University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group (MLDG) > What can we learn from toy models?

What can we learn from toy models?

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It is not generally expected that systems described by simple pair potentials show complex phase behaviour. The Jagla matter, a hard-sphere system augmented by a linear attraction term, has been used to study liquid-liquid transition, but its solid phases have been assumed to form simple close-packed crystals. In my talk I will present how we used nested sampling to discover some thermodynamically stable, but structurally exotic phases that are far from close packed. Even more striking are the similarities between the new Jagla phase and ice VI, a high-pressure polymorph of water.

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

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