Determination of crystal system and space group in the initial stages of crystal structure analysis forms a bottleneck in material science workflow that often requires manual tuning. Herein we propose a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction (XRD) patterns as a proof of concept using simulated patterns. Our tree-ensemble-based ML model works with nearly or over 90% accuracy for crystal system classification, except for triclinic cases, and with 88% accuracy for space group classification with five candidates. We also succeeded in quantifying empirical knowledge vaguely shared among experts, showing the possibility for data-driven discovery of unrecognised characteristics embedded in experimental data by using an interpretable ML approach.

Neutron scattering experiments have been performed to elucidate magnetic properties of the quasicrystal approximant Au 70 Si 17 Tb 13 , consisting of icosahedral spin clusters in a body-centered-cubic lattice. Bulk magnetic measurements performed on the single crystalline sample unambiguously confirm long-range ordering at T C = 11.6 ± 1 K. In contrast to the simple ferromagnetic response in the bulk measurements, single crystal neutron diffraction confirms a formation of intriguing non-collinear and non-coplanar magnetic order. The magnetic moment direction was found to be nearly tangential to the icosahedral cluster surface in the local mirror plane, which is quite similar to that recently found in the antiferromagnetic quasicrystal approximant Au 72 Al 14 Tb 14 . Inelastic neutron scattering on the powdered sample exhibits a very broad peak centered at ω 4 meV. The observed inelastic spectrum was explained by the crystalline-electric-field model taking account of the chemical disorder at the fractional Au/Si sites. The resulting averaged anisotropy axis for the crystalline-electric-field ground state is consistent with the ordered moment direction determined in the magnetic structure analysis, confirming that the non-coplanar magnetic order is stabilized by the local uniaxial anisotropy.

Observation of a quantum spin liquid (QSL) state is one of the most important goals in condensed-matter physics, as well as the development of new spintronic devices that support next-generation industries. The QSL in two dimensional quantum spin systems is expected to be due to geometrical magnetic frustration, and thus a kagome-based lattice is the most probable playground for QSL. Here, we report the first experimental results of the QSL state on a square-kagome quantum antiferromagnet, KCu 6 AlBiO 4 (SO 4) 5 Cl. Comprehensive experimental studies via magnetic susceptibility, magnetisation, heat capacity, muon spin relaxation (μSR), and inelastic neutron scattering (INS) measurements reveal the formation of a gapless QSL at very low temperatures close to the ground state. The QSL behavior cannot be explained fully by a frustrated Heisenberg model with nearest-neighbor exchange interactions, providing a theoretical challenge to unveil the nature of the QSL state.

In the present study, we show that time-consuming manual tuning of parameters in the Rietveld method, one of the most frequently used crystal structure analysis methods in materials science, can be automated by considering the entire trial-and-error process as a blackbox optimisation problem. The automation is successfully achieved using Bayesian optimisation, which outperforms both a human expert and an expert-system type automation despite the absence of expertise. This approach stabilises the analysis quality by eliminating human-origin variance and bias, and can be applied to various analysis methods in other areas which also suffer from similar tiresome and unsystematic manual tuning of extrinsic parameters and human-origin variance and bias.

We investigated the iron-based ladder compounds (Ba,Cs)Fe2Se3. Their parent compounds, BaFe2Se3 and CsFe2Se3, have different space groups, formal valences of Fe and magnetic structures. Electrical resistivity, specific heat, magnetic susceptibility, X-ray diffraction and powder neutron diffraction measurements were conducted to obtain temperature and composition phase diagram of this system. Block magnetism observed in BaFe2Se3 is drastically suppressed with Cs doping. In contrast, stripe magnetism observed in CsFe2Se3 is not so fragile against Ba doping. New type of magnetic structure appears in intermediate compositions, which is similar to stripe magnetism of CsFe2Se3, but inter-ladder spin configuration is different. Intermediate compounds show insulating behavior, nevertheless finite T -linear contribution in specific heat was obtained at low temperatures.

Electrical resistivity measurements have been performed on the iron-based ladder compounds Ba 1−x Cs x Fe 2 Se 3 (x = 0, 0.25, 0.65, and 1) under high pressure. A cubic anvil press was used up to 8.0 GPa, whereas further higher pressure was applied using a diamond anvil cell up to 30.0 GPa. Metallic behavior of the electrical conductivity was confirmed in the x = 0.25 and 0.65 samples for pressures greater than 11.3 and 14.4 GPa, respectively, with the low-temperature log T upturn being consistent with weak localization of 2D electrons due to random potential. At pressures higher than 23.8 GPa, three-dimensional Fermi-liquid-like behavior was observed in the latter sample. No metallic conductivity was observed in the parent compounds BaFe 2 Se 3 (x = 0) up to 30.0 GPa and CsFe 2 Se 3 (x = 1) up to 17.0 GPa. The present results indicate that the origins of the insulating ground states in the parent and intermediate compounds are intrinsically different; the former is a Mott insulator, whereas the latter is an Anderson insulator owing to the random substitution of Cs for Ba.

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