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•Workflow material synthesis and design are proposed.•Desired functional material can be directly predicted on the basis of first principle calculations and machine learning.•The ...predicted material properties for common materials have good agreement with experimental material data.•Prediction of material combinations is achieved using the trained support vector machine.
Desired material synthesis and design can be directly predicted on the basis of first principle calculations and machine learning. Material big data is constructed based on density functional theory where every possible element combinations are considered and then used as training sets for support vector machines. The predicted material properties for common materials are successfully matched with experimental data. In addition, material combinations based on desired material properties are also able to be predicted. Thus, the proposed work flow becomes the bridge between the material database and designing materials. The approach enables efficient material mining from material big data and could potentially reveal undiscovered desired materials. This approach could also potentially enable targeted material mining from material big data, the unveiling of undiscovered desired materials, and the execution of targeted material synthesis in experiment.
Catalysis research is on the verge of experiencing a paradigm shift regarding how catalysts are designed and characterized due to the rise of catalyst informatics. The details of catalyst informatics ...are reviewed where the following three key concepts are proposed: catalyst data, catalyst data to catalyst design via data science, and catalyst platform. Additionally, progress and opportunities within catalyst informatics are explored and introduced. If the field of catalyst informatics grows in the appropriate manner, the methods and approaches taken for catalyst design would be fundamentally altered, leading towards great advancement within catalysis research.
Data in, catalyst out: The emergence of catalyst informatics is introduced with an exploration of three key concepts: catalyst data, catalyst data to design via data science, and platform. The core idea of catalyst informatics is to design catalysts from catalyst data via data science. The current state of catalyst informatics is also reviewed in an effort to address issues and offer potential solutions to obstacles faced by the community.
This paper proposes three new formal models of autonomic proximity-based federation among smart objects with wireless network connectivity and services available on the Internet. Each smart object is ...modeled as a set of ports, each of which represents an I/O interface for a function of this smart object to interoperate with some function of another smart object. This paper first proposes our first-level formal modeling of smart objects and their federation, giving also the semantics of federation defined in a Prolog-like language. Then it proposes the second-level formal modeling using graph rewriting rules for developing application frameworks using a dynamically changing single federation, and finally, proposes the third-level formal modeling based on collectively autocatalytic sets for the development of complex application scenarios in which many smart objects are involved in mutually related more than one federation.
The purpose of this study was to examine whether 4 wk of daily ingestion of milk fat globule membrane (MFGM) combined with exercise training improves physical performance—muscle strength, agility and ...muscle power—in healthy young adults. The study was designed as a randomized, double-blind, and placebo-controlled trial. Twenty healthy young adults received either an MFGM powder containing 1.6 g of fat and 160 mg of sphingomyelin or an isocaloric placebo powder daily throughout 4 wk of power or agility training. Physical performance tests and body composition measurements were conducted before and after the 4-wk intervention. Ingestion of MFGM did not affect isometric or isokinetic muscle strength, but it was associated with a greater increase in vertical jump peak power compared with placebo. There were no significant changes in body weight or lean body mass during the intervention period in either group, and no significant differences between groups. We conclude that daily MFGM supplementation combined with exercise training has the potential to improve physical performance in young adults; however, further studies with larger sample sizes should be conducted to obtain more evidence supporting achievement of improved physical performance through MFGM supplementation.
Characterizing a planet detected by microlensing is hard if the planetary signal is weak or the lens-source relative trajectory is far from caustics. However, statistical analyses of planet ...demography must include those planets to accurately determine occurrence rates. As part of a systematic modelling effort in the context of a >10-yr retrospective analysis of MOA’s survey observations to build an extended MOA statistical sample, we analyse the light curve of the planetary microlensing event MOA-2014-BLG-472. This event provides weak constraints on the physical parameters of the lens, as a result of a planetary anomaly occurring at low magnification in the light curve. We use a Bayesian analysis to estimate the properties of the planet, based on a refined Galactic model and the assumption that all Milky Way’s stars have an equal planet-hosting probability. We find that a lens consisting of a 1.9(+2.2,−1.2)M(J) giant planet orbiting a 0.31(+0.36,−0.19)Mꙩ host at a projected separation of 0.75±0.24au is consistent with the observations and is most likely, based on the Galactic priors. The lens most probably lies in the Galactic bulge, at 7.2(+0.6,−1.7)kpc from Earth. The accurate measurement of the measured planet-to-host star mass ratio will be included in the next statistical analysis of cold planet demography detected by microlensing.
Oxide-embedded bulk iron is investigated in terms of first principles calculations and data mining. Twenty-nine oxides are embedded into a vacancy site of iron where first principles calculations are ...performed and the resulting calculations are stored as a data set. A prediction of the dissolution energy of oxides within iron and the bulk modulus of oxide-embedded iron is performed using machine learning. In particular, support vector machine (SVM) and linear regression (LR) are implemented where descriptors for determining the dissolution energy and bulk modulus are revealed. With trained SVM and LR, the prediction of the dissolution energy for different oxides in iron and the inverse problem-deriving the corresponding descriptor variables from a desired bulk modulus-are achieved. The physical origin behind the chosen descriptors is also revealed where manipulating each individual descriptor within a multidimensional space allows for the prediction of the dissolution energy and bulk modulus. Thus, predictions of physical phenomena are, in principle, achievable if the appropriate descriptors are determined.
LSH (Locality Sensitive Hashing) is one of the best known methods for solving the c-approximate nearest neighbor problem in high dimensional spaces. This paper presents a variant of the LSH ...algorithm, focusing on the special case of where all points in the dataset lie on the surface of the unit hypersphere in a d-dimensional Euclidean space. The LSH scheme is based on a family of hash functions that preserves locality of points. This paper points out that when all points are constrained to lie on the surface of the unit hypersphere, there exist hash functions that partition the space more efficiently than the previously proposed methods. The design of these hash functions uses randomly rotated regular polytopes and it partitions the surface of the unit hypersphere like a Voronoi diagram. Our new scheme improves the exponent ρ, the main indicator of the performance of the LSH algorithm.
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