
A persistent challenge in machine learning for electronic-structure calculations is the sharp imbalance between abundant low-fidelity data like DFT or TDDFT results and the scarcity of high-fidelity data like many-body perturbation theory labels. We show that transfer learning provides an effective route to bridge this gap: graph neural networks pretrained on DFT and TDDFT properties can be finetuned with limited qs$GW$ and qs$GW$-BSE data to yield accurate predictions of quasiparticle and excitation energies. Assessing both full-model and readout-only finetuning across chemically diverse test sets, we find that pretraining improves accuracy, reduces reliance on costly qs$GW$ data, and mitigates large predictive outliers even for molecules larger or chemically distinct from those seen during finetuning. Our results demonstrate that multi-fidelity transfer learning can substantially extend the reach of many-body-level predictions across chemical space.
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