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Evolutionary property prediction for molecular materials

分子材料的进化性质预测

基本信息

批准号：
EP/M017257/1
负责人：
Kim Elizabeth Jelfs
金额：
$ 12.15万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FM017257%2F1
关键词：
Evolutionary property prediction molecular materials

项目摘要

In the simplest of definitions, chemistry concerns the synthesis and the properties of molecules. Supramolecular chemistry is known as "chemistry beyond the molecule", where groups of molecules assemble without forming chemical bonds. Supramolecular systems have exciting applications as sensors, molecular switches, molecular machines (such as molecules that "walk" along a track) and as catalysts that speed up other reactions. We would like to design such systems for new applications by deducing the properties of a supramolecular system from a simple chemical sketch or idea - much as an architect's sketch of a building, for example, can reliably predict its function. However, when we simply draw a molecule, we do not know what properties it will have, nor how it will assemble. Worse, in many cases we cannot be confident that the particular molecule can in fact be synthesised at all since the assembly rules in chemistry are, still, much less well developed than those in architecture. Instead, synthetic chemists use their chemical intuition to guide them as to the best experiments to try. Then, if successful in getting a product, they must characterise the material and its properties. Even in state-of-the-art labs, this is a slow process - a new molecule can take a year to prepare, let alone to characterise. Sometimes even small changes in the reaction can have a large effect on the outcomes, hence 'intuitive' design breaks down, particularly as systems become more complex. In this proposal, our aim is to provide the same computational 'blueprint' for supramolecular materials in order to allow synthetic research teams to discover new, targeted functions in a much more rapid timeframe. We will develop computer software that will allow us to predict the best molecule for a particular type of device. We aim to use our software for more efficient "sieves" that can separate molecules be size, shape or chemistry, for more efficient molecules for optoelectronic devices such as solar cells and more efficient catalysts for the petrochemical and pharmaceutical industry. The software is based on evolutionary algorithms, these are approaches that are inspired by Darwin's theory of evolution and pit candidate materials against each other as with the "survival of the fittest" in nature. Each generation of candidates is tested with simple calculations that predict their properties as a measure of their fitness. The fittest candidates are most likely to survive to the next generation, but also random mutations of their features will occur and pairs of candidates will parent new offspring with mixtures of their features - just as occurs in nature. These evolutionary approaches are extremely effective ways of exploring very complex problems where there are many variables that influence outcome. The development of this procedure specifically for molecular materials is exciting because it will allow us to direct chemists towards the best synthetic systems and our overarching goal is to show that computational modelling can be responsible for the discovery of new materials with useful new applications, rather than simply rationalising results from synthetic teams. Ultimately we hope this will allow the computational design of new materials to become reliable enough such that it is a routine precursor to synthesis in the laboratory, just as an architect's sketch is the first step to constructing a building.

用最简单的定义来说，化学涉及分子的合成和性质。超分子化学被称为“分子之外的化学”，即分子群在不形成化学键的情况下聚集。超分子系统在传感器、分子开关、分子机器（如沿着轨道“行走”的分子）和加速其他反应的催化剂等方面有着令人兴奋的应用。我们希望通过从一个简单的化学草图或想法中推断出一个超分子系统的特性来设计这样的系统，用于新的应用——就像建筑师的建筑草图可以可靠地预测其功能一样。然而，当我们简单地画一个分子时，我们不知道它将具有什么性质，也不知道它将如何组装。更糟糕的是，在许多情况下，我们不能确信特定的分子实际上完全可以被合成，因为化学中的组装规则仍然远不如建筑中的组装规则发达。相反，合成化学家用他们的化学直觉来指导他们去尝试最好的实验。然后，如果成功获得产品，他们必须描述材料及其特性。即使在最先进的实验室里，这也是一个缓慢的过程——一个新分子可能需要一年的时间来准备，更不用说表征了。有时即使是反应中的微小变化也会对结果产生巨大影响，因此“直觉”设计就会失效，特别是当系统变得更加复杂时。在这个提案中，我们的目标是为超分子材料提供相同的计算“蓝图”，以便合成研究团队在更短的时间内发现新的目标功能。我们将开发计算机软件，使我们能够预测特定类型设备的最佳分子。我们的目标是将我们的软件用于更有效的“筛”，可以根据大小、形状或化学成分分离分子，用于太阳能电池等光电器件的更有效分子，以及用于石化和制药行业的更有效催化剂。该软件基于进化算法，这些方法受到达尔文进化论的启发，并将候选材料相互竞争，就像自然界的“适者生存”一样。每一代候选人都通过简单的计算来测试，预测他们的属性，作为衡量他们适合度的标准。最适合的候选者最有可能存活到下一代，但他们的特征也会发生随机突变，成对的候选者会以他们的特征混合的新后代为父母——就像自然界发生的那样。这些进化方法是探索非常复杂的问题的非常有效的方法，因为这些问题有许多影响结果的变量。这一专门针对分子材料的程序的发展是令人兴奋的，因为它将使我们能够指导化学家走向最好的合成系统，我们的首要目标是表明计算建模可以负责发现具有有用新应用的新材料，而不是简单地将合成团队的结果合理化。最终，我们希望这将使新材料的计算设计变得足够可靠，从而成为实验室合成的常规先驱，就像建筑师的草图是建造建筑物的第一步一样。