The past decade has witnessed rapid progress in deep learning for molecular design, owing to the availability of invertible and invariant representations for molecules such as simplified ...
The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sampling the entire ...
A team of theoretical researchers has found duality can unveil non-invertible symmetry protected topological phases, which can lead to researchers understanding more about the properties of these ...
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