jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Duong‐Nguyen Nguyen | Tien-Lam Pham | Viet-Cuong Nguyen | Hiori Kino | Takashi Miyake | Hieu‐Chi Dam
Source: arXiv (Cornell University) (preprint, 2020-08-20)
Field: Materials Science — Materials Chemistry

Ensemble learning reveals dissimilarity between rare-earth transition metal binary alloys with respect to the Curie temperature We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting model; multiple random subset sampling of the materials is done to generate prediction models and the corresponding contributions of the reference training materials in detail. The distribution of the predicted values for each material can be approximated by a Gaussian mixture model. The reference training materials contributed to the prediction model that accurately predicts the physical property value of a specific material, are considered to be similar to that material, or vice versa. Evaluations using synthesized data demonstrate that the proposed method can effectively measure the dissimilarity between data instances. An application of the analysis method on the data of Curie temperature (TC) of binary 3d transition metal 4f rare earth binary alloys also reveals meaningful results on the relations between the materials. The proposed method can be considered as a potential tool for obtaining a deeper understanding of the structure of data, with respect to a target property, in particular. Binary number ; Property (philosophy) ; Curie temperature ; Gaussian ; Statistical physics ; Physical property ; Materials science ; Computer science ; Mathematics ; Condensed matter physics ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Jikun Chen | Haifan Li | Jiaou Wang | Xinyou Ke | Binghui Ge | Jinhao Chen | Hongliang Dong | Yong Jiang | Nuofu Chen
Source: arXiv (Cornell University) (preprint, 2020-03-16)
Field: Materials Science — Materials Chemistry

AC-frequency switchable correlated transports in rare-earth perovskite nickelates Whilst electron correlations were previously recognized to trigger beyond conventional direct current (DC) electronic transportations (e.g. metal-to-insulator transitions, bad metal, thermistors), their respective influences to the alternation current (AC) transport are largely overlooked. Herein, we demonstrate active regulations in the electronic functionalities of d-band correlated rare-earth nickelate (ReNiO3) thin films, by simply utilizing their electronic responses to AC-frequencies (fAC). Assisted by temperature dependent near edge X-ray absorption fine structure analysis, we discovered positive temperature dependences in Coulomb viscosity of ReNiO3 that moderates their AC impedance. Distinguished crosslinking among R(Real)-fAC measured in nearby temperatures is observed that differs to conventional oxides. It enables active adjustability in correlated transports of ReNiO3, among NTCR-, TDelta- and PTCR- thermistors, via fAC from the electronic perspective without varying materials or device structures. The TDelta-fAC relationship can be further widely adjusted via Re composition and interfacial strains. The AC-frequency sensitivity discovered in ReNiO3 brings in a new freedom to regulating and switching the device working states beyond the present semiconductor technologies. It opens a new paradigm for enriching novel electronic applications catering automatic transmission or artificial intelligence in sensing temperatures and frequencies. Thermistor ; Materials science ; Perovskite (structure) ; Coulomb ; Electrical impedance ; Condensed matter physics ; Semiconductor ; Optoelectronics ; Electrical engineering ; Physics ; Electron ; Chemistry ; Crystallography Ferroelectric and Piezoelectric Materials ; Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides
Authors: Sergey Dianov | В.М. Новичков
Source: Civil Aviation High TECHNOLOGIES (article, 2020-12-31)
Field: Materials Science — Materials Chemistry

System based approach to the design of tension sensing element made of modified diamond Modern and perspective tasks of robotics with control from artificial intelligence systems require the use of small-sized measuring devices. In this case, the intensively developed quantum sensors and quantum computers have a bright prospect. Their main advantage is the ability to successfully process the data of random processes with decomposition of complex functions into simple multipliers, as well as their small size and the ability to transmit data over long distances without wires. Data transmitted over quantum communication lines cannot be copied or intercepted, which is very useful for remote control of complex technical systems. Based on the results of the analysis of probabilistic noisy data quantum computer is able to quickly develop an assessment of the technical condition of the complex system. At the same time, there is no need to go through all the possible solutions to the evaluation problem with a huge amount of input data, some of which can sometimes be undefined. The main problem in the research of quantum processes is that researchers study the processes occurring in materials, but they do not indicate the ways in which quantum sensors and quantum computers are used in practical applications. This article explains how to form a measuring transformer that will be compatible with a quantum computer. The main objective of the study was to bring the results of basic research in the field of quantum computing closer to their application in applied tasks. It is shown how quantum processes can be shifted to the field of technical measurements of physical quantities used in complex systems. In the process of obtaining the results of the study, the hypothetical deductive method and the method of ascent from the abstract to the concrete within the framework of a systematic approach to the development of elements of technical systems were used. The result is a description of the processes of designing of tension sensing element made of modified diamond. The main findings of the study include the fact that quantum sensors implemented in the form of a modified diamond crystal are well described by the theory of measuring transducers with frequency output and can be used to get data about the state of an object. Computer science ; Quantum ; Probabilistic logic ; Quantum computer ; Process (computing) ; Field (mathematics) ; Computer engineering ; Artificial intelligence ; Mathematics ; Physics ; Quantum mechanics Diamond and Carbon-based Materials Research ; Advanced Materials Characterization Techniques
Authors: Hong-Jun Ye | Zhuo Xing
Source: Journal of Physics Conference Series (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

Study of the energy band structure of xBa (Sc1/2Nb1/2)O3-(1-x)(K1/2Na1/2)NbO3 perovskite solid solution Abstract As an important branch of the research on smart materials, the piezoelectric and ferroelectric materials play an important role in the devices with functions of sensing and energy conversion of electricity, magnetism, sound, light, force, and heat, etc., and provide a strong hardware support for the development of artificial intelligence and data science. This paper studied the properties of the potassium sodium niobate (K1/2Na1/2) NbO3-based doped solid solution, calculated the xBa(Sc1/2Nb1/2)O3-(1-x)( K1/2Na1/2)NbO3 solid solution by using the First-Principles software based on quantum mechanics, and by changing the value of x, studied the energy band structure of several doping amounts. In the end, it found that Nb4d and Sc3d electrons contributed to the valence band maximum, and had a hybrid phenomenon with O2p between the energy band -7eV to and -2eV. Also, due to the doping of the Atom B, the energy band gap changed regularly. Doping ; Solid solution ; Band gap ; Ferroelectricity ; Materials science ; Electronic band structure ; Piezoelectricity ; Condensed matter physics ; Atom (system on chip) ; Optoelectronics ; Nanotechnology ; Physics ; Computer science ; Dielectric ; Composite material Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Microwave Dielectric Ceramics Synthesis
Authors: Koichi Tsuneyama | Yuki Morimoto | Shoma Kaji | Takeshi Oya
Source: Institutional Repositories DataBase (IRDB) (article, 2020-04-25)
Field: Chemistry — Spectroscopy

AIと分子病理学の新展開 A pathological diagnosis that incorporates a molecular biology technique into a conventional pathological diagnosis based on morphology is called a molecular pathological diagnosis. Immunostaining, which detects proteins derived from oncogenes and tumor suppressor genes with specific antibodies, and FISH, which uses a fluorescently labeled nucleic acid probe to examine the amplification of a target gene, are already in clinical practice. In recent years, the application of genome analysis using a next-generation sequencer to pathological diagnosis has become available, and cancer genomic medicine has been rapidly expanding and evolving. In addition, new technologies such as liquid biopsy technology that detects proteins, DNA, microRNA, methylated DNA, etc. from samples that are less invasive at the time of collection, such as blood, are being incorporated into pathological diagnosis. The application of artificial intelligence（AI）is being studied around the world to reflect the diagnosis. This article introduces the application of rapid mass spectrometry（PESI-MS）to liquid biopsy and the development of diagnostic aids for urine cytology using AI. Liquid biopsy ; Pathological ; Nucleic acid ; Cancer ; microRNA ; Tumor suppressor gene ; Biopsy ; Gene Advanced Proteomics Techniques and Applications ; Cancer Genomics and Diagnostics ; AI in cancer detection
Authors: Kohei Aso | Jens Maebe | Xuan Quy Tran | Tomokazu Yamamoto | Yoshifumi Oshima | Syo Matsumura
Source: arXiv (Cornell University) (preprint, 2020-11-06)
Field: Materials Science — Materials Chemistry

Shape-dependent local strain in gold nanorods: data-driven atomic-resolution electron microscopy analysis The local variation in inter-atomic distances, or local lattice strain often influences significantly material properties of nanoparticles. Strain measurement with ~1% precision is provided by recent atomic-resolution electron microscopy. However, the precision has been limited by noises in the experimental data. Here, we have applied one of the data-driven analyses, Gaussian process regression to predict true form of strain. The precision has been improved to be sub-percent of 0.2 % and more for detection of local strain. Rod-shaped nanoparticles have been revealed to contain characteristic lattice expansion ~+0.6 % around the subsurface cap tip area. The experimental results are reproduced by molecular dynamics simulations of the corresponding shaped atomic models. The strain peculiar to nanorods are explained in terms of curvature-dependent non-uniform surface stress due to shape anisotropy. The present results bring a hint to nanoscale engineering to optimize the strain in nanoparticles by shape control. Nanorod ; Materials science ; Anisotropy ; Curvature ; Nanoparticle ; Nanoscopic scale ; Strain (injury) ; Nanotechnology ; Atomic force microscopy ; Surface stress ; Lattice (music) ; Microscopy ; Molecular physics ; Resolution (logic) ; Chemical physics ; Optics ; Chemistry ; Physics ; Composite material ; Surface energy ; Geometry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; nanoparticles nucleation surface interactions
Authors: Thibaud Rossel | Marc Creus
Source: ChemRxiv (preprint, 2020-04-20)
Field: Chemistry — Organic Chemistry

Synthetic biology yields a dinuclear copper based antibody for the naked-eye detection of glyphosate &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;The design and construction of synthetic antibodies capable of recognizing selectively antigens is an important challenge for synthetic biology and bio-engineering. Here, we develop a step towards the construction of de novo synthetic antibodies using a new combinatorial based approach. The bioconstruct incorporates supramolecularly a self assembling inorganic complex into a protein thus creating a modular and evolvable receptor. We used a dinuclear copper based inhibitor of carbonic anhydrase to program host-guest recognition, which in the presence of glyphosate generates a selective naked-eye sensing. The construct was used to detect the pesticide in complex samples. Taken together, our results provide new opportunities for the combinatorial optimization and evolution of our synthetic antibodies. Our assay is geared and designed specifically towards the mimic of natural antibodies but with features reminiscent of both synthetic biology and inorganic chemistry. In the near future, we anticipate evolution and artificial intelligence protocols dedicated to fine tune our host for the optimal recognition of a multitude of analytes.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Synthetic biology ; Computational biology ; Modular design ; Combinatorial chemistry ; Chemistry ; Molecular recognition ; Antibody ; Analyte ; Host (biology) ; Molecular biology ; Computer science ; Biology ; Biochemistry ; Genetics ; Molecule ; Organic chemistry Click Chemistry and Applications ; Biotin and Related Studies ; Monoclonal and Polyclonal Antibodies Research
Authors: Н. Н. Орлова | А. V. Тimonina | N. N. Kolesnikov | E. V. Deviatov
Source: arXiv (Cornell University) (preprint, 2020-12-11)
Field: Materials Science — Materials Chemistry

Switching ferroelectricity in SnSe across diffusionless martensitic phase transition We experimentally investigate transport properties of a hybrid structure, which consists of a thin single crystal SnSe flake on a top of 5~$μ$m spaced Au leads. The structure initially is in highly-conductive state, while it can be switched to low-conductive one at high currents due to the Joule heating of the sample, which should be identified as $α$-$Pnma$ -- $β$-$Cmcm$ diffusionless martensitic phase transition in SnSe. For highly-conductive state, there is significant hysteresis in $dI/dV(V)$ curves at low biases, so the sample conductance depends on the sign of the applied bias change. This hysteretic behavior reflects slow relaxation due to additional polarization current in the ferroelectric SnSe phase, which we confirm by direct measurement of time-dependent relaxation curves. In contrast, we observe no noticeable relaxation or low-bias hysteresis for the quenched $β$-$Cmcm$ low-conductive phase. Thus, ferroelectric behavior can be switched on or off in transport through hybrid SnSe structure by controllable $α$-$Pnma$ -- $β$-$Cmcm$ phase transition. This result can also be important for nonvolatile memory development, e.g. phase change memory for neuromorphic computations or other applications in artificial intelligence and modern electronics. Ferroelectricity ; Condensed matter physics ; Materials science ; Hysteresis ; Phase transition ; Electrical conductor ; Martensite ; Phase (matter) ; Relaxation (psychology) ; Optoelectronics ; Physics ; Composite material ; Dielectric Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Molecular Junctions and Nanostructures
Authors: Georgios A. Tritsaris | Stephen Carr | Gabriel R. Schleder | Pavlos Protopapas | Efthimios Kaxiras
Source:  (article, 2020-11-09)
Field: Materials Science — Materials Chemistry

Towards the Computational Design of Moir\'e Assemblies Two-dimensional (2D) layered materials, demonstrating significantly different properties from their bulk counterparts, offer a materials platform with potential applications from novel information processing to communication devices. Although some single- and few-layer forms of materials such as graphene and transition metal dichalcogenides have been realized and thoroughly studied, the space of arbitrarily layered assemblies is still mostly unexplored. We introduce concepts and methods for automated materials discovery and design, and use them to investigate trends in various low-dimensional moir\'e assemblies. Specifically, we use electronic structure calculations based on tight binding with agent-based simulation to explore the spaces of two-layer molybdenum disulfide, multi-layer graphene, and model one-dimensional moir\'e assemblies. This work more generally combines physics-based and data-driven modeling to demonstrate precise control of the electronic properties of layered materials through careful choice of the constituent layers, their stacking, and relative orientation, with important implications for the development of novel devices. Stacking ; Molybdenum disulfide ; Graphene ; Moiré pattern ; Materials science ; Nanotechnology ; Layer (electronics) ; Computer science ; Physics ; Composite material Machine Learning in Materials Science
Authors: Kenichi Kato
Source: Nihon Kessho Gakkaishi (article, 2020-02-29)
Field: Materials Science — Materials Chemistry

Synchrotron Total-Scattering Measurements by a Data-Driven Approach A data-driven approach to X-ray response non-uniformity in photon-counting detectors, which is referred to as ReLiEf （Response to Light Effector）, has been developed to realize synchrotron total-scattering measurements for materials with both crystalline and non-crystalline domains. A total-scattering measurement system corrected by ReLiEf, which is called OHGI （Overlapped High-Grade Intelligencer）, can give wide-Q-range （0.1～31 Å－1） and small-Q-step （10－3 Å－1） data with an accuracy of 0.2％ by a single measurement. Synchrotron ; Scattering ; Synchrotron radiation ; Optics ; Range (aeronautics) ; Detector ; Photon ; Materials science ; Physics ; Computational physics X-ray Diffraction in Crystallography ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: Michael Schauperl | Sophie Kantonen | Lee‐Ping Wang | Michael K. Gilson
Source: ChemRxiv (preprint, 2020-05-22)
Field: Chemistry — Spectroscopy

Data-driven analysis of the number of Lennard-Jones types needed in a force field &lt;p&gt;We optimized force fields with smaller and larger sets of chemically motivated Lennard-Jones types against the experimental properties of organic liquids. Surprisingly, we obtained results as good as or better than those from much more complex typing schemes from exceedingly simple sets of LJ types; e.g. a model with only two types of hydrogen and only one type apiece for carbon, nitrogen and oxygen.&lt;/p&gt;&lt;p&gt;The results justify sharply limiting the number of parameters to be optimized in future force field development work, thus reducing the risks of overfitting and the difficulties of reaching a global optimum in the multidimensional parameter space. They thus increase our chances of arriving at well-optimized force fields that will improve predictive accuracy, with applications in biomolecular modeling and computer-aided drug design. The results also prove the feasibility and value of a rigorous, data-driven approach to advancing the science of force field development.&lt;/p&gt; Overfitting ; Force field (fiction) ; Limiting ; Field (mathematics) ; Simple (philosophy) ; Space (punctuation) ; Computer science ; Parameter space ; Work (physics) ; Value (mathematics) ; Type (biology) ; Statistical physics ; Mathematics ; Physics ; Thermodynamics ; Artificial intelligence ; Engineering ; Mechanical engineering ; Machine learning ; Statistics Analytical Chemistry and Chromatography ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Ichiro Akai | Kazunori Iwamitsu | Yasuhiko Igarashi | Masato Okada | Hiroyuki Setoyama | Toshihiro Okajima
Source: Nihon Kessho Gakkaishi (article, 2020-02-29)
Field: Materials Science — Materials Chemistry

Analysis of Extended X-ray Absorption Fine Structure by Sparse Modeling We propose a new method to extract the informations of microscopic structure from the extended X-ray absorption fine structure by the application of sparse modeling based on a simplified single-scattering approximation of photo-electron waves. This method can extract the sparse radial distribution function of the atoms located nearby the target atom and can estimate the Debye-Waller factor without any assumption of the micro-structures. Therefore, this method is expected to exhibit considerable ability in the crystallographic researches on new materials and such cooperative researches of data-driven science and crystallographic measurements are strongly expected to extend the frontiers in various research fields. X-ray ; Absorption (acoustics) ; Scattering ; Atom (system on chip) ; Computational physics ; Materials science ; Debye ; Function (biology) ; Structure factor ; X-ray absorption fine structure ; Molecular physics ; Physics ; Atomic physics ; Statistical physics ; Optics ; Computer science ; Condensed matter physics ; Quantum mechanics X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Biel Roig-Solvas | Dana H. Brooks | Lee Makowski
Source: Acta Crystallographica Section D Structural Biology (article, 2020-01-24)
Field: Materials Science — Materials Chemistry

<i>FiXR</i>: a framework to reconstruct fiber cross-sections from X-ray fiber diffraction experiments Ab initio reconstruction methods have revolutionized the capabilities of small-angle X-ray scattering (SAXS), allowing the data-driven discovery of previously unknown molecular conformations, exploiting optimization heuristics and assumptions behind the composition of globular molecules. While these methods have been successful for the analysis of small particles, their impact on fibrillar assemblies has been more limited. The micrometre-range size of these assemblies and the complex interaction of their periodicities in their scattering profiles indicate that the discovery of fibril structures from SAXS measurements requires novel approaches beyond extending existing tools for molecular discovery. In this work, it is proposed to use SAXS measurements, together with diffraction theory, to infer the electron distribution of the average cross-section of a fiber. This cross-section is modeled as a discrete electron density with continuous support, allowing representations beyond binary distributions. Additional constraints, such as non-negativity or smoothness/connectedness, can also be added to the framework. The proposed approach is tested using simulated SAXS data from amyloid β fibril models and using measured data of Tobacco mosaic virus from SAXS experiments, recovering the geometry and density of the cross-sections in all cases. The approach is further tested by analyzing SAXS data from different amyloid β fibril assemblies, with results that are in agreement with previously proposed models from cryo-EM measurements. The limitations of the proposed method, together with an analysis of the robustness of the method and the combination with different experimental sources, are also discussed. Small-angle X-ray scattering ; Scattering ; Diffraction ; Materials science ; Ab initio ; Fiber diffraction ; Statistical physics ; Biological system ; Crystallography ; Physics ; X-ray crystallography ; Chemistry ; Optics Enzyme Structure and Function ; Advanced NMR Techniques and Applications ; Protein Structure and Dynamics