Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning.

Crystals with complicated geometry are often observed with mixed chemical occupancy among Wyckoff sites, presenting a unique challenge for accurate atomic modeling. Similar systems possessing exact occupancy on all the sites can exhibit superstructural ordering, dramatically inflating the unit cell size. In this work, a crystal graph convolutional neural network (CGCNN) is used to predict optimal atomic decorations on fixed crystalline geometries. This is achieved with a site permutation search (SPS) optimization algorithm based on Monte Carlo moves combined with simulated annealing and basin-hopping techniques. Our approach relies on the evidence that, for a given chemical composition, a CGCNN estimates the correct energetic ordering of different atomic decorations, as predicted by electronic structure calculations. This provides a suitable energy landscape that can be optimized according to site occupation, allowing the prediction of chemical decoration in crystals exhibiting mixed or disordered occupancy, or superstructural ordering. Verification of the procedure is carried out on several known compounds, including the superstructurally ordered clathrate compound Rb8Ga27Sb16 and vacancy-ordered perovskite Cs2SnI6, neither of which was previously seen during the neural network training. In addition, the critical temperature of an order-disorder phase transition in solid solution CuZn is probed with our SPS routines by sampling site configuration trajectories in the canonical ensemble. This strategy provides an accurate method for determining favorable decoration in complex crystals and analyzing site occupation at unprecedented speed and scale.

ABa6Cu31Te22 (A = K, Rb, Cs) Featuring Polyanionic Copper-Telluride Frameworks with Ultralow Thermal Conductivity.

Three polyanionic tellurides, ABa6Cu31Te22 (A = K, Rb, Cs), were synthesized in salt flux. The isostructural tellurides crystallize in a new structure type, in the cubic Pa3 space group with a Wyckoff sequence of d10c2b1 and large unit cell volumes of over 5500 Å3. The structures feature a framework of [CuTe4] tetrahedra and [CuTe3] trigonal pyramids with disorder in the Cu sites. The polyanionic frameworks have large square antiprism and cuboctahedral voids where Ba and alkali metal cations are situated, forming [BaTe8] and [ATe12], respectively. The overall compositions are close to being charge balanced. The large [ATe12] cuboctahedra allowed for significant anisotropic displacement of the A cations, as observed from both single crystal X-ray diffraction and heat capacity studies. Alkali cations rattling together with Cu atom displacement and disorder leads to the dispersion of phonons, thus softening the lattice and subsequently reducing the thermal conductivity. Evaluations of the electronic band structure revealed the occurrence of a narrow bandgap together with the presence of a flat band near the valence band maximum, giving rise to the high thermopower. The Cs and Rb analogues show a slope change in the temperature dependence of electrical resistivity around room temperature, which is typical for semimetals or degenerate semiconductors. For the as-synthesized and unoptimized materials, high values of the thermoelectric figure-of-merit of ∼0.2 were observed at 623 K.

Quantum computation of silicon electronic band structure.

Development of quantum architectures during the last decade has inspired hybrid classical-quantum algorithms in physics and quantum chemistry that promise simulations of fermionic systems beyond the capability of modern classical computers, even before the era of quantum computing fully arrives. Strong research efforts have been recently made to obtain minimal depth quantum circuits which could accurately represent chemical systems. Here, we show that unprecedented methods used in quantum chemistry, designed to simulate molecules on quantum processors, can be extended to calculate properties of periodic solids. In particular, we present minimal depth circuits implementing the variational quantum eigensolver algorithm and successfully use it to compute the band structure of silicon on a quantum machine for the first time. We are convinced that the presented quantum experiments performed on cloud-based platforms will stimulate more intense studies towards scalable electronic structure computation of advanced quantum materials.