Novel computational routes to materials discovery

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.

Academic impact: This project will achieve cross-disciplinary academic impact spanning through not only the the computational and theoretical chemistry community but through those fields that intensively use data generated by computer modelling on the atomistic level: materials science, geology, spectroscopy and environmental chemistry. The new computational technique and the protocol to generate improved computational models will be widely disseminated. The project is also expected to aid me in consolidating my membership of the computational materials science community as an independent researcher.

Economic impact: As the available computational resources are becoming more powerful, it is increasingly more important that high-throughput computational techniques are developed in order to optimise efficiency both for academic and industrial applications, as human resources are expensive and limited. The proposed methodology fulfils all necessary criteria: it can be fully automated, predictions can be made without advance knowledge, technique is not limited to a single type of material, allowing the straightforward calculation of thermodynamic properties under a wide range of conditions. In the long term, this will have a high impact in areas where a large number of screening computations have to be performed, hence automation is crucial for high efficiency and cost-effective manufacturing, such as in pharmaceuticals (screening large amount of candidate molecules while searching for crystal structures with appropriate properties) or alloy development (screening a large number of different compositions, searching for structures stable under certain conditions). Looking beyond the lifetime of this project, it is envisaged that technology transfer will allow accurate, predictive, high-throughput simulations to be carried out directly by industrial partners.

Knowledge: Many of the issues that will be investigated in this project have great fundamental interest whose importance extends beyond computational modelling. Studying the behaviour and structure of materials under a variety conditions help us better understand the interior of our planet, the chemistry of the atmosphere or the unique properties of certain elements. I expect to deliver significant contributions to the state of knowledge on these topics.

People and societal impact: The high costs of laboratory investigations and conditions where measurements are impossible or dangerous (e.g. extreme high pressure or radiation) mean that theory must come in to support experiments to produce new knowledge. During the fellowship I will use and develop new methodology that will help scientists and engineers to model materials, thus saving time and precious resources, enabling more sustainable production in the long term. I will also contribute to inform the general public of these research advances, through a series of events, including public lectures and school demonstrations.