Talk given to the 1st multidisciplinary conference of Italian researchers in Czechia.
This is a public engagement talk about computational tools to investigate materials properties.
Computational materials design with high-throughput and machine learning methodsAnubhav Jain
Computational materials design with high-throughput and machine learning methods was presented. The presentation discussed (1) using density functional theory and high-throughput screening to rapidly generate data on many materials, (2) developing data mining approaches like matminer and matbench to extract useful information and connect to machine learning algorithms from the large volumes of data, and (3) concluded with a discussion of using these methods to accelerate materials innovation.
A perovskite solar cell is a type of solar cell which includes a perovskite structured compound, most commonly a hybrid organic-inorganic lead or tin halide-based material, as the light-harvesting active layer.
(If visualization is slow, please try downloading the file.)
Part 1 of a tutorial given in the Brazilian Physical Society meeting, ENFMC. Abstract: Density-functional theory (DFT) was developed 50 years ago, connecting fundamental quantum methods from early days of quantum mechanics to our days of computer-powered science. Today DFT is the most widely used method in electronic structure calculations. It helps moving forward materials sciences from a single atom to nanoclusters and biomolecules, connecting solid-state, quantum chemistry, atomic and molecular physics, biophysics and beyond. In this tutorial, I will try to clarify this pathway under a historical view, presenting the DFT pillars and its building blocks, namely, the Hohenberg-Kohn theorem, the Kohn-Sham scheme, the local density approximation (LDA) and generalized gradient approximation (GGA). I would like to open the black box misconception of the method, and present a more pedagogical and solid perspective on DFT.
This document discusses metal-semiconductor contacts, including Schottky and ohmic contacts. It provides energy band diagrams to illustrate how Schottky and ohmic junctions work. Schottky contacts form a rectifying barrier between a metal and lightly doped semiconductor. Ohmic contacts have a low resistance non-rectifying junction between metal and heavily doped semiconductor. The document discusses the advantages of Schottky diodes for applications such as RF mixing and solar cells due to their higher current and frequency performance compared to PN junction diodes. Ohmic contacts are used where low resistance contact is needed to allow easy flow of charge carriers.
Perovskite: introduction, classification, structure of perovskite, method to synthesis, characterization by XRD and UV- vis spectroscopy , lambert beer's law, material properties and advantage and application.
The document discusses structural, electrical, and thermoelectric properties of CrSi2 thin films. It describes how 1 μm and 0.1 μm CrSi2 thin films were prepared by RF sputtering onto quartz substrates under various conditions. Various characterization techniques were used to analyze the structural and compositional properties of the thin films, including XRD, SEM, and EDAX. Seebeck coefficient measurements of the thin films found values ranging from 30-80 μV/K depending on annealing temperature and film thickness. Overall the document examines how processing conditions affect the properties of CrSi2 thin films and their potential for thermoelectric applications.
metal organic framework-carbon capture and sequestrationVasiUddin Siddiqui
MOF is a porous crystal like a spunge having an enormous surface area and provide much more rooms for storage the gases preferentially hydrogen and carbon dioxide and work as storage for next generation fuel.
Computational materials design with high-throughput and machine learning methodsAnubhav Jain
Computational materials design with high-throughput and machine learning methods was presented. The presentation discussed (1) using density functional theory and high-throughput screening to rapidly generate data on many materials, (2) developing data mining approaches like matminer and matbench to extract useful information and connect to machine learning algorithms from the large volumes of data, and (3) concluded with a discussion of using these methods to accelerate materials innovation.
A perovskite solar cell is a type of solar cell which includes a perovskite structured compound, most commonly a hybrid organic-inorganic lead or tin halide-based material, as the light-harvesting active layer.
(If visualization is slow, please try downloading the file.)
Part 1 of a tutorial given in the Brazilian Physical Society meeting, ENFMC. Abstract: Density-functional theory (DFT) was developed 50 years ago, connecting fundamental quantum methods from early days of quantum mechanics to our days of computer-powered science. Today DFT is the most widely used method in electronic structure calculations. It helps moving forward materials sciences from a single atom to nanoclusters and biomolecules, connecting solid-state, quantum chemistry, atomic and molecular physics, biophysics and beyond. In this tutorial, I will try to clarify this pathway under a historical view, presenting the DFT pillars and its building blocks, namely, the Hohenberg-Kohn theorem, the Kohn-Sham scheme, the local density approximation (LDA) and generalized gradient approximation (GGA). I would like to open the black box misconception of the method, and present a more pedagogical and solid perspective on DFT.
This document discusses metal-semiconductor contacts, including Schottky and ohmic contacts. It provides energy band diagrams to illustrate how Schottky and ohmic junctions work. Schottky contacts form a rectifying barrier between a metal and lightly doped semiconductor. Ohmic contacts have a low resistance non-rectifying junction between metal and heavily doped semiconductor. The document discusses the advantages of Schottky diodes for applications such as RF mixing and solar cells due to their higher current and frequency performance compared to PN junction diodes. Ohmic contacts are used where low resistance contact is needed to allow easy flow of charge carriers.
Perovskite: introduction, classification, structure of perovskite, method to synthesis, characterization by XRD and UV- vis spectroscopy , lambert beer's law, material properties and advantage and application.
The document discusses structural, electrical, and thermoelectric properties of CrSi2 thin films. It describes how 1 μm and 0.1 μm CrSi2 thin films were prepared by RF sputtering onto quartz substrates under various conditions. Various characterization techniques were used to analyze the structural and compositional properties of the thin films, including XRD, SEM, and EDAX. Seebeck coefficient measurements of the thin films found values ranging from 30-80 μV/K depending on annealing temperature and film thickness. Overall the document examines how processing conditions affect the properties of CrSi2 thin films and their potential for thermoelectric applications.
metal organic framework-carbon capture and sequestrationVasiUddin Siddiqui
MOF is a porous crystal like a spunge having an enormous surface area and provide much more rooms for storage the gases preferentially hydrogen and carbon dioxide and work as storage for next generation fuel.
Super capacitors# synthesis# material# analysis#cv#gcd#fra#xrd#ftir#metail oxide#chemical # nano# METLERGY#chemical synthesis# chemical technology#petrolium# renewable energy sources# power storage
A DFT & TDDFT Study of Hybrid Halide Perovskite Quantum DotsAthanasiosKoliogiorg
Perovskite quantum dots (QDs) constitute a novel and rapidly developing field of nanotechnology with promising potential for optoelectronic applications. However, few perovskite materials for QDs and other nanostructures have been theoretically explored. In this study, we present a wide spectrum of different hybrid halide perovskite cuboid-like QDs with the general formula of FABX3 (A = (NH2)CH(NH2), B = Pb, Sn, Ge, and X = Cl, Br, I) with varying sizes below and near the Bohr exciton radius. Density functional theory (DFT) and time-dependent DFT calculations were employed to determine their structural, electronic, and optical properties. Our calculations include both stoichiometric model, proved to be close to experimental results where available, and our results reveal several materials with high optical absorption and application-suitable electronic and optical gaps. Our study highlights the potential as well as the challenges and issues regarding nanostructured halide perovskite materials, laying the background for future theoretical and experimental work.
This document discusses perovskite solar cells as a promising new material for next generation solar cells. It provides an overview of solar cell basics and the emergence of perovskites. Key features of perovskites discussed include their crystal structure, high optical absorption coefficient, excellent charge carrier transport properties, and tunable bandgap. Methods for preparing perovskite solar cells are described, along with future challenges such as improving stability and replacing toxic lead.
This document discusses the preparation of MXene, a new class of 2D transition metal carbides and nitrides. MXenes are produced through selective etching of MAX phases, which are layered ternary compounds composed of early transition metals, group A elements, and carbon and/or nitrogen. The etching process removes the A layers from the MAX phase, resulting in 2D sheets of the transition metal carbides or nitrides known as MXenes. Potential applications of MXenes include their use as electrode materials in batteries and supercapacitors due to their high electrical conductivity and capacitance.
Materials Modelling: From theory to solar cells (Lecture 1)cdtpv
This document provides an overview of a mini-module on materials modelling for solar energy applications. It introduces the lecturers and outlines the course structure, which includes lectures on modelling, interfaces, and multi-scale approaches. It also describes a literature review activity where students will present a research paper using materials modelling in photovoltaics. Recommended textbooks are provided on topics like bonding in solids, computational chemistry, and density functional theory for solids.
This document provides an overview of density functional theory (DFT). It discusses the history and development of DFT, including the Hohenberg-Kohn and Kohn-Sham theorems. The document outlines the fundamentals of DFT, including how it uses functionals of electron density rather than wavefunctions to simplify solving the many-body Schrodinger equation. It also describes the self-consistent approach in DFT calculations and provides examples of popular DFT software packages.
Silicon carbide is a compound of silicon and carbon with the chemical formula SiC. It occurs naturally as the rare mineral moissanite. Mass production of silicon carbide powder began in 1893 for use as an abrasive. Edward Acheson produced silicon carbide experimentally in 1891 and patented the process, founding the Carborundum Company. Silicon carbide exists in over 250 crystalline structures and polymorphs. It has excellent chemical and physical properties including high hardness, thermal conductivity, resistance to acids and heat. The main production method involves heating quartz sand and carbon in an electric resistance furnace above 2000°C. Silicon carbide has many applications due to its properties, including use in abrasives, automotive brake discs
The document discusses computational modeling of perovskites for photovoltaic applications. Perovskites have shown great promise for solar cells due to their excellent optoelectronic properties. Computational modeling can provide insights into perovskite properties that are difficult to obtain experimentally. While lead-based perovskites have achieved high efficiencies, their toxicity is a concern, creating interest in developing non-toxic alternatives through computational studies and materials design. Opportunities and challenges of computational modeling for understanding perovskites and designing new materials are also examined.
Dye Sensitized Solar Cells- PhD Stage 3 SeminarNarges Mohamadi
This document discusses computational modeling of organic dye sensitizers for application in solar cells. It outlines the research question of how rational in silico design can be used to develop new organic dyes to increase photocurrent density by decreasing the optical band gap and extending light absorption into the near-infrared region. The document describes computational methods used, including density functional theory and time-dependent density functional theory to optimize dye structures, calculate frontier molecular orbital energies, and simulate UV-Vis absorption spectra. Selected results are presented on modifications made to an existing dye sensitizer to lower the band gap and shift absorption spectra bathochromically into the near-infrared. The overall outcome was successful design of new dyes with improved light absorption properties for potential
This document summarizes the lead storage battery. It introduces the battery as a secondary cell that can operate as both a voltaic and electrical cell. During discharging, lead plates act as the anode and lead dioxide plates act as the cathode, with sulfuric acid as the electrolyte. Chemical reactions occur that convert lead and lead dioxide to lead sulfate. The reactions reverse during charging. Lead storage batteries are commonly used in automobiles and other applications due to their ability to provide current over repeated charge/discharge cycles.
This document summarizes research on synthesizing ternary cadmium chalcogenide quantum dots (QDs) with a gradient structure and tunable bandgaps. The QDs were loaded onto mesoporous titanium dioxide films using electrophoretic deposition to create quantum dot solar cells (QDSCs). Sequentially depositing different sized QDs with varying bandgaps improved light absorption and increased power conversion efficiency compared to mixing the QDs. Further studies are investigating the synergistic electron or energy transfer mechanisms enabling the improved performance. In conclusion, the layer-by-layer QD structure maximizes light harvesting for QDSCs across the visible spectrum.
MXenes are a class of two-dimensional inorganic compounds composed of layers of transition metal carbides, nitrides, or carbonitrides. They have many desirable properties including hardness, high melting points, oxidation resistance, and high electrical and thermal conductivity. Common synthesis methods involve selectively etching MAX precursor phases, which are hexagonal layered transition metal carbides and/or nitrides. MXenes show potential for applications as sensors due to their hydrophilic surfaces, high surface areas, and ability to host intercalants.
This presentation introduces two-dimensional materials like graphene. It defines two-dimensional materials as being only one or two atoms thick and able to conduct electrons freely within their plane. The document discusses how graphene, being a single layer of graphite, is the strongest material yet and can efficiently conduct heat and electricity. It notes graphene's potential applications in electronics, solar cells, and biomedicine. In conclusion, two-dimensional materials like graphene are seen as having great potential for developing new nanoelectronics, optoelectronics, and flexible devices.
Ferromagnetic materials have three main characteristics:
1) They become spontaneously magnetized in the absence of an external magnetic field due to parallel alignment of magnetic moments.
2) They have a magnetic ordering temperature called the Curie temperature, above which they become paramagnetic.
3) They are used in many devices like transformers, electromagnets, and computer hard drives due to their magnetic properties.
Fabrication of Organic bulk Heterojunction Solar CellFarzane Senobari
This document discusses the fabrication of organic bulk heterojunction solar cells. It begins with an introduction to organic solar cells and their advantages. It then describes the working principles of organic polymer solar cells including light absorption, exciton diffusion, charge transfer, and charge collection. The common layer stack is explained including the active layer, transport layers, electrodes, and substrates. Fabrication methods like spin coating and spray coating are outlined along with specific fabrication steps for layers. Performance parameters like open circuit voltage and power conversion efficiency are discussed. The effects of UV irradiation treatment and adding zinc oxide nanoparticles are evaluated.
The document discusses Maria Goeppert Mayer's work in physics and her contributions to the field. It then lists various branches of physics and prominent physics research institutes in India, providing details on the research areas of some key institutes.
This document summarizes a presentation about using machine learning for computational chemistry. It discusses how machine learning and computational chemistry are deeply connected, with machine learning serving as a new tool for computational chemistry. The presentation outlines how machine learning can help accelerate drug discovery and materials design for applications in health and sustainability by generating new molecules and predicting chemical reactions.
Dynamic Homogenisation of randomly irregular viscoelastic metamaterialsUniversity of Glasgow
An analytical framework is developed for investigating the effect of viscoelasticity on irregular hexagonal lattices. At room temperature, many polymers are found to be near their glass temperature. Elastic moduli of honeycombs made of such materials are not constant, but changes in the time or frequency domain. Thus consideration of viscoelastic properties is essential for such honeycombs. Irregularity in lattice structures being inevitable from a practical point of view, analysis of the compound effect considering both irregularity and viscoelasticity is crucial for such structural forms. On the basis of a mechanics-based bottom-up approach, computationally efficient closed-form formulae are derived in the frequency domain. The spatially correlated structural and material attributes are obtained based on Karhunen-Lo\`{e}ve expansion, which is integrated with the developed analytical approach to quantify the viscoelastic effect for irregular lattices. Consideration of such spatially correlated behaviour can simulate the practical stochastic system more closely. Two Young's moduli and shear modulus are found to be dependent on the viscoelastic parameters, while the two in-plane Poisson's ratios are found to be independent of viscoelastic parameters. Results are presented in both deterministic and stochastic regime, wherein it is observed that the elastic moduli are significantly amplified in the frequency domain. The response bounds are quantified considering two different forms of irregularity, randomly inhomogeneous irregularity and randomly homogeneous irregularity. The computationally efficient analytical approach presented in this study can be quite attractive for practical purposes to analyse and design lattices with predominantly viscoelastic behaviour along with consideration of structural and material irregularity.
Super capacitors# synthesis# material# analysis#cv#gcd#fra#xrd#ftir#metail oxide#chemical # nano# METLERGY#chemical synthesis# chemical technology#petrolium# renewable energy sources# power storage
A DFT & TDDFT Study of Hybrid Halide Perovskite Quantum DotsAthanasiosKoliogiorg
Perovskite quantum dots (QDs) constitute a novel and rapidly developing field of nanotechnology with promising potential for optoelectronic applications. However, few perovskite materials for QDs and other nanostructures have been theoretically explored. In this study, we present a wide spectrum of different hybrid halide perovskite cuboid-like QDs with the general formula of FABX3 (A = (NH2)CH(NH2), B = Pb, Sn, Ge, and X = Cl, Br, I) with varying sizes below and near the Bohr exciton radius. Density functional theory (DFT) and time-dependent DFT calculations were employed to determine their structural, electronic, and optical properties. Our calculations include both stoichiometric model, proved to be close to experimental results where available, and our results reveal several materials with high optical absorption and application-suitable electronic and optical gaps. Our study highlights the potential as well as the challenges and issues regarding nanostructured halide perovskite materials, laying the background for future theoretical and experimental work.
This document discusses perovskite solar cells as a promising new material for next generation solar cells. It provides an overview of solar cell basics and the emergence of perovskites. Key features of perovskites discussed include their crystal structure, high optical absorption coefficient, excellent charge carrier transport properties, and tunable bandgap. Methods for preparing perovskite solar cells are described, along with future challenges such as improving stability and replacing toxic lead.
This document discusses the preparation of MXene, a new class of 2D transition metal carbides and nitrides. MXenes are produced through selective etching of MAX phases, which are layered ternary compounds composed of early transition metals, group A elements, and carbon and/or nitrogen. The etching process removes the A layers from the MAX phase, resulting in 2D sheets of the transition metal carbides or nitrides known as MXenes. Potential applications of MXenes include their use as electrode materials in batteries and supercapacitors due to their high electrical conductivity and capacitance.
Materials Modelling: From theory to solar cells (Lecture 1)cdtpv
This document provides an overview of a mini-module on materials modelling for solar energy applications. It introduces the lecturers and outlines the course structure, which includes lectures on modelling, interfaces, and multi-scale approaches. It also describes a literature review activity where students will present a research paper using materials modelling in photovoltaics. Recommended textbooks are provided on topics like bonding in solids, computational chemistry, and density functional theory for solids.
This document provides an overview of density functional theory (DFT). It discusses the history and development of DFT, including the Hohenberg-Kohn and Kohn-Sham theorems. The document outlines the fundamentals of DFT, including how it uses functionals of electron density rather than wavefunctions to simplify solving the many-body Schrodinger equation. It also describes the self-consistent approach in DFT calculations and provides examples of popular DFT software packages.
Silicon carbide is a compound of silicon and carbon with the chemical formula SiC. It occurs naturally as the rare mineral moissanite. Mass production of silicon carbide powder began in 1893 for use as an abrasive. Edward Acheson produced silicon carbide experimentally in 1891 and patented the process, founding the Carborundum Company. Silicon carbide exists in over 250 crystalline structures and polymorphs. It has excellent chemical and physical properties including high hardness, thermal conductivity, resistance to acids and heat. The main production method involves heating quartz sand and carbon in an electric resistance furnace above 2000°C. Silicon carbide has many applications due to its properties, including use in abrasives, automotive brake discs
The document discusses computational modeling of perovskites for photovoltaic applications. Perovskites have shown great promise for solar cells due to their excellent optoelectronic properties. Computational modeling can provide insights into perovskite properties that are difficult to obtain experimentally. While lead-based perovskites have achieved high efficiencies, their toxicity is a concern, creating interest in developing non-toxic alternatives through computational studies and materials design. Opportunities and challenges of computational modeling for understanding perovskites and designing new materials are also examined.
Dye Sensitized Solar Cells- PhD Stage 3 SeminarNarges Mohamadi
This document discusses computational modeling of organic dye sensitizers for application in solar cells. It outlines the research question of how rational in silico design can be used to develop new organic dyes to increase photocurrent density by decreasing the optical band gap and extending light absorption into the near-infrared region. The document describes computational methods used, including density functional theory and time-dependent density functional theory to optimize dye structures, calculate frontier molecular orbital energies, and simulate UV-Vis absorption spectra. Selected results are presented on modifications made to an existing dye sensitizer to lower the band gap and shift absorption spectra bathochromically into the near-infrared. The overall outcome was successful design of new dyes with improved light absorption properties for potential
This document summarizes the lead storage battery. It introduces the battery as a secondary cell that can operate as both a voltaic and electrical cell. During discharging, lead plates act as the anode and lead dioxide plates act as the cathode, with sulfuric acid as the electrolyte. Chemical reactions occur that convert lead and lead dioxide to lead sulfate. The reactions reverse during charging. Lead storage batteries are commonly used in automobiles and other applications due to their ability to provide current over repeated charge/discharge cycles.
This document summarizes research on synthesizing ternary cadmium chalcogenide quantum dots (QDs) with a gradient structure and tunable bandgaps. The QDs were loaded onto mesoporous titanium dioxide films using electrophoretic deposition to create quantum dot solar cells (QDSCs). Sequentially depositing different sized QDs with varying bandgaps improved light absorption and increased power conversion efficiency compared to mixing the QDs. Further studies are investigating the synergistic electron or energy transfer mechanisms enabling the improved performance. In conclusion, the layer-by-layer QD structure maximizes light harvesting for QDSCs across the visible spectrum.
MXenes are a class of two-dimensional inorganic compounds composed of layers of transition metal carbides, nitrides, or carbonitrides. They have many desirable properties including hardness, high melting points, oxidation resistance, and high electrical and thermal conductivity. Common synthesis methods involve selectively etching MAX precursor phases, which are hexagonal layered transition metal carbides and/or nitrides. MXenes show potential for applications as sensors due to their hydrophilic surfaces, high surface areas, and ability to host intercalants.
This presentation introduces two-dimensional materials like graphene. It defines two-dimensional materials as being only one or two atoms thick and able to conduct electrons freely within their plane. The document discusses how graphene, being a single layer of graphite, is the strongest material yet and can efficiently conduct heat and electricity. It notes graphene's potential applications in electronics, solar cells, and biomedicine. In conclusion, two-dimensional materials like graphene are seen as having great potential for developing new nanoelectronics, optoelectronics, and flexible devices.
Ferromagnetic materials have three main characteristics:
1) They become spontaneously magnetized in the absence of an external magnetic field due to parallel alignment of magnetic moments.
2) They have a magnetic ordering temperature called the Curie temperature, above which they become paramagnetic.
3) They are used in many devices like transformers, electromagnets, and computer hard drives due to their magnetic properties.
Fabrication of Organic bulk Heterojunction Solar CellFarzane Senobari
This document discusses the fabrication of organic bulk heterojunction solar cells. It begins with an introduction to organic solar cells and their advantages. It then describes the working principles of organic polymer solar cells including light absorption, exciton diffusion, charge transfer, and charge collection. The common layer stack is explained including the active layer, transport layers, electrodes, and substrates. Fabrication methods like spin coating and spray coating are outlined along with specific fabrication steps for layers. Performance parameters like open circuit voltage and power conversion efficiency are discussed. The effects of UV irradiation treatment and adding zinc oxide nanoparticles are evaluated.
The document discusses Maria Goeppert Mayer's work in physics and her contributions to the field. It then lists various branches of physics and prominent physics research institutes in India, providing details on the research areas of some key institutes.
This document summarizes a presentation about using machine learning for computational chemistry. It discusses how machine learning and computational chemistry are deeply connected, with machine learning serving as a new tool for computational chemistry. The presentation outlines how machine learning can help accelerate drug discovery and materials design for applications in health and sustainability by generating new molecules and predicting chemical reactions.
Dynamic Homogenisation of randomly irregular viscoelastic metamaterialsUniversity of Glasgow
An analytical framework is developed for investigating the effect of viscoelasticity on irregular hexagonal lattices. At room temperature, many polymers are found to be near their glass temperature. Elastic moduli of honeycombs made of such materials are not constant, but changes in the time or frequency domain. Thus consideration of viscoelastic properties is essential for such honeycombs. Irregularity in lattice structures being inevitable from a practical point of view, analysis of the compound effect considering both irregularity and viscoelasticity is crucial for such structural forms. On the basis of a mechanics-based bottom-up approach, computationally efficient closed-form formulae are derived in the frequency domain. The spatially correlated structural and material attributes are obtained based on Karhunen-Lo\`{e}ve expansion, which is integrated with the developed analytical approach to quantify the viscoelastic effect for irregular lattices. Consideration of such spatially correlated behaviour can simulate the practical stochastic system more closely. Two Young's moduli and shear modulus are found to be dependent on the viscoelastic parameters, while the two in-plane Poisson's ratios are found to be independent of viscoelastic parameters. Results are presented in both deterministic and stochastic regime, wherein it is observed that the elastic moduli are significantly amplified in the frequency domain. The response bounds are quantified considering two different forms of irregularity, randomly inhomogeneous irregularity and randomly homogeneous irregularity. The computationally efficient analytical approach presented in this study can be quite attractive for practical purposes to analyse and design lattices with predominantly viscoelastic behaviour along with consideration of structural and material irregularity.
Discovering advanced materials for energy applications (with high-throughput ...Anubhav Jain
This document summarizes a talk on discovering advanced materials for energy applications using high-throughput computing and mining the scientific literature. It discusses how materials discovery and optimization typically take decades due to the vast number of possible atomic configurations. Density functional theory provides a way to computationally screen millions of potential materials by automating calculations on supercomputers. Examples are given of new battery cathode and thermoelectric materials that have been discovered through high-throughput density functional theory calculations and later experimentally confirmed.
History of nanoscience, Nanomaterial Dimensions, why small is good, surface area to volume ratio, top down and bottom up technique and physical and chemical synthesis technique and future application.
History and Applications of Finite Element Analysis
Theory of Elasticity
Finite Element Equation of Bar element
Finite Element Equation of Truss element
Finite Element Equation of Beam element
Tutorial related to
Bar element
Beam element
Finite element simulation using ANSYS 15.0
Bar element
Truss element
Beam element
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.
This document summarizes the work of Working Group II on Probabilistic Numerics from the SAMSI QMC Transition Workshop. The working group aims to develop probabilistic numerical methods that provide a richer probabilistic quantification of numerical error in outputs, allowing for better statistical inference. Members of the working group have published several papers on topics like Bayesian probabilistic numerical methods for solving differential equations and performing integral approximations, and applying these methods to problems in mathematical epidemiology and industrial process monitoring. The group has also organized workshops and reading groups to discuss the development of probabilistic numerical methods.
Available methods for predicting materials synthesizability using computation...Anubhav Jain
This document summarizes a talk about computational and machine learning approaches for predicting materials synthesizability. It discusses how machine learning algorithms are generating millions of potential stable compound predictions, far more than can be experimentally tested. It also examines ways to better prioritize candidate materials for synthesis, such as by assessing their likelihood of dynamical stability and calculating their finite-temperature Gibbs free energies more efficiently using machine-learned interatomic force constants. Finally, it describes efforts to integrate literature knowledge using natural language processing to further guide experimental exploration and reduce the number of experiments needed to synthesize predicted materials.
Introduction to computation material science.
The presentation source can be downloaded here:
http://www.attaccalite.com/wp-content/uploads/2022/11/CompMatScience.odp
Accelerating Science with Generative Adversarial NetworksMichela Paganini
Presentation at NERSC Data Day 2017 at Lawrence Berkeley National Laboratory on the potential of Generative Adversarial Networks to speed up scientific simulation and empower scientists and researchers.
This thesis examines the ab-initio calculation of spin-dependent transport properties in disordered materials. It presents a theoretical method to systematically calculate macroscopic transport quantities from first principles. The thesis considers vibrational excitations within perturbation theory and includes anharmonic terms using the quasi-harmonic approximation. It also presents a theory to introduce temperature corrections to the calculation of magnetic exchange interactions. Results are presented for a set of materials of interest for spintronics and spin-caloritronics applications.
Asynchronous futures: Digital technologies at the time of the AnthropoceneAlexandre Monnin
1) The document discusses the future of digital technologies and their relationship to physical resources and sustainability in the context of the Anthropocene.
2) It notes that while Moore's Law has led to exponential growth in computing power, this has come at tremendous resource and energy costs that may not be sustainable long-term as technologies approach physical limits.
3) The document questions where research may lead in the future and considers more sustainable alternatives like biomimetics, new architectures, and alternative materials if current trajectories prove unsustainable in light of physical and resource constraints.
The interplay between data-driven and theory-driven methods for chemical scie...Ichigaku Takigawa
The 1st International Symposium on Human InformatiX
X-Dimensional Human Informatics and Biology
ATR, Kyoto, February 27-28, 2020
https://human-informatix.atr.jp
The Algorithms of Life - Scientific Computing for Systems Biologyinside-BigData.com
In this deck from ISC 2019, Ivo Sbalzarini from TU Dresden presents: The Algorithms of Life - Scientific Computing for Systems Biology. In his talk, Sbalzarini mainly discussed the rapidly growing importance and influence in the life sciences for scientific high-performance computing.
"Scientific high-performance computing is of rapidly growing importance and influence in the life sciences. Thanks to the increasing knowledge about the molecular foundations of life, recent advances in biomedical data science, and the availability of predictive biophysical theories that can be numerically simulated, mechanistic understanding of the emergence of life comes within reach. Computing is playing a pivotal and catalytic role in this scientific revolution, both as a tool of investigation and hypothesis testing, but also as a school of thought and systems model. This is because a developing tissue, embryo, or organ can itself be seen as a massively parallel distributed computing system that collectively self-organizes to bring about behavior we call life. In any multicellular organism, every cell constantly takes decisions about growth, division, and migration based on local information, with cells communicating with each other via chemical, mechanical, and electrical signals across length scales from nanometers to meters. Each cell can therefore be understood as a mechano-chemical processing element in a complexly interconnected million- or billion-core computing system. Mechanistically understanding and reprogramming this system is a grand challenge. While the “hardware” (proteins, lipids, etc.) and the “source code” (genetic code) are increasingly known, we known virtually nothing about the algorithms that this code implements on this hardware. Our vision is to contribute to this challenge by developing computational methods and software systems for high-performance data analysis, inference, and numerical simulation of computer models of biological tissues, incorporating the known biochemistry and biophysics in 3D-space and time, in order to understand biological processes on an algorithmic basis. This ranges from real-time approaches to biomedical image analysis, to novel simulation languages for parallel high-performance computing, to virtual reality and machine learning for 3D microscopy and numerical simulations of coupled biochemical-biomechanical models. The cooperative, interdisciplinary effort to develop and advance our understanding of life using computational approaches not only places high-performance computing center stage, but also provides stimulating impulses for the future development of this field."
Watch the video: https://wp.me/p3RLHQ-kBB
Learn more: https://www.isc-hpc.com/
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Moshe Talesnik, Towards a ubiquitous good NST education Brussels, Belgium
The document discusses nanotechnology education programs for secondary schools. It analyzes 12 exemplary programs from different European countries based on parameters like whether they are compulsory or optional courses, integrate nanotechnology into other subjects or are standalone, involve virtual or in-person teaching, industry/academic partnerships, and hands-on versus theoretical focus. The analysis finds that while programs vary in their approaches, most involve independent nanotechnology subjects, hands-on teaching with industry/academic support, and aim to engage students and the broader community. The document concludes that to fully realize the potential of nanotechnology education, schools need programs that are both comprehensive and innovative.
The Face of Nanomaterials: Insightful Classification Using Deep Learning - An...PyData
Artificial intelligence is emerging as a new paradigm in materials science. This talk describes how physical intuition and (insightful) machine learning can solve the complicated task of structure recognition in materials at the nanoscale.
Combining density functional theory calculations, supercomputing, and data-dr...Anubhav Jain
The document summarizes how computational materials science using density functional theory (DFT) calculations, supercomputing, and data-driven methods can help design new materials faster than traditional experimental approaches. It describes how high-throughput DFT calculations are run on supercomputers to screen large numbers of potential materials. The results are compiled in open databases like the Materials Project to be shared and reused by researchers. While computational limitations remain, combining computation and data is helping accelerate the discovery of new materials with improved properties for applications like batteries, thermoelectrics, and carbon capture.
Nature-inspired Solutions for Engineering: A Transformative Methodology for I...KTN
Nature- Inspired Engineering (NIE) is the application of fundamental scientific mechanisms, underpinning desirable properties observed in nature (e.g., resilience, scalability, efficiency), to inform the design of advanced technological solutions. As illustrated by the many applications, from energy technology, catalysis and reactor engineering, to functional materials for the built environment, electronic or optical devices, biomedical and healthcare engineering, NIE has the opportunity to inform transformative solutions to tackle some of our most pressing challenges, as well as to be a pathway to innovation.
The webcast recording is now available. Click here to watch it: https://www.youtube.com/watch?v=gPyTb_-qhgo
Find out more about the Nature Inspired Solutions special interest group at https://ktn-uk.co.uk/interests/nature-inspired-solutions
Join the Nature Inspired Solutions LinkedIn group at https://www.linkedin.com/groups/13701855/
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Computational methods applied to materials modeling
1. Computational methods applied to materials
modeling
Dr. Federico Brivio - Federico.Brivio@natur.cuni.cz
1st
multidisciplinary conference of Italian researchers in Czechia
- June 19, 2019
Raffaello Sanzio - La scuola di Atene
6. 5
Everyone’s Materials Science
Material Science(tists) is an extreme schizophrenic field that aspire to
do everything!
Commercial products
Fundamental development
Materials itself
New applications
|
7. 6
Everyone’s Materials Science
Material Science(tists) is an extreme schizophrenic field that aspire to
do everything!
Commercial products
Fundamental development
Materials itself
New applications
|
8. 7
Material Simulation
Computers are nowadays at the base of most research!
Solve theoretical equation to
predict Material Properties
(often) Cheaper, safer,
cleaner, faster, ...
|
10. 9
Material Design
Stage I (Past):
Reproduction of specific cases
Few "home-brew" software
Stage II (Today):
Prediction of Properties of materials
Accessible/commercial software
Stage III (Future):
Prediction of Materials with specific
properties
Network, cloud, data-mining
REAL material design
|
11. 10
Material Design
Stage I (Past):
Reproduction of specific cases
Few "home-brew" software
Stage II (Today):
Prediction of Properties of materials
Accessible/commercial software
Stage III (Future):
Prediction of Materials with specific
properties
Network, cloud, data-mining
REAL material design
|
14. 13
Quantum Mechanic
Most of the properties of Materials depends on electrons, i.e. their
energy.
Instead of small little balls we need to consider waves(functions), this
is the equation:
Each electron
Electrons are waves
Electrons interact (?!)
we have a global final wavefunction!
|
15. 14
Quantum Mechanic
Most of the properties of Materials depends on electrons, i.e. their
energy.
Instead of small little balls we need to consider waves(functions), this
is the equation:
Each electron
Electrons are waves
Electrons interact (?!)
we have a global final wavefunction!
|
16. 15
Wavefunctions are toooo-large
The previous equation is VERY DIFFICULT!
Material with N
electron
3N variable (xyz)
Electron has also
a spin! 6N
variables!
we need to find a
final
wavefunction!
|
17. 16
Physicist are lazy! - DFT is born
The multivariable problem is substituted by analyzing a mean case.
The (charge) electron Density n!
Φspin,N(x, y, z) → n(x, y, z)
Study large system (today 100s atoms)
Implementation of different models with the same basics!
Check with experimental DATA!
|
30. 29
Acknowledgement
Thank you for your attention! I also want to thanks:
- Prof. Nachtigall and the whole research group
- EU - European structural and investing funds and the MSMT
This Presentation is powered by LATEX- Beamer Class
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32. 31
Images
Images sources if not specified
Slide 5 - Modified from xkcd.com
Slide 6 - Taken from: https:
//www.azom.com/article.aspx?ArticleID=15337
Slide 8 - Taken from: www.top500.org
Slide 10 - Pokemon are a trademark of Nintendo
Slide 12 - Modified from:
http://www.mm.ethz.ch/research_multiscale.html
Slide 13 - IBM Almaden Research Center
Slide 15 - Taken from: https://docplayer.ru/
57424226-Nauchnaya-vizualizaciya-v-fizike-kondensirov
html
Slide 24 - Elliott Wave International
|