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

(PHYMOL) 物理学、准确性和机器学习：迈向下一代分子潜力

• 批准号: EP/X036863/1
• 负责人: Alston Misquitta
• 金额: $ 33.8万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX036863%2F1
• 关键词: PHYMOL; Physics; Accuracy; Machine; Learning

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.

（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。这些相互作用不容易从第一原理计算，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）、多体非加性（整体不是部分之和）和量子隧穿（电荷转移或离域）的微妙影响，它们通常也很难精确建模（即构建一个分析的、易于计算的表示）。简单的模型忽略了这些微妙之处，或者说将它们平均化了，但是，尽管这些计算简单的模型允许在长时间尺度上研究大型系统，但它们这样做是以准确性和预测能力为代价的。这在剑桥晶体数据中心进行的有机分子晶体结构预测的盲测中得到了最好的例证，这些文献最终证明了分子晶体的结构和自由能等级的最可靠的预测是通过将先进的基于理论的模型与从头算方法如密度泛函理论相结合来获得的，这些结合起来的方法大大优于经验模型。关注我们要建模的现象的物理细节，可以提高预测能力，这是有实际意义的：在分子晶体的情况下，这可以更好地理解晶体材料及其多晶型物的稳定性。深刻理解是避免人道主义和金融灾难的关键，因为当一种药物以意想不到的方式转变为另一种药物时，就会出现这种情况，就像抗逆转录病毒药物利托那韦（Ritonavir）所发生的那样。另一个应用是过境透射光谱学，当行星从我们和它的太阳之间过境时，人们可以观察到行星大气层的吸收。在这里，碰撞诱导吸收（我们将评估PHYMOL中的参考数据的高度敏感的方法之一）提供了关于大气压力的信息。特别令人感兴趣的是碰撞引起的O2吸收的测量，因为富含O2的大气的证据可以用光合作用来解释，因此生命，这些应用的核心是PES -势能面-需要构建一个精确的数学模型，能够准确描述分子间的相互作用以及分子内的相互作用，复合物，以及它们之间的耦合。此外，PES必须计算便宜的评估，使我们能够模拟大系统的长时间尺度。这就是我们看到物理模型和机器学习之间的紧密联系：通过结合这两者的最佳之处-在某种意义上，通过将人类学习与机器学习相结合-我们将看到针对目标应用的分子间模型构建的最大进步。在这个建议中，我们试图通过将PES的关键参数映射到电子密度的属性上来使PES的开发在人类方面变得容易。后者是相对容易计算的高精度使用标准的方法。这样的映射可以使PES的发展足够快，他们可以使用分子模拟的过程中更新。这将预示着计算机模拟的从头算方法的新时代。