Data-driven techniques are increasingly used to replace electronic-structure
calculations of matter. In this context, a relevant question is whether machine
learning (ML) should be applied directly to predict the desired properties or
be combined explicitly with physically-grounded operations. We present an
example of an integrated modeling approach, in which a symmetry-adapted ML
model of an effective Hamiltonian is trained to reproduce electronic
excitations from a quantum-mechanical calculation. The resulting model can make
predictions for molecules that are much larger and more complex than those that
it is trained on, and allows for dramatic computational savings by indirectly
targeting the outputs of well-converged calculations while using a
parameterization corresponding to a minimal atom-centered basis. These results
emphasize the merits of intertwining data-driven techniques with physical
approximations, improving the transferability and interpretability of ML models
without affecting their accuracy and computational efficiency, and providing a
blueprint for developing ML-augmented electronic-structure methods.

数据驱动技术与物理近似相结合的例子展示了机器学习在电子结构计算中的应用，从而提高了模型的可迁移性和可解释性，节省了计算成本，并为发展机器学习增强型电子结构方法提供了蓝图。