We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce four metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and academic exams. Overall, the frameworks have an excellent performance with few weaknesses related to the training data. The use of the introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks by focusing on the performance of the singlestep model only.

In bilayer graphene, electrostatic confinement can be realized by a suitable design of top and back gate electrodes. We measure electronic transport through a bilayer graphene quantum dot, which is laterally confined by gapped regions and connected to the leads via p-n junctions. Single electron and hole occupancy is realized and charge carriers n = 1, 2, . . . 50 can be filled successively into the quantum system with charging energies exceeding 10 meV. For the lowest quantum states, we can clearly observe valley and Zeeman splittings with a spin g-factor of gs ≈ 2. In the low field-limit, the valley splitting depends linearly on the perpendicular magnetic field and is in qualitative agreement with calculations.

The hybrid halide perovskites, the very performant compounds in photovoltaic applications, possess large Seebeck coefficient and low thermal conductivity making them potentially interesting high figure of merit (ZT) materials. For this purpose one needs to tune the electrical conductivity of these semiconductors to higher values. We have studied the CH3NH3MI3 (M=Pb,Sn) samples in pristine form showing very low ZT values for both materials; however, photoinduced doping (in M=Pb) and chemical doping (in M=Sn) indicate that, by further doping optimization, ZT can be enhanced toward unity and reach the performance level of the presently most efficient thermoelectric materials.X. Mettan et al. 04.26.2015 1

We explore a network of electronic quantum valley Hall (QVH) states in the moiré crystal of minimally twisted bilayer graphene. In our transport measurements we observe Fabry-Pérot and Aharanov-Bohm oscillations which are robust in magnetic fields ranging from 0 to 8 T, in strong contrast to more conventional 2D systems where trajectories in the bulk are bent by the Lorentz force. This persistence in magnetic field and the linear spacing in density indicate that charge carriers in the bulk flow in topologically protected, one dimensional channels. With this work we demonstrate coherent electronic transport in a lattice of topologically protected states. arXiv:1802.07317v2 [cond-mat.mes-hall]

We report the fabrication of electrostatically defined nanostructures in encapsulated bilayer graphene, with leakage resistances below depletion gates as high as R ∼ 10 GΩ. This exceeds previously reported values of R = 10-100 kΩ.1-3 We attribute this improvement to the use of a graphite back gate. We realize two split gate devices which define an electronic channel on the scale of the Fermi-wavelength. A channel gate covering the gap between the split gates varies the charge carrier density in the channel. We observe device-dependent conductance quantization of ΔG = 2e/h and ΔG = 4e/h. In quantizing magnetic fields normal to the sample plane, we recover the four-fold Landau level degeneracy of bilayer graphene. Unexpected mode crossings appear at the crossover between zero magnetic field and the quantum Hall regime.