(PHYMOL) Physics, Accuracy and Machine Learning: Towards the next-generation of Molecular Potentials

(中性)分子之间的基本相互作用相对较弱，但它们决定了固体、液体和气体中的许多复杂现象。这些分子间的相互作用在界面、分子晶体、细胞，甚至星际气体云中都是至关重要的。从第一性原理计算这些相互作用并不容易，因为这些能量的小尺寸对所使用的理论和数值方法提出了极端的要求。由于各向异性(分子中的原子不是球体)、多体的非加性(整体不是其部分的总和)和量子隧道(电荷转移或离域)的微妙影响，它们通常很难准确建模(即构建一个解析的、容易计算的表示)。简单的模型忽略或平均了许多这些细微之处，但尽管这些计算简单的模型允许在长时间尺度上研究大系统，但它们这样做是以精度和预测能力为代价的。这一点在剑桥晶体数据中心进行的有机分子晶体结构预测的盲法测试中得到了最好的例证，这些测试最终证明，最可靠的分子晶体结构和自由能排名的预测是通过先进的基于理论的模型和从头计算方法(如密度泛函理论)的组合获得的，这些组合方法的性能远远优于经验模型。关注我们目标建模的现象的物理细节所产生的预测能力的增加有有形的后果：就分子晶体而言，这导致更好地理解晶体材料及其多晶型的稳定性。深刻的理解是避免一种药物以意想不到的方式转变为另一种药物时引发人道主义和金融灾难的关键，就像抗逆转录病毒药物利托纳病毒所发生的那样。另一个应用是凌日透射谱，当行星在我们和太阳之间过境时，人们可以观察到行星外大气的吸收。在这里，碰撞诱导吸收(我们将评估PHYMOL参考数据的一种高度敏感的方法)提供了关于大气压力的信息。尤其令人感兴趣的是对O2碰撞诱导吸收的测量，因为富含O2的大气的证据可以通过光合作用解释，因此可以解释系外行星上的生命。这些应用的核心是PES-势能面-需要构建为能够准确描述分子间以及分子复合体内的分子间相互作用和分子间耦合的准确数学模型。此外，PES的计算成本必须很低，以便我们能够在长时间尺度上模拟大型系统。这就是我们看到物理模型和机器学习的强大联系的地方：通过结合这两者的最佳-在某种意义上，通过结合人类学习和机器学习-我们将看到针对目标应用的分子间模型构建方面的最大进展。在这项建议中，我们试图通过将PES的关键参数映射到电子密度的属性来使PES的开发从人的角度来看是容易的。使用标准方法可以相对容易地计算出高精度的后者。这样的映射可以使PES的发展足够快，以至于可以使用分子模拟的过程进行更新。这将预示着计算机模拟从头算方法的新纪元。

The fundamental interactions between (neutral) molecules are relatively weak, but they determine much of the complex phenomena in solids, liquids, and gases. These intermolecular interactions are of paramount importance at interfaces, in molecular crystals, in cells, and even in interstellar gas clouds. These interactions are not easy to compute from first principles because the small size of these energies places extreme demands on the theoretical and numerical methods used. They are also often quite difficult to model (i.e. to construct an analytic, easily computable representation) accurately due to the subtle effects of anisotropy (atoms in a molecule are not spheres), many-body non-additivity (the whole is not the sum of its parts), and quantum tunneling (charge-transfer or delocalization). Simple models ignore, or average out many of these subtleties, but while these computationally simple models allow the study of large systems at long time-scales, they do so at the cost of accuracy and predictive power.This is best exemplified in blind tests of organic molecular crystal structure prediction conducted by the Cambridge Crystallographic Data Centre, which have conclusively demonstrated that the most reliable predictions of the structures and free energy ranking of the molecular crystals is obtained by a combination of advanced theory-based models with ab initio methods such as density functional theory, and these combined approaches vastly outperformed the empirical models. There are tangible consequences for the increase in predictive power that arises from paying attention to physical details of the phenomena we aim to model: in the case of molecular crystals this leads to a better understanding of the stability of the crystalline material and its polymorphs. A profound understanding is the key to avoid the humanitarian and financial disasters that have arisen when one drug form transforms into another in an unexpected way, as it has happened with the antiretroviral medication Ritonavir.Another application is transit transmission spectroscopy, where one observes absorption by an exoplanetary atmosphere as the planet transits between us and its sun. Here, collision-induced absorption (one of the highly sensitive ways in which we will assess reference data in PHYMOL) gives information on the atmospheric pressure. Of particular interest is the measurement of collision-induced absorption by O2, as evidence of an O2-rich atmosphere could be explained by photosynthesis, and therefore life, on the exoplanet.Central to these applications is the PES - the potential energy surface - that needs to be constructed as an accurate mathematical model that is capable of accurately describing the intermolecular interactions as well as those within the molecular complexes, and also the couplings between these. Additionally, the PES must be computationally cheap to evaluate so as to allow us to simulate large systems for long time-scales. This is where we see the strong interlinking of physical models and machine learning: by combining the best of these two - in a sense, by combining human learning with machine learning - we will see the biggest advances in intermolecular model building for targeted applications. In this proposal we seek to make the development of the PES easy in Human terms by mapping key parameters of the PES onto properties of the electronic density. The latter is relatively easily computable to a high accuracy using standard methods. Such a mapping could make PES development fast enough that they could be updated using the course of a molecular simulation. This would herald a new era in ab initio methods for computer simulations.