Ahmed Abdul-Basit Mohammed is a teaching assistant in Qatar with a MSc in physics from Cairo University. He has broad knowledge and experience in theoretical and experimental physics, including quantum mechanics, statistical physics, condensed matter physics, and astrophysics. He has over 10 years of teaching experience and multiple publications in physics journals.
Database of Topological Materials and Spin-orbit SpillageKAMAL CHOUDHARY
We present the results of a high-throughput, first principles search for topological materials based on identifying materials with band inversion induced by spin-orbit coupling. Out of the currently available 30000 materials in our database, we investigate more than 4507 non-magnetic materials having heavy atoms and low bandgaps. We compute the spillage between the spin-orbit and non-spin-orbit wave functions, resulting in more than 1699 high-spillage candidate materials. We demonstrate that in addition to Z2 topological insulators, this screening method successfully identifies many semimetals and topological crystalline insulators. Our approach is applicable to the investigation of disordered or distorted materials, because it is not based on symmetry considerations, and it can be extended to magnetic materials. After our first screening step, we use Wannier-interpolation to calculate the topological invariants and to search for band crossings in our candidate materials. We discuss some individual example materials, as well as trends throughout our dataset, that is available at JARVIS-DFT website: http://jarvis.nist.gov
Computational Discovery of Two-Dimensional Materials, Evaluation of Force-Fie...KAMAL CHOUDHARY
JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments.
The Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials.
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
The Machine-learning section of JARVIS (JARVIS-ML) consists of machine learning prediction tools, trained on JARVIS-DFT data. Some of the ML-predictions focus on energetics, heat of formation, GGA/METAGGA bandgaps, bulk and shear modulus. The ML webpage is visible to NIST employees only right now, but will be available publicly soon.
Database of Topological Materials and Spin-orbit SpillageKAMAL CHOUDHARY
We present the results of a high-throughput, first principles search for topological materials based on identifying materials with band inversion induced by spin-orbit coupling. Out of the currently available 30000 materials in our database, we investigate more than 4507 non-magnetic materials having heavy atoms and low bandgaps. We compute the spillage between the spin-orbit and non-spin-orbit wave functions, resulting in more than 1699 high-spillage candidate materials. We demonstrate that in addition to Z2 topological insulators, this screening method successfully identifies many semimetals and topological crystalline insulators. Our approach is applicable to the investigation of disordered or distorted materials, because it is not based on symmetry considerations, and it can be extended to magnetic materials. After our first screening step, we use Wannier-interpolation to calculate the topological invariants and to search for band crossings in our candidate materials. We discuss some individual example materials, as well as trends throughout our dataset, that is available at JARVIS-DFT website: http://jarvis.nist.gov
Computational Discovery of Two-Dimensional Materials, Evaluation of Force-Fie...KAMAL CHOUDHARY
JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments.
The Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials.
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
The Machine-learning section of JARVIS (JARVIS-ML) consists of machine learning prediction tools, trained on JARVIS-DFT data. Some of the ML-predictions focus on energetics, heat of formation, GGA/METAGGA bandgaps, bulk and shear modulus. The ML webpage is visible to NIST employees only right now, but will be available publicly soon.
How JSR 385 could have Saved the Mars Climate Orbiter - JFokus 2020Werner Keil
In 1999, NASA lost the $125 million Mars Climate Orbiter as it went into orbital insertion. Due to a mismatch between US customary and SI units of measurements in one of the APIs, the spacecraft came too close to the planet, passed through the upper atmosphere and disintegrated. Sadly, this hasn’t been the only instance where a mismatch between units of measurements had catastrophic consequences, but it’s certainly one of the most spectacular and expensive ones.
How could this happen? The bad news: if you use primitive types to handle quantities in your code, due to the same practice at best, you’ve codified the unit in a variable name or database field, e.g. calling it lengthInMetres. Otherwise, you’re only relying on convention, just like Lockheed Martin and NASA did.
Join this talk to learn how JSR 385 can help you avoid $125 million mistakes, how it applies the 2019 redefinition of SI base units, and discover the immeasurable world of dimensions, units and quantities.
Computational Database for 3D and 2D materials to accelerate discoveryKAMAL CHOUDHARY
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
We report on cosmological N-body
simulations which run over up to 4
supercomputers across the globe. We
achieved to run simulations on 60 to 750
cores distributed over a variety of
supercomputers. Regardless of the
network latency of 0.32 s and the
communication over 30.000 km of optical
network cable we are able to achieve up
to 92% of the performance compared to
an equal number of cores on a single
supercomputer.
With IoT it’s all about things and sensors. And when representing a temperature, for example, we normally have it as a float. But is this float in Celsius? Kelvin? This is one of the problems JSR 363 wants to solve: have all “real world” value and unit data represented in a standard way. This JSR is also very suitable for scientific applications, where data representation, conversion, and formatting are very important. In this session, you’ll see how developers as well as platform providers can leverage this JSR, coding a smart gas pump that reports its values by using Java standards. Come to meet JSR 363, Units of Measurement.
Machine Learning in Materials Science and Chemistry, USPTO, Nathan C. FreyNathan Frey, PhD
Machine learning and artificial intelligence have transformed our online experience, and for an increasing number of individuals, these fields are fundamentally changing the way we work. In this talk, I will discuss how machine learning is used in the physical sciences, particularly materials science and chemistry, and what transformative impacts we have seen or might expect to see in the future. This discussion will focus on the unique challenges (and opportunities) faced by materials and chemistry researchers applying machine learning in their work. I will present a brief introduction to machine learning for physical scientists and give examples related to synthesis, property prediction and engineering, and artificial intelligence that “reads” research articles. These examples will introduce some of the most prevalent and useful open-source software tools that drive modern machine learning applications. Two significant themes will be emphasized throughout: the careful evaluation of machine learning results and the central importance of data quality and quantity. Finally, I will provide some mundane, “human learned” speculation about the future of machine learning in physical science and recommended resources for further study.
How JSR 385 could have Saved the Mars Climate Orbiter - JFokus 2020Werner Keil
In 1999, NASA lost the $125 million Mars Climate Orbiter as it went into orbital insertion. Due to a mismatch between US customary and SI units of measurements in one of the APIs, the spacecraft came too close to the planet, passed through the upper atmosphere and disintegrated. Sadly, this hasn’t been the only instance where a mismatch between units of measurements had catastrophic consequences, but it’s certainly one of the most spectacular and expensive ones.
How could this happen? The bad news: if you use primitive types to handle quantities in your code, due to the same practice at best, you’ve codified the unit in a variable name or database field, e.g. calling it lengthInMetres. Otherwise, you’re only relying on convention, just like Lockheed Martin and NASA did.
Join this talk to learn how JSR 385 can help you avoid $125 million mistakes, how it applies the 2019 redefinition of SI base units, and discover the immeasurable world of dimensions, units and quantities.
Computational Database for 3D and 2D materials to accelerate discoveryKAMAL CHOUDHARY
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
We report on cosmological N-body
simulations which run over up to 4
supercomputers across the globe. We
achieved to run simulations on 60 to 750
cores distributed over a variety of
supercomputers. Regardless of the
network latency of 0.32 s and the
communication over 30.000 km of optical
network cable we are able to achieve up
to 92% of the performance compared to
an equal number of cores on a single
supercomputer.
With IoT it’s all about things and sensors. And when representing a temperature, for example, we normally have it as a float. But is this float in Celsius? Kelvin? This is one of the problems JSR 363 wants to solve: have all “real world” value and unit data represented in a standard way. This JSR is also very suitable for scientific applications, where data representation, conversion, and formatting are very important. In this session, you’ll see how developers as well as platform providers can leverage this JSR, coding a smart gas pump that reports its values by using Java standards. Come to meet JSR 363, Units of Measurement.
Machine Learning in Materials Science and Chemistry, USPTO, Nathan C. FreyNathan Frey, PhD
Machine learning and artificial intelligence have transformed our online experience, and for an increasing number of individuals, these fields are fundamentally changing the way we work. In this talk, I will discuss how machine learning is used in the physical sciences, particularly materials science and chemistry, and what transformative impacts we have seen or might expect to see in the future. This discussion will focus on the unique challenges (and opportunities) faced by materials and chemistry researchers applying machine learning in their work. I will present a brief introduction to machine learning for physical scientists and give examples related to synthesis, property prediction and engineering, and artificial intelligence that “reads” research articles. These examples will introduce some of the most prevalent and useful open-source software tools that drive modern machine learning applications. Two significant themes will be emphasized throughout: the careful evaluation of machine learning results and the central importance of data quality and quantity. Finally, I will provide some mundane, “human learned” speculation about the future of machine learning in physical science and recommended resources for further study.
유통산업의 주요 소식중에서 Hot한 소식을 뽑아 전달해드리고 있습니다.
이번 6호는 '글로벌 리테일 동향 (Retail선을 타고 만국유람기)' 입니다.
1. 중국온라인쇼핑몰, 시골공략에 나선다 2. IKEA가 숲을 사들이는 이유? 3. 물보다 싼 유럽의 우유값, 유럽 낙농업계 적신호
향후에도 저희가 준비한 유통 트렌드 소식지 및 IBM의 제품, 서비스 및 오퍼링 관련 정보를 받아 보실 의향이 있으시다면
페북 메시지 또는 kimyu@kr.ibm.com에 성함과 메일을 알려주시면 감사하겠습니다.
정기적으로 유통산업의 주요 소식을 전달해 드리도록 하겠습니다.
감사합니다.
Heart health is of utmost importance for an individual. There are many things which are talked about keeping heart healthy. One concern everyone may have is alcohol good for my heart? If you are also one of them, go through the presentation to get an answer to your question. Heart health is important pillar of healthy living, so keep it happy and healthy.
Efficient time-series forecasting of nuclear reactions using swarm intellige...IJECEIAES
In this research paper, we focused on the developing a secure and efficient time-series forecasting of nuclear reactions using swarm intelligence (SI) algorithm. Nuclear radioactive management and efficient time series for casting of nuclear reactions is a problem to be addressed if nuclear power is to deliver a major part of our energy consumption. This problem explains how SI processing techniques can be used to automate accurate nuclear reaction forecasting. The goal of the study was to use swarm analysis to understand patterns and reactions in the dataset while forecasting nuclear reactions using swarm intelligence. The results obtained by training the SI algorithm for longer periods of time for predicting the efficient time series events of nuclear reactions with 94.58 percent accuracy, which is higher than the deep convolution neural networks (DCNNs) 93% accuracy for all predictions, such as the number of active reactions, to see how the results can improve. Our earliest research focused on determining the best settings and preprocessing for working with a certain nuclear reaction, such as fusion and fusion task: forecasting the time series as the reactions took 0-500 ticks being trained on 300 epochs.
1. July, 06, 2016
Ahmed Abdul-Basit Mohammed, MSc
Teaching Assistant, Department of Math, Statistics, and Physics, College of
Arts and Sciences, Qatar University, Doha, Qatar
Phone: +19293398466
Email: ahmedbasit5585@gmail.com
I am highly motivated, self-confident, and ambitious with deep interest in and broad understanding
of physics and astronomy. I have gained an excellent knowledge in advanced quantum mechanics,
advanced statistical physics, condensed matter physics, group theory, advanced nuclear physics and
advanced electrodynamics. I also have broad knowledge in theoretical physics, nuclear physics,
solid-state physics, thermodynamics, statistics, numerical analysis and computer science, solar
physics, celestial mechanics, space science, galactic dynamic, practical astronomy, mathematical
astronomy and astrophysics.
Qualification
January 2015 MSc. in physics, Cairo University, Cairo, Egypt
Thesis title: Quantum Transport in Carbon Nanotubes.
My work described the Zitterbewegung (trembling motion) of electron in
Carbon Nano tubes using tight binding model.
2007/2006 Premaster in theoretical physics, Cairo University, Egypt.
June 2006 B.Sc. in Physics and Astronomy, faculty of science, Cairo University,
Cairo, Egypt, Ranked No. 1
Professional Experience
Aug 2015- July2016: Teaching Assistant: Department of Math, Statistics, and Physics at
College of Arts and Sciences, Qatar University, Doha, Qatar.
May 2007-May 2015: Teaching Assistant: Physics Department, Faculty of Science, Cairo
University, Egypt.
Sep2013-Feb2014: Physics Teacher (Part time) STEM international American school, 6th
October City, Giza, Egypt.
Sept. 2008-June 2012: Teaching Assistant (Part time), Physics Department, Higher
Institute of Engineering and Technology in Giza, Egypt.
2. July, 06, 2016
Publications and Conferences Proceedings
[1]CMS Collaboration, Search for Dijet Resonances in 7TeV pp Collisions at CMS, PHYSICAL
REVIEW LETTERS, 105, (2010) 211801.IF (7.5)
http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.105.211801
[2].CMS Collaboration, First measurement of the cross section for top-quark pair production in
proton–proton collisions at √s = 7TeV, Physics Letters B 695 (2011) 424–443.IF (6.1)
http://www.sciencedirect.com/science/article/pii/S037026931001333X
[3]Ahmed Abdel-Basit, Mohamed M. Sherif, Ahmed A. Elsoud, Quantum Transport in Carbon
Nanotubes, Thesis, January 2015.
[4]Ahmed Abdel-Basit, Kim page, GRB 080123, Swift-BAT refined analysis. Poster, 8th COSPAR
Capacity Building Workshop on Space Astrophysics with NASA and ESA Missions: Swift,
Chandra, and XMM/Newton, Alexandria, 2008.
Academic Projects (Internships) and Awards
2010: CMS (Compact Muon Solenoid) collaboration member whom published CERN-PH-
EP/2010-035,2010/10/04 & CERN-PH-EP/2010-039, 2010/10/29
2009: Advanced training in CMS experiment at Cern, Switzerland
April 2008: LOC, 1st Middle East and Africa Regional Meeting (MEARIM, 1st Congress)
January 2008: COSPAR Capacity Building Workshop, Space astrophysics with NASA and ESA
mission Swift, Chandra and XMM Newton. An advanced high energy astrophysics workshop for
young physicists and astronomers from the Middle East and North Africa
March-April, 2006: Held parallel to the International Astronomical Union Symposium number
233, entitled "Solar Activity and its Magnetic Origin"
July 2005: Summer Training and Self-Development, National Institute of Astronomical &
Geophysical Research National Institute for Standards, Egypt.
March 2007: Distinction award Prof. A. Zaki’s Award for excellence in Major Astronomy
Faculty of Science, Cairo University, Egypt.
February 2003: Distinction award SCHLUMBERGER Company for Petroleum,
http://www.slb.com/ Cairo, Egypt
3. July, 06, 2016
International Research Collaborations:
9002 – 2010 Advanced training in CMS experiment at Cern, Switzerland. CMS (Compact Muon
Solenoid) collaboration member whom published CERN-PH- EP/2010- 035, 2010/10/04 & CERN-
PH-EP/2010-039, 2010/10/29.
Research Interest:
Quantum transport in nanomaterials
Carbon nanotubes and its electrical transport
Condensed matter physics
Theoretical solid state
Mat lab programing of Quantum physics
Theoretical Nuclear physics
Astrophysics
Gamma-ray bursts (GRBs)
Statistical Physics of Quantum matter
Analysisthe multi-wavelength data of Newton stars by XSPE (An X-Ra Spectral Fitting
Package)
Professional Association:
Member of the Egyptian Association for Mathematics and Physics.
Laboratory Skills:
Friction peel Tester
Broadband Dielectric Spectrometer
Oscilloscopes
Geiger Muller counter
Beta spectrum counter
Gamma spectrum detectors
Scintillator
Special Technical Skills:
MATLAB
XSPE (An X-Ra Spectral Fitting Package)
Proficient user of Blackboard 9.1 learning management system,
Sigma plot
OriginPro 8 software
Literature search using SciFinder, Scopus, Web of Knowledge, and Web of Science, and
Literature database processing in EndNote software
WinDeltaV5.86 Software
TeachingExperiences
At Qatar University:
Experimental General Physics For Engineering I PHYS192
Experimental General Physics For Engineering II PHYS194
4. July, 06, 2016
At Cairo University:
Practical Courses
1. General Physics Lab I (Mechanics, Heat & Thermodynamics)
2. General Physics Lab II (Electricity & Magnetism)
3. General Physics Lab III (Optics, Electromagnetism & Circuits)
4. Electronic Lab
5. Solid State Lab
6. Nuclear Physics Lab
Tutorial
1. General PhysicsI (Mechanics, Heat & Thermodynamics)
2. General Physics Lab II (Electricity & Magnetism)
3. Mathematical Physics
4. Electromagnetic Physics
5. Nuclear Physics
Higher Institute of Engineering and TechnologySept. 2008-June 2012
Practical Courses
General Physics Lab I
General Physics Lab II
Tutorial
Circuits
LanguagesandComputing
Computer SkillsICDL Certificate
Mat Lab programming
Experienced with operating systems; Linux –Mandriva- Windows MS office:
Excellent
Languages: English: fluent, Arabic: Native, French: Fair, German: Fair
Personal Data
Nationality: Egyptian
Marital Status:Married
D.O.B: 5 / 5/ 1985