Exploring Symbolic Regression for hypothesis testing of London-Dispersion corrections in theoretical molecular physics - Data

Dataset and text of the master thesis "Exploring Symbolic Regression for hypothesis testing of London-Dispersion corrections in theoretical molecular physics" by Gerfried Millner. The Equation Learner Version used in this thesis is available at: https://github.com/GMillner/eql-etc Abstract: "In quantum chemistry, material science, physics and other fields, modeling atoms and molec- ular systems is becoming increasingly popular over the last decades. Approaches, like the Hartree-Fock method (HF), do not include the total electronic energy as compared to more advanced ones (e.g. Coupled Cluster), which are computationally much more demanding and therefore several orders of magnitudes slower to simulate the required task. The difference of HF and post-HF methods is improved by adding the London-dispersion interaction, an attrac- tive van der Waals force. While its principle dependence on interatomic distance is well known, several improvements have been suggested in the past. In this work interpretable correlations for this correction are searched using a machine learning method called Symbolic Regression and the data input of atomic pairs moving apart from each other"本数据集及配套文本来自Gerfried Millner的硕士论文《探索符号回归用于理论分子物理领域伦敦色散校正的假设检验》。 本论文中使用的方程学习器（Equation Learner）版本可从以下地址获取：https://github.com/GMillner/eql-etc 摘要： “近数十年来，在量子化学、材料科学、物理学等领域，原子与分子系统的建模研究愈发流行。诸如哈特莱-福克方法（Hartree-Fock, HF）这类计算手段无法涵盖完整电子能量；而更先进的方法（如耦合簇方法Coupled Cluster）虽能弥补这一缺陷，但计算成本极高，完成指定模拟任务所需时长会慢数个数量级。通过添加伦敦色散相互作用——一种具有吸引力的范德瓦尔斯力，可以弥补HF方法与后HF方法之间的能量差异。尽管伦敦色散力与原子间距离的基本依赖关系已被广泛认知，但过往已有诸多改进方案被提出。 本研究采用符号回归（Symbolic Regression）这一机器学习方法，结合彼此逐渐远离的原子对数据集，旨在探寻该校正项的可解释关联式。”