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Using the tight-binding model, we investigate the valley current of partially overlapped graphene layers with the two arrangements, $\Box \,_-\,_-^-\,^-\Box$ and $\Box \,_-\,_-^-\,_-\Box$, where symbols $\Box$, $\,_-$ ($\,^-$) and $\,_-^-$ represent the current probe, the lower (upper) monolayer region, and the AB stacking bilayer region, respectively. In the arrangement $\Box \,_-\,_-^-\,^-\Box$, there is no intra-layer current path connecting both the current probes; thus, the total current must flow through the interlayer path. We measure valley current reversal (VCR) by the average of $\frac{1}{2} \sum_{\nu =\pm}(T_{\nu,-\nu}-T_{\nu,\nu})$ per lateral wave number, where $T_{\nu,\nu'}$ denotes the electron transmission rate from the left $K_{\nu'}$ valley to the right $K_{\nu}$ valley. Without the vertical electric field, the VCR is less than half in both arrangements. As the vertical field intensifies, the VCR increases to about 0.8 in the $\Box \,_-\,_-^-\,^-\Box$ arrangement, but declines in the other arrangement. We derive an analytic scattering matrix $S_\downarrow$ ($S_\uparrow$) between the monolayer $\,_-$ ($\,^-$ ) and bilayer regions for the normal incidence. These $S_\downarrow$ and $S_\uparrow$ are real symmetric unitary matrixes ($S^{-1}=S=\;^tS=S^*$) and elucidate the vertical field effects on the VCR., Comment: 14 pages, 15 figures

NIMS-OS (NIMS Orchestration System) is a Python library created to realize a closed loop of robotic experiments and artificial intelligence (AI) without human intervention for automated materials exploration. It uses various combinations of modules to operate autonomously. Each module acts as an AI for materials exploration or a controller for a robotic experiments. As AI techniques, Bayesian optimization (PHYSBO), boundless objective-free exploration (BLOX), phase diagram construction (PDC), and random exploration (RE) methods can be used. Moreover, a system called NIMS automated robotic electrochemical experiments (NAREE) is available as a set of robotic experimental equipment. Visualization tools for the results are also included, which allows users to check the optimization results in real time. Newly created modules for AI and robotic experiments can be added easily to extend the functionality of the system. In addition, we developed a GUI application to control NIMS-OS.To demonstrate the operation of NIMS-OS, we consider an automated exploration for new electrolytes. NIMS-OS is available at https://github.com/nimsos-dev/nimsos., Comment: 29 pages, 5 figures, 2 tables

Our developed algorithm, BLOX (BoundLess Objective-free eXploration), successfully found “out-of-trend” molecules potentially useful for photofunctional materials from a drug database., Materials chemists develop chemical compounds to meet often conflicting demands of industrial applications. This process may not be properly modeled by black-box optimization because the target property is not well defined in some cases. Herein, we propose a new algorithm for automated materials discovery called BoundLess Objective-free eXploration (BLOX) that uses a novel criterion based on kernel-based Stein discrepancy in the property space. Unlike other objective-free exploration methods, a boundary for the materials properties is not needed; hence, BLOX is suitable for open-ended scientific endeavors. We demonstrate the effectiveness of BLOX by finding light-absorbing molecules from a drug database. Our goal is to minimize the number of density functional theory calculations required to discover out-of-trend compounds in the intensity–wavelength property space. Using absorption spectroscopy, we experimentally verified that eight compounds identified as outstanding exhibit the expected optical properties. Our results show that BLOX is useful for chemical repurposing, and we expect this search method to have numerous applications in various scientific disciplines.

Black-box optimization has potential in numerous applications such as hyperparameter optimization in machine learning and optimization in design of experiments. Ising machines are useful for binary optimization problems because variables can be represented by a single binary variable of Ising machines. However, conventional approaches using an Ising machine cannot handle black-box optimization problems with non-binary values. To overcome this limitation, we propose an approach for integer-variable black-box optimization problems by using Ising/annealing machines and factorization machines in cooperation with three different integer-encoding methods. The performance of our approach is numerically evaluated with different encoding methods using a simple problem of calculating the energy of the hydrogen molecule in the most stable state. The proposed approach can calculate the energy using any of the integer-encoding methods. However, one-hot encoding is useful for problems with a small size., Comment: 12 pages, 5 figures

A black-box optimization algorithm such as Bayesian optimization finds extremum of an unknown function by alternating inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perform poorly due to the difficulty of acquisition function optimization. Herein, we apply quantum annealing (QA) to overcome the difficulty in the continuous black-box optimization. As QA specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution of QA has to be translated back. Our method has the following three parts: 1) Random subspace coding based on axis-parallel hyperrectangles from continuous vector to binary vector. 2) A quadratic unconstrained binary optimization (QUBO) defined by acquisition function based on nonnegative-weighted linear regression model which is solved by QA. 3) A penalization scheme to ensure that the QA solution can be translated back. It is shown in benchmark tests that its performance using D-Wave Advantage$^{\rm TM}$ quantum annealer is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up a new possibility of quantum annealing and other QUBO solvers including quantum approximate optimization algorithm (QAOA) using a gated-quantum computers, and expand its range of application to continuous-valued problems., 8 pages, 5 figures

Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of datapoints for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system., Comment: 16 pages, 13 figures

In materials science, both controllable and uncontrollable descriptors can be used to characterize materials. Examples of controllable descriptors include the composition of elements and fabrication processes; in contrast, uncontrollable descriptors are generated by experimental data characterizing particular samples, such as raw spectral data or specific gravity. In this study, we consider an experimental design to obtain a highly accurate prediction model where the uncontrollable descriptors of materials are features and its material properties are labels. In general, as uncontrollable descriptors are more closely related to material properties, predictions based on them will be more accurate. The goal of the experimental design in the present study is not the improvement of the material properties as such but the prediction of their properties. To realize this design, we select appropriate controllable descriptors for the synthesis of the candidate material that improve the prediction accuracy when the corresponding uncontrollable descriptors and material properties are added to the training data. We propose two experimental design methods, one based on Bayesian optimization and the other on uncertainty sampling. Using a polymer database in which controllable and uncontrollable descriptors, and mechanical properties are recorded, we confirm that our method can select an appropriate candidate material to train a highly accurate prediction model in which the material properties are predicted by uncontrollable descriptors. Our proposed method can be applied to materials developments where uncontrollable descriptors are more easily obtained by experiments than obtaining target material properties; it will also be useful for extracting the relationship between structure and properties of a material.

We propose a data-driven technique to estimate the spin Hamiltonian, including uncertainty, from multiple physical quantities. Using our technique, an effective model of KCu$_4$P$_3$O$_{12}$ is determined from the experimentally observed magnetic susceptibility and magnetization curves with various temperatures under high magnetic fields. An effective model, which is the quantum Heisenberg model on a zigzag chain with eight spins having $J_1= -8.54 \pm 0.51 \{\rm meV}$, $J_2 = -2.67 \pm 1.13 \{\rm meV}$, $J_3 = -3.90 \pm 0.15 \{\rm meV}$, and $J_4 = 6.24 \pm 0.95 \{\rm meV}$, describes these measured results well. These uncertainties are successfully determined by the noise estimation. The relations among the estimated magnetic interactions or physical quantities are also discussed. The obtained effective model is useful to predict hard-to-measure properties such as spin gap, spin configuration at the ground state, magnetic specific heat, and magnetic entropy., 10 pages, 8 figures

The intertube conductance $G$ of the parallel side contacted armchair nanotubes is calculated by Landauer's formula as a function of the Fermi level $E$. When the intertube difference in the dope strength is large enough, a resonant peak dominant over the others appears in the $E$-$G$ curve. The overlap length and the interlayer configuration do not influence the resonant energy. The intervalley transmission at the resonant peak works as a reverser and a filter of the valley current., version 4. In part just above Table V in version 3, there is a typo, where $|q_{\rm r}|$ is replaced by $E'/E$ by mistake. (correct) The small nonzero $I_{-\sigma,\sigma}$ being inversely proportional to $\widetilde{E}^2$ originates from the slight difference of $|q_{\rm r}|$ from 1 as is explained by Appendix D

Machine learning applications in materials science are often hampered by shortage of experimental data. Integration with legacy data from past experiments is a viable way to solve the problem, but complex calibration is often necessary to use the data obtained under different conditions. In this paper, we present a novel calibration-free strategy to enhance the performance of Bayesian optimization with preference learning. The entire learning process is solely based on pairwise comparison of quantities (i.e., higher or lower) in the same dataset, and experimental design can be done without comparing quantities in different datasets. We demonstrate that Bayesian optimization is significantly enhanced via addition of legacy data for organic molecules and inorganic solid-state materials., 10 pages, 4 figures

Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle study on designing complex thermofunctional metamaterials consisting of SiO2, SiC, and Poly(methyl methacrylate). Empirical computing time of our quantum-classical hybrid algorithm involving a factorization machine, a rigorous coupled wave analysis, and a D-Wave 2000Q quantum annealer was insensitive to the problem size, while a classical counterpart experienced rapid increase. Our method was used to design complex structures of wavelength selective radiators showing much better concordance with the thermal atmospheric transparency window in comparison to existing human-designed alternatives. Our result shows that quantum annealing provides scientists gigantic computational power that may change how materials are designed., 26pages, 5 figures

Mixing heterogeneous Li-ion conductive materials is one of potential ways to enhance the Li-ion conductivity more than that of the parent materials. However, the development of the mixtures had not exhibited significant progress because it is a formidable task to cover the vast possible composition of the parent materials using traditional ways. Here, we introduce a fashion based on machine learning to optimize the composition ratio of ternary Li3PO4-Li3BO3-Li2SO4 mixture for its Li-ion conductivity. According to our results, the optimum composition of the ternary mixture system is 25:14:61 (Li3PO4: Li3BO3: Li2SO4 in mol%), whose Li-ion conductivity is measured as 4.9 x 10E-4 S/cm at 300 {\deg}C. Our X-ray structure analysis indicates that Li-ion conductivity in the mixing systems is enhanced by virtue of the coexistence of two or more phases. Although the mechanism enhancing Li-ion conductivity is not simple, our results demonstrate the effectiveness of machine learning for the development of materials., Comment: 12 pages, 7 Figures, 3 Tables

In contrast to bone scan and computed tomography (CT), which depend on osteoblastic response to detect bone metastasis, whole-body magnetic resonance imaging (WB-MRI) may be able to directly detect viable tumors. A 75-year-old male who had progressive metastatic prostate cancer during primary androgen deprivation therapy was referred to our hospital. Although bone scan and CT showed multiple bone metastases, WB-MRI suggested nonviable bone metastasis and viable tumor of the primary lesion. Prostate needle biopsy demonstrated viable prostate cancer cells from 10 of 12 cores. In contrast, CT-guided needle biopsy from bone metastasis of the lumbar vertebra revealed no malignant cells. Based on these findings, we reasoned that viable tumor cells inducing disease progression may primarily exist in the primary lesions and not in the metastatic lesions, and combined prostate radiotherapy and systemic hormonal therapy resulted in successful clinical response and disease control. The use of WB-MRI to detect viable disease lesions may enable us to design optimal treatment strategies for patients with metastatic castration-resistant prostate cancer.

Recent advances in RNA-sequencing technology have enabled the discovery of gene fusion transcripts in the transcriptome of cancer cells. However, it remains difficult to differentiate the therapeutically targetable fusions from passenger events. We have analyzed RNA-sequencing data and DNA copy number data from 25 endometrial cancer cell lines to identify potential therapeutically targetable fusion transcripts, and have identified 124 high-confidence fusion transcripts, of which 69% are associated with gene amplifications. As targetable fusion candidates, we focused on three in-frame kinase fusion transcripts that retain a kinase domain (CPQ-PRKDC, CAPZA2-MET, and VGLL4-PRKG1). We detected only CPQ-PRKDC fusion transcript in three of 122 primary endometrial cancer tissues. Cell proliferation of the fusion-positive cell line was inhibited by knocking down the expression of wild-type PRKDC but not by blocking the CPQ-PRKDC fusion transcript expression. Quantitative real-time RT-PCR demonstrated that the expression of the CPQ-PRKDC fusion transcript was significantly lower than that of wild-type PRKDC, corresponding to a low transcript allele fraction of this fusion, based on RNA-sequencing read counts. In endometrial cancers, the CPQ-PRKDC fusion transcript may be a passenger aberration related to gene amplification. Our findings suggest that transcript allele fraction is a useful predictor to find bona-fide therapeutic-targetable fusion transcripts., 学位の種類: 博士（医学）. 報告番号: 甲第4254号. 学位記番号: 新大院博（医）甲第732号. 学位授与年月日: 平成29年3月23日, Scientific Reports 5, Article number: 18657 (2015), 新大院博（医）甲第732号

Recently, machine learning has emerged as an alternative, powerful approach for predicting quantum-mechanical properties of molecules and solids. Here, using kernel ridge regression and atomic fingerprints representing local environments of atoms, we trained a machine-learning model on a crystalline silicon system in order to directly predict the atomic forces at a wide range of temperatures. Our idea is to construct a machine-learning model using a quantum-mechanical data set taken from canonical-ensemble simulations at a higher temperature, or an upper bound of the temperature range. With our model, the force prediction errors were about 2% or smaller with respect to the corresponding force ranges, in the temperature region between 300 and 1650 K. We also verified the applicability to a larger system, ensuring the transferability with respect to system size., Comment: 20pages, 5 figures

Membrane curvature and lipid composition plays critical role in interchanging of matter and energy in cells. Peptide curvature sensors are known to activate signaling pathways, and promote molecular transport across cell membranes. Recently, the 25-mer MARCKS-ED peptide, which is derived from the effector domain of the myristoylated alanine-rich C kinase substrate protein, has been reported to selectively recognize highly curved membrane surfaces. Our previous studies indicated that the naturally occurring L-MARCKS-ED peptide could simultaneously detect both phosphatidylserine (PS) and curvature. Here, we demonstrate that D-MARCKS-ED, composed by unnatural D-amino acids, has the same activities as its enantiomer, LMARCKS-ED, as a curvature and lipid sensor. An atomistic molecular dynamics (MD) simulation suggests that D-MARCKS-ED may change from linear to a boat conformation upon binding to the membrane. Comparable enhancement of fluorescence intensity was observed between D- and L- MARCKS-ED peptides, indicating similar binding affinities. Meanwhile, circular dichroism (CD) spectra of D- and L- MARCKS-ED are almost symmetrical both in the presence and absence of liposomes. These results suggest similar behavior of artificial D- and natural L- MARCKS-ED peptides when binding to curved membranes. Our studies may contribute to further understanding of how MARCKS-ED senses membrane curvature, as well as provide a new direction to develop novel membrane curvature probes.

We investigated the crystal and magnetic structures of the spin-1/2 frustrated antiferromagnet Cu3Mo2O9 in which the spin system consists of antiferromagnetic chains and dimers. The space group at room temperature has been reported to be orthorhombic Pnma (No. 62). We infer that the space group above TN = 7.9 K is monoclinic P2_1/m (No. 11) from the observation of reflections forbidden in Pnma in x-ray powder diffraction experiments at room temperature. We determined the magnetic structure of Cu3Mo2O9 in neutron powder diffraction experiments. Magnetic moments on dimer sites lie in the ac planes. The magnitudes are 0.50 - 0.74 mu_B. Moments on chain sites may exist but the magnitudes are very small. The magnetic structure indicates that a partial disordered state is realized. We consider the origin of the magnetic structure, weak ferromagnetism, and electric polarization., Comment: 8 pages, 5 figures, 2 tables

Ryo Tamura, Ikunosuke Tsuneki, Toru Yanase Department of Obstetrics and Gynecology, Niigata City General Hospital, Niigata, Japan Abstract: We have reported good control of atypical genital bleeding when using a cyclic administration of dienogest (repeated 4-week cycles, each consisting of the administration of 2 mg/day of dienogest for 3 weeks, followed by 1 week of drug withdrawal) in patients with endometriosis. Herein, we report the effectiveness of the long-term cyclic administration (22 months) of dienogest in a case of pathological disappearance of intestinal endometriosis diagnosed by endoscopy and histology of the lower gastrointestinal tract. There is no recurrent sign after 16 months of the treatment being stopped. Atypical genital bleeding during treatment was 3–5 days a month in each cycle. Compliance was good, so we could continue the therapy. The long-term cyclic administration of dienogest in patients with intestinal endometriosis may have significant merit. Keyword: atypical genital bleeding, dysmenorrhea, drug compliance, endoscopy, histology

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing., 57pages, 15figures, to appear in "Lectures on Quantum Computing, Thermodynamics and Statistical Physics," Kinki University Series on Quantum Computing (World Scientific, 2012)

We calculate the magnetic moment caused by the persistent currents in polygonal carbon nanotube tori by the tight binding model. The polygonal CNT tori are formed by introducing heptagonal and pentagonal defects along the inner hole and outer fringe, respectively. We found a new type of large paramagnetic persistent current caused by the semi-metallic band structures of the periodic CNT junction. It is contrasted with the persistent current originated from the metallic band structure. In spite of diamagnetism of the graphite, this paramagnetism is caused by not only the magnetic flux in the inner hole (AB flux) but also the magnetic flux directly penetrating the graphite plane (direct flux). This magnetic moment is close to that calculated with only Aharonov Bohm effect where the direct flux is included in the AB flux., Comment: RevTeX, uses epsf.sty, 7 figures