ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
The O4-benzo[c]phenanthridine alkaloids exhibit potent antiproliferative activity against cancer cells, which is derived from their ability to inhibit of topoisomerase I and II. It has been reported that in the alkaloids a cationic quaternary ammonium atom, which results in resonance effects between ring A and B, is necessary for increased antiproliferative activity. These findings indicate the role of their substituents at ring A on inhibition of tumor cell proliferation. In the present study, we systematically assessed the cytotoxic activities of naturally occurring alkaloids and their derivatives containing various ring A substituents against two tumor cell lines, HCT-116 colon tumor cells and HL-60 promyelocytic leukemia cells. Among the cationic iminium alkaloids, which displayed more potent activity than the corresponding neutral derivatives, and the 7,8-oxygenated benzo[c]phenanthridine alkaloids, chelerythrine and NK109, exhibited stronger antiproliferative activity than the 8,9- and 9,10-oxygenated alkaloids. The activity of cationic iminium alkaloids could be correlated with the bond lengths of their ring A substituents and the electrostatic potentials of their ammonium molecules by DFT calculation.
