Building better molecular models through artificial intelligence

点击翻译按钮获取中文摘要

The vast majority of simulation studies of liquids and solutions make use of non-polarisable pair-wise potential models, or force fields, that date back to the 1990s. Their relatively simplified nature has enabled immense progress to be made in fields like chemistry, biology, engineering and material science, allowing for highly complex systems to be simulated at the molecular level. Despite their successes, however, there are increasingly clear signs that they have exceeded their expiry date, suggesting that there is some essential missing physics in non-polarisable molecular models. For example, the neglect of polarisation effects has long been recognised as the "elephant in the room" of classical models, while more complex and time-consuming polarisable models have not yet yielded the required improvements in prediction ability, particularly for mixtures and solutions. More generally, pair-wise force fields are mostly based on the concept of "atom types", whereby molecules are built by assembling together a set of interacting units based on chemical intuition. However, it has been shown that using such a rigid definition of what constitutes a "molecule" can lead to inconsistencies in property predictions. Finally, it is still unclear what is the best set of experimental data required to fit a sufficiently accurate force field.The main objective of this project is to turn this problem on its head - i.e. instead of being constrained by chemical intuition, limited experimental data and conventional approaches, we aim to use artificial intelligence (AI) methods to "teach" us how to better build molecular models. The project will address fundamental scientific questions such as: What is the best way to define a "molecule" as a set of mathematical functions that define its interactions with the surrounding environment? To which extent does the chemical connectivity of the molecule play a role in its interactions? What is the minimal set of experimental data that is required to build a force field with strong prediction performance over a wide range of thermodynamic conditions? What is the right balance between pure-liquid and mixture data in force field parametrisation? How can polarisation effects be implicitly included in the model parameters without using expensive polarisable models? In so doing, we will lay the foundation for the next generation of molecular models that can be reliably used in predictive design of molecules and materials for multiple technological applications, including: design of electrolyte systems for energy storage; design of porous materials for carbon capture; drug design and manufacture.