Modelling amorphous materials with machine-learning-driven interatomic potentials
Department of Engineering, University of Cambridge, CB2 1PZ, UK
Understanding the links between atomic structure, bonding, and properties in materials is a formidable task. Quantum-mechanical atomistic simulations, prominently based on density-functional theory (DFT), have played important roles in this – but they are computationally expensive, and can describe complex materials only in small model systems. Novel interatomic potentials based on machine learning (ML) have recently garnered a lot of attention in the computational materials-science community: they achieve close-to DFT accuracy but at only a fraction of the cost.
In this talk, I will argue that ML-based interatomic potentials are particularly useful for studying materials with complex structures, such as amorphous (non-crystalline) solids. I will first describe an ML potential for amorphous carbon , with a special view on what is needed to generate and validate ML potentials for the amorphous state. I will then present an application to porous and partly "graphitised" carbon structures, which are relevant for applications in batteries and supercapacitors ; this includes a new ML methodology for simulating the movement of Li ions in such materials . Finally, I will present recent work on amorphous silicon (a-Si), another prototypical non-crystalline material – where ML-driven simulations allowed us to unlock long simulation times and accurate atomistic structures , and “machine-learned” atomic energies were shown to permit a chemical interpretation, suggesting a more general approach to modelling and understanding the intricate liquid and amorphous phases of silicon .
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