Quasicrystals have emerged as the third class of solid‐state materials, distinguished from periodic crystals and amorphous solids, which have long‐range order without periodicity exhibiting rotational symmetries that are disallowed for periodic crystals in most cases. To date, more than one hundred stable quasicrystals have been reported, leading to the discovery of many new and exciting phenomena. However, the pace of the discovery of new quasicrystals has lowered in recent years, largely owing to the lack of clear guiding principles for the synthesis of new quasicrystals. Here, it is shown that the discovery of new quasicrystals can be accelerated with a simple machine‐learning workflow. With a list of the chemical compositions of known stable quasicrystals, approximant crystals, and ordinary crystals, a prediction model is trained to solve the three‐class classification task and its predictability compared to the observed phase diagrams of ternary aluminum systems is evaluated. The validation experiments strongly support the superior predictive power of machine learning, with the overall prediction accuracy of the phase prediction task reaching ≈0.728. Furthermore, analyzing the input–output relationships black‐boxed into the model, nontrivial empirical equations interpretable by humans that describe conditions necessary for stable quasicrystal formation are identified.

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In article number 2102507, Kaoru Kimura, Ryo Yoshida, and co-workers demonstrate that machine-learning algorithms can predict the chemical composition of new quasicrystals. Furthermore, analyzing the input-output relationships black-boxed into the machine-learning model, they successfully identify nontrivial empirical equations interpretable by humans that describe the conditions necessary for stable quasicrystal formation. This is the first step toward understanding the formation mechanism of quasicrystals, which has been long sought in quasicrystal research.

Quasicrystals have emerged as a new class of solid-state materials that have long-range order without periodicity, exhibiting rotational symmetries that are disallowed for periodic crystals in most cases. To date, hundreds of new quasicrystals have been found, leading to the discovery of many new and exciting phenomena. However, the pace of the discovery of new quasicrystals has slowed in recent years, largely owing to the lack of clear guiding principles for the synthesis of new quasicrystals. Here, we show that the discovery of new quasicrystals can be accelerated with a simple machine learning workflow. With a list of the chemical compositions of known quasicrystals, approximant crystals, and ordinary crystals, we trained a prediction model to solve the three-class classification task and evaluated its predictability compared to the observed phase diagrams of ternary aluminum systems. The validation experiments strongly support the superior predictive power of machine learning, with the precision and recall of the phase prediction task reaching approximately 0.793 and 0.714, respectively. Furthermore, analyzing the input--output relationships black-boxed into the model, we identified nontrivial empirical equations interpretable by humans that describe conditions necessary for quasicrystal formation.

We present a computational approach for identifying the important descriptors of the ionic conductivities of lithium solid electrolytes. Our approach discriminates the factors of both bulk and grain boundary conductivities, which have been rarely reported. The effects of the interrelated structural (e.g. grain size, phase), material (e.g. Li ratio), chemical (e.g. electronegativity, polarizability) and experimental (e.g. sintering temperature, synthesis method) properties on the bulk and grain boundary conductivities are investigated via machine learning. The data are trained using the bulk and grain boundary conductivities of Li solid conductors at room temperature. The important descriptors are elucidated by their feature importance and predictive performances, as determined by a nonlinear XGBoost algorithm: (i) the experimental descriptors of sintering conditions are significant for both bulk and grain boundary, (ii) the material descriptors of Li site occupancy and Li ratio are the prior descriptors for bulk, (iii) the density and unit cell volume are the prior structural descriptors while the polarizability and electronegativity are the prior chemical descriptors for grain boundary, (iv) the grain size provides physical insights such as the thermodynamic condition and should be considered for determining grain boundary conductance in solid polycrystalline ionic conductors.