Novel computational routes to materials discovery

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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.