Amorphous Materials by Design through Atomistic Simulations

The discovery and design of new technologically relevant materials is a major research goal in the physical sciences. Quantum-mechanical simulations on large supercomputers have brought the computational design of materials within reach: identifying suitable compositions, searching for stable crystal structures, guiding and inspiring experimental discoveries. However powerful, this progress has been largely limited to crystalline materials with long-range structural order and relatively small unit cells. In contrast, the amorphous (non-crystalline) state has been a long-standing challenge for predictive atomistic simulations, which has severely restricted the range of new materials to be discovered. I here propose to overcome this important challenge: by developing machine learning (ML) driven approaches for the atomic-scale modelling, optimisation, and design of multicomponent amorphous materials with desired properties. This ambitious project will leverage the power of both supervised and unsupervised ML algorithms to "learn" and navigate structural space. The first objective is to develop methodology with which to create universally applicable fitting databases for ML interatomic potentials - thereby making multicomponent amorphous materials amenable to realistic atomistic modelling, on par with how crystalline solids are treated today. The second objective is to develop a novel deep learning model, here to be used for predicting solid-state NMR shifts, but with more general implications for ML-based property prediction. Finally, the methodology will be used in computational practice and applied to key materials systems. This project will open up a new degree of realism in the structural modelling and understanding of the amorphous state, provide a wealth of openly available research data, and ultimately enable the computationally driven design of new amorphous materials.