First principles prediction of experimental observables

这是一个激动人心的时刻，在物质的原子理论和实验之间的界面工作。这两个领域之间的差距正在缩小，因为改进的算法和计算机可以对更大的系统进行预测，而实验学家可以对物质进行更精细的控制。现在有可能根据量子力学的基本方程，不需要外部参数，对可能被认为是现实的复杂系统进行计算。大多数第一性原理的研究结果都是对所研究物质中原子位置的预测，如果要与实验结果进行比较，那也只能与x射线或中子衍射的结果进行比较。不幸的是，在许多情况下，这样的数据不存在，或者不够准确，无法发挥作用。我的工作将确保我们的计算工具箱在面对这种情况时不会空无一物。我将开发和验证稳健、准确和最终广泛可用的技术，用于计算一些精心选择的实验观测值：核磁共振化学位移（它们作为一种分析工具在物理和生物科学中普遍使用），电子自旋共振g张量（一种识别固体顺磁缺陷的强大工具）和能量损失近边缘结构（一种在原子尺度上表征材料的无与伦比的技术）。

It is an exciting time to work at the interface between the atomistic theory of matter and experiment. The gap between the two fields is closing, as improved algorithms and computers allow predictions to be made for ever larger systems, and experimentalists achieve ever finer control over matter. It is now possible to perform calculations, based on the fundamental equations of quantum mechanics and requiring no external parameters, for complex systems which may be considered realistic. The majority of first principles studies result in predictions for the positions of atoms in the material under study, and if these are compared to experiment at all it is to x-ray or neutron diffraction results. Unfortunately, there are many situations where such data does not exist, or is not accurate enough to be useful. My work will ensure that our computational tool-box is not empty when faced with this situation. I will develop, and validate, robust, accurate and ultimately widely available techniques for the calculation of some well chosen experimental observables: Nuclear Magnetic Resonance chemical shifts (their use as an analytical tool is pervasive throughout the physical and biological sciences), Electron Spin Resonance g-tensors (a powerful tool for the identification of paramagnetic defects in solids) and Energy Loss Near Edge Structure (an unparalleled technique for materials characterisation at the atomic scale).