Department of Chemistry, University of Basel, Basel, Switzerland.
J Chem Phys. 2019 Feb 14;150(6):064105. doi: 10.1063/1.5053562.
The role of response operators is well established in quantum mechanics. We investigate their use for universal quantum machine learning models of response properties in molecules. After introducing a theoretical basis, we present and discuss numerical evidence based on measuring the potential energy's response with respect to atomic displacement and to electric fields. Prediction errors for corresponding properties, atomic forces, and dipole moments improve in a systematic fashion with training set size and reach high accuracy for small training sets. Prediction of normal modes and infrared-spectra of some small molecules demonstrates the usefulness of this approach for chemistry.
响应算子的作用在量子力学中已经得到了很好的确立。我们研究了它们在分子中响应性质的通用量子机器学习模型中的应用。在介绍了理论基础之后，我们提出并讨论了基于测量势能对原子位移和电场的响应的数值证据。对于相应的性质、原子力和偶极矩的预测误差随着训练集的大小呈系统的方式改善，并在小训练集上达到高精度。对一些小分子的简正模式和红外光谱的预测证明了这种方法在化学中的有用性。
