Embedded mean-field theory: chemical simulation in complex environments

密度泛函理论（DFT）现在被广泛应用于化学和相关学科的许多分支，无论是在工业界还是学术界。它以较低的计算成本提供了良好的准确性，使研究人员能够优化结构，研究机制和计算大量过程的半定量能量学。更复杂系统的现实建模-例如纳米颗粒表面的催化作用，电池中的电解质分解或生物系统中的反应性-需要准确性和广泛的核构型热力学采样的组合。目前的方法并不能做到这一点，多尺度建模已经极大地提高了我们对复杂系统建模的能力。最值得注意的是QM/MM方法-将一个区域中的量子方法与环境中的分子力学相结合-已被广泛庆祝（2013年诺贝尔化学奖），并被广泛使用。但是，有两个主要原因说明为什么必须超越QM/MM范式。首先，与一个nonpolarizable，点电荷模型的接口可能会引起寄生效应，通常只能通过增加有源子系统的大小来减轻。其次，子系统之间没有量子力学相互作用，因此例如子系统之间没有数量波动，这对于电化学过程或金属或纳米颗粒表面上的过程至关重要。我们将开发量子嵌入方案，其中使用高效的密度泛函紧束缚方法描述复杂系统，重要的子系统描述更准确的DFT处理。子系统之间的耦合将以量子力学的方式进行处理，每个子系统中都有一个混合量子态，例如，允许电子在子系统之间流动。该项目的构想将工业影响作为一个关键动机，因此我们将与丰田和巴斯夫的项目合作伙伴保持联系，以确保该方法有效地转移到工业环境中，最大限度地发挥项目的影响。

Density functional theory (DFT) is now widely used in many branches of chemistry and related disciplines, both in industry and academia. It provides a good level of accuracy at low computational cost, enabling researchers to optimize structures, study mechanism and compute semiquantitative energetics for a huge range of processes. Realistic modelling of more complex systems - such as catalysis on nanoparticle surfaces, electrolyte decomposition in batteries, or reactivity in biological systems - demands a combination of accuracy and extensive thermodynamic sampling of nuclear configurations. Current methods do not deliver this.Multiscale modelling has produced huge gains in our ability to model complex systems. Most notably the QM/MM approach - which combines a quantum method in one region with molecular mechanics in the environment - has been widely celebrated (2013 Nobel Prize for Chemistry) and is very widely used. But there are two primary reasons why it is essential to move beyond the QM/MM paradigm. First the interface with a nonpolarizable, point-charge model can give rise to spurious effects that can only typically be mitigated by increasing the size of the active subsystem. Second, there is no quantum mechanical interaction between subsystems, so for example there are no number fluctuations between the subsystems, and this is critical for processes in electrochemistry, or on metal or nanoparticle surfaces.We will develop a quantum embedding scheme in which a complex system is described using the highly efficient density functional tight binding method, with a small, important subsystem described by more accurate DFT treatments. The coupling between subsystems will be treated quantum mechanically, with a mixed quantum state in each subsystem, allowing, for example, for electrons to flow between subsystems.This project has been conceived with industrial impact as a key motivation, so we will liaise with project partners in Toyota and BASF to ensure that this method is efficiently transferred to industrial settings, maximizing impact from the project.