两个错误可以变成一个正确：一种具有化学准确性的化学发现迁移学习方法
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy
摘要：
在虚拟高通量筛选（VHTS）中，正确识别和处理具有显著多参考（MR）特征的分子和材料对于实现高数据保真度至关重要。然而，大多数VHTS是用近似的密度泛函理论（DFT），使用一个单一的功能。尽管开发了许多MR诊断，但这种诊断的单个值指示MR对化学性质预测的影响的程度还没有很好地建立。我们评估了超过10，000种过渡金属络合物（TMC）的MR诊断，并与有机分子中的那些进行比较。我们发现，只有一些MR诊断是跨这些材料空间转移。通过研究磁流变特性对化学性质的影响（即，MR效应），涉及多个势能面（即，绝热自旋分裂，$ΔE_\mathrm{H-L}$，和电离势，IP），我们观察到MR效应的抵消超过积累。在预测MR效应时，MR特征的差异比MR特征的总程度更重要。受此观察的启发，我们建立了迁移学习模型，从较低的理论水平直接预测CCSD（T）水平的绝热$ΔE_\mathrm{H-L}$和IP。通过将这些模型与不确定性量化和多级建模相结合，我们引入了一种多管齐下的策略，该策略将数据采集加速至少三倍，同时实现化学准确性（即，1 kcal/mol）。
英文摘要：
Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high throughput screening (VHTS). Nevertheless, most VHTS is carried out with approximate density functional theory (DFT) using a single functional. Despite development of numerous MR diagnostics, the extent to which a single value of such a diagnostic indicates MR effect on chemical property prediction is not well established. We evaluate MR diagnostics of over 10,000 transition metal complexes (TMCs) and compare to those in organic molecules. We reveal that only some MR diagnostics are transferable across these materials spaces. By studying the influence of MR character on chemical properties (i.e., MR effect) that involves multiple potential energy surfaces (i.e., adiabatic spin splitting, $ΔE_\mathrm{H-L}$, and ionization potential, IP), we observe that cancellation in MR effect outweighs accumulation. Differences in MR character are more important than the total degree of MR character in predicting MR effect in property prediction. Motivated by this observation, we build transfer learning models to directly predict CCSD(T)-level adiabatic $ΔE_\mathrm{H-L}$ and IP from lower levels of theory. By combining these models with uncertainty quantification and multi-level modeling, we introduce a multi-pronged strategy that accelerates data acquisition by at least a factor of three while achieving chemical accuracy (i.e., 1 kcal/mol) for robust VHTS.
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