Novel computational routes to materials discovery

理解材料在原子尺度上的行为是现代科学和技术的基础，因为大多数性质和现象最终都由原子过程的细节控制。在过去的几十年中，原子水平上的计算机模拟成为现代化学中的一个强大工具，通过进行初步预测，辅助极端条件下的研究或提供对机制的原子见解来增强实验。例如，预测行星内部或核反应堆中物质的状态，这些测量是不可能的或危险的，或者有效地精确定位稳定的结构和性质，例如试验药物或合金，减少了昂贵和耗时的实验量。计算机模拟广泛应用的主要领域之一是材料科学，研究相变和相图。相图显示了给定材料在特定条件下的性质，例如，告诉物质在特定温度和压力下是气体，液体还是固体，或者在多组分系统的情况下是特定组成。它还显示了当这些相位相互转换时，对应于相变。拥有相图的完整图像具有重要的技术重要性，并且广泛采用计算工具来实现这一点。尽管如此，使用计算机模拟的主要困难在于，原子在空间中排列的可能方式是巨大的，没有任何技术能够考虑所有这些方式，因此我们需要重要性抽样。存在过多的计算技术，然而，这些通常是特定于问题的，并且依赖于原子结构的先验知识，限制了它们的预测能力。我一直在开发一种新的计算技术，嵌套采样（NS），它从一个新的角度解决了这些挑战：它会自动生成所有相关的原子配置（所有可能变化的一个小子集），并确定它们的相对稳定性，提供完整的热力学信息，而无需任何材料的预先知识，我已经展示了NS如何以自动化的方式用于计算金属和合金的相图，我的目标是将其适用性扩展到更广泛的问题：增强晶体结构预测研究（在开发药物方面高度相关），计算光谱特性的新应用（用于气候科学和天体化学中成分的精确测量），并制定战略，以确定和提高潜在的模型（原子相互作用的数学公式）的可靠性，有利于在广泛的背景下计算研究。

Understanding the behaviour of materials on the atomic scale is fundamental to modern science and technology, because most properties and phenomena are ultimately controlled by the details of atomistic processes. During the past decades computer simulations on the atomistic level became a powerful tool in modern chemistry, augmenting experiments, by making initial predictions, aiding studies under extreme conditions or providing an atomistic insight into mechanisms. For example, predicting the state of matter in planetary interiors or in nuclear reactors where measurements are impossible or dangerous, or pinpointing stable structures and properties efficiently, such as for trial drugs or alloys, reduces the amount of expensive and time-consuming experiments.One of the major fields where computer simulations became widely used is material science, studying phase transitions and phase diagrams. A phase diagram shows the properties of a given material at specific conditions, for example, tells whether a substance is found as gas, liquid or solid at a particular temperature and pressure, or at a particular composition in case of a multicomponent system. It also shows when these phases transform into each other, corresponding to phase transitions. It is of great technological importance to have a complete picture of the phase diagram, and computational tools are widely employed to enable this. Nonetheless, the main difficulty in using computer simulations is that the number of possible ways atoms can be arranged in space is enormous, and no technique is capable of considering all of them, hence we need importance sampling. A plethora of computational techniques exist, however, these are usually problem specific and rely on prior knowledge of the atomic structure, limiting their predictive power. I have been developing a novel computational technique, nested sampling (NS), which addresses these challenges from a new perspective: it automatically generates all relevant atomic configurations (a small subset of all possible variations), and determines their relative stability, offering complete thermodynamic information without any advance knowledge of the material, except its composition.I have already shown how NS can be used to calculate the phase diagram of metals and alloys, in an automated way, and my aim is to extend its applicability to a broader range of problems: augment crystal structure prediction studies (highly relevant in developing pharmaceuticals), a novel application in calculating spectroscopic properties (for accurate measurements of composition in climate science and astrochemistry), and develop strategies to determine and improve the reliability of potential models (the mathematical formulation of atomic interactions) benefiting computational research in a wide context.