A5101463613- Particle physics theoretical and experimental studies
- Scientific Computing and Data Management
- Quantum Chromodynamics and Particle Interactions
- High-Energy Particle Collisions Research
- Computational Physics and Python Applications
- Research Data Management Practices
- Distributed and Parallel Computing Systems
- ZnO doping and properties
- GaN-based semiconductor devices and materials
- Anomaly Detection Techniques and Applications
- Ga2O3 and related materials
- Particle Detector Development and Performance
- Mesoporous Materials and Catalysis
- Porphyrin and Phthalocyanine Chemistry
- Chalcogenide Semiconductor Thin Films
- Software System Performance and Reliability
- Sugarcane Cultivation and Processing
- Agriculture, Land Use, Rural Development
- Oxidative Organic Chemistry Reactions
- Catalytic Processes in Materials Science
- Glass properties and applications
- Ammonia Synthesis and Nitrogen Reduction
- Image Processing and 3D Reconstruction
- Chemical Synthesis and Reactions
- Magnesium Alloys: Properties and Applications
Massachusetts Institute of Technology
2021-2024
Rice University
2017-2019
Center for Migration Studies of New York
2017
Inha University
2007
Pusan National University
2001-2002
Korea Research Institute of Chemical Technology
2000
We report on the discovery of a room-temperature ferromagnetism in Cr-doped GaN single crystals with Tc=280 K. The addition Cr into grown by flux method induces lattice constant increase due to larger atomic radius. In x-ray photoelectron spectroscopy measurement, 2p3/2 core-level exhibited spectra near 575.7 eV. This binding energy is similar reported value CrN. coercive field magnetization–magnetic (M–H) hysteresis curve at 250 K was 54 Oe. verified presence ferromagnetic transition...
A bstract Discoveries of new phenomena often involve a dedicated search for hypothetical physics signature. Recently, novel deep learning techniques have emerged anomaly detection in the absence signal prior. However, by ignoring priors, sensitivity these approaches is significantly reduced. We present strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alternative priors that capture some salient features signatures, allowing recovery even when incorrect. This approach...
To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere principles findability, accessibility, interoperability, (FAIR) for data artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide evaluate whether or not given dataset meets these principles. We demonstrate how use this FAIRness an open simulated produced CMS Collaboration at CERN Large Hadron Collider. consists Higgs boson decays quark...
In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be powerful step in the analysis pipeline for many applications. Using progressively more realistic simulated collisions at Large Hadron Collider, show approach learns underlying latent structure. With notion volume spaces, provide first time viable solution to quantifying true...
Abstract The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, improving how is shared to facilitate scientific discovery. Generalizing these research software other digital products an active area of research. Machine learning models—algorithms that have been trained on without being explicitly programmed—and more generally, artificial intelligence (AI) models, are important target this because the ever-increasing pace...
The Ge1−xMnx thin films were fabricated at x=0.25, of which the composition is close to Ge3Mn. They showed ferromagnetism up above 350K, even though their structure amorphous. Ge3Mn amorphous samples grown 200°C have n-type characteristics, while those 500°C p-type characteristics. former has a different state from latter, energetically. It suggested that short range orders Ge3Mn5 and/or Ge8Mn11 already formed in phase according growth temperature. authors also discussed relationship between...
We report on the growth of MgB2 single crystals with a large thickness about 100 μm and clear hexagonal prismatic morphology. On low-field magnetization curve M(T), superconducting transition was observed at 39 K. Using x-ray diffractometer equipped microprobe, we obtained Laue patterns crystal from piece grown investigated impurities introduced during growing process using Auger electron spectroscopy.
Bulk GaN single crystals above 4 mm in size were grown by a Na flux method. Micro-Raman scattering from bulk was performed over the temperature range 80 K to 300 K. The results obtained reveal that Raman phonon frequency decreases with increasing temperature. This dependence of optical phonons is well described an empirical relationship has proved be effective for other semiconductors. Small 667 cm -1 peaks appeared systematically on every piece GaN. We suggest lattice-disorder-induced modes...
In nucleus-nucleus collisions, the linear dependence found for elliptic flow harmonic of both positive or negative charged particles as a function event charge asymmetry is predicted by phenomenon known Chiral Magnetic Wave (CMW) due to its induced electric quadrupole moment. Here, Ach defined N+−N−N++N−, where N+ and N− are number particles, respectively. However, other scenarios also possible may provide alternative explanations experimental results. New measurements (v2) triangular (v3)...
The findable, accessible, interoperable, and reusable (FAIR) data principles serve as a framework for examining, evaluating, improving sharing to advance scientific endeavors. There is an emerging trend adapt these machine learning models—algorithms that learn from without specific coding—and, more generally, AI models, due AI’s swiftly growing impact on engineering sectors. In this paper, we propose practical definition of the FAIR models provide template program their adoption. We...
Sn-containing hydrotalcite-like compounds were prepared by three different methods such as (i) direct synthesis, (ii) ion-exchange and (iii) grafting. These catalysts proved to have framework Sn species powder X-ray diffraction (XRD) analysis UV-vis spectroscopy. The found be active selective for the liquid phase Baeyer–Villiger (BV) oxidation of admantonone using hydrogen peroxide (H2O2) an oxidant acetonitrile a solvent. Sn-hydrotalcite-like method exhibited better catalytic performance...
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into real-time experimental data processing loop to accelerate scientific discovery. The material report builds on two workshops held by Fast Science covers three main areas: across a number domains; training implementing performant resource-efficient algorithms; computing architectures, platforms, technologies deploying these...
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, improving how is shared to facilitate scientific discovery. Generalizing these research software other digital products an active area of research. Machine learning (ML) models -- algorithms that have been trained on without being explicitly programmed more generally, artificial intelligence (AI) models, are important target this because the ever-increasing pace with...
In nucleus-nucleus collisions, the linear dependence found for elliptic flow harmonic of both positive or negative charged particles as a function event charge asymmetry is predicted by phenomenon known Chiral Magnetic Wave (CMW) due to its induced electric quadrupole moment. Here, $A_{\rm ch}$ defined $\frac{N_{+}-N_{-}}{N_{+}+N_{-}}$, where $N_{+}$ and $N_{-}$ are number particles, respectively. However, other scenarios also possible may provide alternative explanations experimental...
In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be powerful step in the analysis pipeline for many applications. Using progressively more realistic simulated collisions at Large Hadron Collider, show approach learns underlying latent structure. With notion volume spaces, provide first time viable solution to quantifying true...