BasisFlow: Machine learning for tailor made quantum chemistry

化学反应的计算机模拟被用来指导和解释实验，使科学家能够在发现化学新领域时探索令人兴奋的新领域。但是，计算所需的时间限制了化学系统的规模和结果的准确性。我们目前可以对大约50-100个原子系统进行计算，并正在达到我们所能做的极限，但我们需要能够考虑数百或数千个原子来进行影响更广泛社会的发展：例如，将工业化学转变为更环保的，帮助发现新的抗生素，或帮助设计可生物降解的塑料。在这个研究项目中，我们将解决如何在更大的化学系统上进行准确计算的部分谜题。我们将通过开发新的理论工具来做到这一点，这些工具比现有的替代方案更准确，产生结果更快。我们还将开发一个计算机程序，允许其他从事计算化学工作的科学家根据他们正在研究的特定应用定制他们的理论工具，这将使他们更容易跟上实验化学及其与物理、材料科学和生物学的接口的最新发现。为此，我们将使用人工智能领域的技术，使我们应对开发新的理论化学工具和技术的挑战的方式发生重大变化。通过应用与大型科技公司在自动驾驶汽车中使用的方法类似的方法，我们将能够考虑分子及其性质的大型数据集，这将意味着我们的工具将比现有方法针对数十万个分子进行优化。当代人工智能依赖于拥有大量高质量的数据，这就是为什么这个研究项目的一部分将专注于学习生成关于分子及其经历的化学数据的最佳方法，以推进计算化学。我们不仅将在我们自己的计算工具开发中构建和使用新的数据集，我们还将建立一个工作流程，允许其他科学家产生兼容的数据集，并将这项研究转移到许多其他化学领域。在这项工作中开发的工具和软件将向所有人提供，但将特别引起化学家的兴趣，无论是在大学工作的人还是在工业领域工作的人，他们使用计算化学来配合实验室发生的事情。加速他们对结果的分析将使他们能够做出更好的预测和解释，最终将实验引向正确的方向。增加计算机可以模拟的化学系统的大小，也可以重新定义我们如何使用理论方法来学习更多关于化学的知识，以及它在我们现代世界中所扮演的角色。

Computer modelling of chemical reactions is used to guide and interpret experiments, allowing scientists to explore exciting new areas of chemistry as they are discovered. But, the time taken for the calculations limits how big the chemical system can be and how accurate the results are. We can currently carry out calculations on roughly 50-100 atom systems and are reaching the limits of what we can do, but we need to able to consider hundreds or thousands of atoms to make developments that affect wider society: for example, transforming industrial chemistry to be more environmentally friendly, aiding discovery of new antibiotics, or helping to design bio-degradable plastics.In this research project we are going to address part of the puzzle of how to carry out accurate calculations on much larger chemical systems. We will do this by developing new theoretical tools that are more accurate and produce results more quickly than the existing alternatives. We will also develop a computer program that will allow other scientists working with computational chemistry to tailor their theoretical tools to the specific applications they are working on, which will make it easier for them to keep pace with the latest discoveries in experimental chemistry and its interfaces with physics, materials science and biology. To do this we will use techniques from the field of artificial intelligence to create a step change in how we approach the challenge of developing new theoretical chemistry tools and techniques. By applying similar methods as those used by large technology companies in, for example, self-driving cars, we will be able to consider large data sets of molecules and their properties, which will mean our tools will be optimised for hundreds of thousands more molecules than would be possible with existing methods.Contemporary artificial intelligence relies on having large amounts of high-quality data, which is why part of this research project will focus on learning the best ways to generate the data about molecules, and the chemistry they undergo, for the purposes of advancing computational chemistry. Not only will we construct and use new data sets in our own development of computational tools, we will also establish a workflow that allows other scientists to produce compatible data sets and move this research into many other areas of chemistry. The tools and software developed in this work will be made available to all, but will be of particular interest to chemists, both those working at universities and in industry, who use computational chemistry to accompany what happens in the lab. Accelerating their analysis of results will allow them to make better predictions and interpretations, ultimately guiding experiment in the right direction. Increasing the size of chemical system that is feasible for computers to model can also redefine how we use theoretical methods to learn more about chemistry, and the role it plays in our modern world.