Crystal structure prediction is one of the major unsolved problems in materials science. Traditionally, this problem is formulated as a global optimization problem for which global search algorithms are combined with first principle free energy calculations to predict the ground-state crystal structure given only a material composition or a chemical system. These ab initio algorithms usually cannot exploit a large amount of implicit physicochemical rules or geometric constraints (deep knowledge) of atom configurations embodied in a large number of known crystal structures. Inspired by the deep learning enabled breakthrough in protein structure prediction, herein we propose AlphaCrystal, a crystal structure prediction algorithm that combines a deep residual neural network model that learns deep knowledge to guide predicting the atomic contact map of a target crystal material followed by reconstructing its 3D crystal structure using genetic algorithms. Based on the experiments of a selected set of benchmark crystal materials, we show that our AlphaCrystal algorithm can predict structures close to the ground truth structures. It can also speed up the crystal structure prediction process by predicting and exploiting the predicted contact map so that it has the potential to handle relatively large systems. We believe that our deep learning based ab initio crystal structure prediction method that learns from existing material structures can be used to scale up current crystal structure prediction practice. To our knowledge, AlphaCrystal is the first neural network based algorithm for crystal structure contact map prediction and the first method for directly reconstructing crystal structures from materials composition, which can be further optimized by DFT calculations.
Synthesis, Contact Printing, and Device Characterization of Ni-Catalyzed, Crystalline InAs Nanowires
InAs纳米线由于其高电子迁移率和易于形成欧姆金属接触而被积极地探索作为高性能晶体管的沟道材料。然而，非外延InAs纳米线的催化生长通常依赖于非CMOS兼容的Au胶体的使用。在这里，我们证明了成功的合成高产量的结晶InAs纳米线的高产率和可调的直径，通过使用Ni纳米粒子作为催化剂材料上的非晶SiO2衬底。纳米线显示出优异的电学性能，场效应电子迁移率约为2，700 cm 2/Vs，ION/IOFF >103。生长的InAs纳米线的均匀性和纯度通过经由接触印刷工艺在衬底上大规模组装纳米线的平行阵列来进一步证明，该接触印刷工艺实现了高性能的可印刷晶体管，能够提供5-10 mA的导通电流（~400根纳米线）。
Strained GaN Quantum-Well FETs on Single Crystal Bulk AlN Substrates
我们报告的第一个实现的分子束外延生长应变GAN量子阱场效应晶体管的单晶体AlN衬底。所制造的双异质结构FET表现出超过2 X 10^13/cm 2的二维电子气（2DEG）密度。欧姆接触的2DEG沟道形成n+ GAN MBE再生长过程中，与0.13欧姆-毫米的接触电阻。拉曼光谱使用的量子阱作为光学标记揭示了量子阱中的应变，和应变弛豫的再生长的GAN接触。65 nm长的矩形栅器件的直流漏电流驱动为2.0 A/mm，峰值非本征漏电流为250 mS/mm，小信号射频性能达到fT~120 GHz。DC和RF性能表明，体AlN衬底为应变量子阱氮化物晶体管提供了一个有吸引力的替代平台，用于未来的高电压和高功率微波应用。
Andreev-reflection spectroscopy of SrPt3P single crystals
P. N. Lebedev Physical Institute, Russian Academy of Sciences（俄罗斯科学院列别杰夫物理研究所）；CrystMat Company（CrystMat公司）；Department of Applied Science and Technology, Politecnico di Torino（都灵理工大学应用科学与技术学院）
