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.
Lecture: Interatomic Potentials Enabled by Machine LearningDanielSchwalbeKoda
Lecture for the 4th IKZ-FairMAT Winter School. Describes recent advances in neural network interatomic potentials, deep learning models accelerating quantum chemistry, and more.
In computational physics and Quantum chemistry, the Hartree–Fock (HF) method also known as self consistent method is a method of approximation for the determination of the wave function and the energy of a quantum many-body system or many electron system in a stationary state
Lecture: Interatomic Potentials Enabled by Machine LearningDanielSchwalbeKoda
Lecture for the 4th IKZ-FairMAT Winter School. Describes recent advances in neural network interatomic potentials, deep learning models accelerating quantum chemistry, and more.
In computational physics and Quantum chemistry, the Hartree–Fock (HF) method also known as self consistent method is a method of approximation for the determination of the wave function and the energy of a quantum many-body system or many electron system in a stationary state
Magnetocaloric effect and magnetic field-induced martensitic transformation i...Universidad de Oviedo
One of the challenges of modern societies consists in to increase the equipment energy efficiency, whereby reducing the energy consumption. In this sense, the magnetic solid-state refrigeration technology based on the magnetocaloric effect (MCE), attracts an enormous interest because of its potential to substitute the conventional liquid-gas refrigerant systems due to, among other advantages, its superior efficiency (up to 60% of Carnot's cycle) [1,2]. However, to be commercially competitive, this technology still needs cheap materials with enhanced refrigerant properties. Among the potential materials, metamagnetic shape memory alloys (mainly, Heusler-type Ni-Mn-based alloys) occupy a unique place because, alongside the shape memory effect and superelasticity, they exhibit large magnetocaloric effect due to the sharp change of the magnetization associated to the magnetostructural martensitic transformation (MT) [4].
We will present our recent studies of both the magnetocaloric effect and the influence of magnetic field on MT in metamagnetic Ni-Mn-In alloys doped by Cu and Cr. This doping mode allows a fine tuning of both the MT temperature around the room temperature (278-315 K) and magnetization drop at MT. The adiabatic MCE measurements have been performed using in-house made set-up [3]. An application of 1.9 T magnetic field results in a maximum inverse adiabatic temperature change of ~ -2 K caused by magnetic field-induced MT. Besides, the austenite phase undergoes a ferro-to-paramagnetic transition to which a direct adiabatic temperature change of almost the same amplitude as for inverse effect is associated. Furthermore, MT moves to lower temperatures (around 40 K for Cu-doped alloy) in magnetic fields up to 10 T accompanied by a decrease of the transformation entropy change.
References:
1. M.-H. Phan and S.-C. Yu, J. Magn. Magn. Mater. 308, 325 (2007).
2. V. Franco, J.S. Blázquez, B. Ingale, and A. Conde, Annu. Rev. Mater. Res. 42, 305 (2012).
3. V.A. Chernenko et al., J. Magn. Magn. Mater. 324, 3519 (2012).
4. P. Álvarez-Alonso et al., Key Eng. Mater. 644, 215–218 (2015).
Solid-state electrolytes exhibit good safety and stability, and are promising to replace current organic liquid electrolytes in rechargeable battery applications. In this talk, we will present our efforts at developing scalable first principles techniques to design novel solid-state electrolytes. Using the recently discovered Li10GeP2S12 lithium super ionic conductor as an example, we will discuss how various properties of interest in a solid-state electrolyte can be predicted using first principles calculations. We will show how the application of these first principles techniques has suggested two chemical modifications, Li10SiP2S12 and Li10SnP2S12, that retains the excellent Li+ conductivity of Li10GeP2S12 at a significantly reduced cost. These modifications have recently been synthesized, and the measured Li+ conductivities are in excellent agreement with our first principles predictions. We will conclude with a demonstration of how relatively expensive first principles calculations can be intelligently scaled and combined with topological analysis to be a useful screening tool for novel solid-state electrolytes.
Resources for Teaching Undergraduate Computational PhysicsAmdeselassie Amde
Experience from Physics Department, University of Gondar ...why we should teach our students undergraduate computational physics (UCP), and Free & Open Resources for teaching UCP
TMS workshop on machine learning in materials science: Intro to deep learning...BrianDeCost
This presentation is intended as a high-level introduction for to deep learning and its applications in materials science. The intended audience is materials scientists and engineers
Disclaimers: the second half of this presentation is intended as a broad overview of deep learning applications in materials science; due to time limitations it is not intended to be comprehensive. As a review of the field, this necessarily includes work that is not my own. If my own name is not included explicitly in the reference at the bottom of a slide, I was not involved in that work.
Any mention of commercial products in this presentation is for information only; it does not imply recommendation or endorsement by NIST.
This presentation is the introduction to Density Functional Theory, an essential computational approach used by Physicist and Quantum Chemist to study Solid State matter.
Hokkaido University (HU) - Seoul National University (SNU) Joint Symposium
2018 International Workshop on
New Frontiers in Convergence Science and Technology
(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.
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Magnetocaloric effect and magnetic field-induced martensitic transformation i...Universidad de Oviedo
One of the challenges of modern societies consists in to increase the equipment energy efficiency, whereby reducing the energy consumption. In this sense, the magnetic solid-state refrigeration technology based on the magnetocaloric effect (MCE), attracts an enormous interest because of its potential to substitute the conventional liquid-gas refrigerant systems due to, among other advantages, its superior efficiency (up to 60% of Carnot's cycle) [1,2]. However, to be commercially competitive, this technology still needs cheap materials with enhanced refrigerant properties. Among the potential materials, metamagnetic shape memory alloys (mainly, Heusler-type Ni-Mn-based alloys) occupy a unique place because, alongside the shape memory effect and superelasticity, they exhibit large magnetocaloric effect due to the sharp change of the magnetization associated to the magnetostructural martensitic transformation (MT) [4].
We will present our recent studies of both the magnetocaloric effect and the influence of magnetic field on MT in metamagnetic Ni-Mn-In alloys doped by Cu and Cr. This doping mode allows a fine tuning of both the MT temperature around the room temperature (278-315 K) and magnetization drop at MT. The adiabatic MCE measurements have been performed using in-house made set-up [3]. An application of 1.9 T magnetic field results in a maximum inverse adiabatic temperature change of ~ -2 K caused by magnetic field-induced MT. Besides, the austenite phase undergoes a ferro-to-paramagnetic transition to which a direct adiabatic temperature change of almost the same amplitude as for inverse effect is associated. Furthermore, MT moves to lower temperatures (around 40 K for Cu-doped alloy) in magnetic fields up to 10 T accompanied by a decrease of the transformation entropy change.
References:
1. M.-H. Phan and S.-C. Yu, J. Magn. Magn. Mater. 308, 325 (2007).
2. V. Franco, J.S. Blázquez, B. Ingale, and A. Conde, Annu. Rev. Mater. Res. 42, 305 (2012).
3. V.A. Chernenko et al., J. Magn. Magn. Mater. 324, 3519 (2012).
4. P. Álvarez-Alonso et al., Key Eng. Mater. 644, 215–218 (2015).
Solid-state electrolytes exhibit good safety and stability, and are promising to replace current organic liquid electrolytes in rechargeable battery applications. In this talk, we will present our efforts at developing scalable first principles techniques to design novel solid-state electrolytes. Using the recently discovered Li10GeP2S12 lithium super ionic conductor as an example, we will discuss how various properties of interest in a solid-state electrolyte can be predicted using first principles calculations. We will show how the application of these first principles techniques has suggested two chemical modifications, Li10SiP2S12 and Li10SnP2S12, that retains the excellent Li+ conductivity of Li10GeP2S12 at a significantly reduced cost. These modifications have recently been synthesized, and the measured Li+ conductivities are in excellent agreement with our first principles predictions. We will conclude with a demonstration of how relatively expensive first principles calculations can be intelligently scaled and combined with topological analysis to be a useful screening tool for novel solid-state electrolytes.
Resources for Teaching Undergraduate Computational PhysicsAmdeselassie Amde
Experience from Physics Department, University of Gondar ...why we should teach our students undergraduate computational physics (UCP), and Free & Open Resources for teaching UCP
TMS workshop on machine learning in materials science: Intro to deep learning...BrianDeCost
This presentation is intended as a high-level introduction for to deep learning and its applications in materials science. The intended audience is materials scientists and engineers
Disclaimers: the second half of this presentation is intended as a broad overview of deep learning applications in materials science; due to time limitations it is not intended to be comprehensive. As a review of the field, this necessarily includes work that is not my own. If my own name is not included explicitly in the reference at the bottom of a slide, I was not involved in that work.
Any mention of commercial products in this presentation is for information only; it does not imply recommendation or endorsement by NIST.
This presentation is the introduction to Density Functional Theory, an essential computational approach used by Physicist and Quantum Chemist to study Solid State matter.
Hokkaido University (HU) - Seoul National University (SNU) Joint Symposium
2018 International Workshop on
New Frontiers in Convergence Science and Technology
(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.
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.
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
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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.
Introduction to computation material science.
The presentation source can be downloaded here:
http://www.attaccalite.com/wp-content/uploads/2022/11/CompMatScience.odp
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X-Dimensional Human Informatics and Biology
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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/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The Face of Nanomaterials: Insightful Classification Using Deep Learning - An...PyData
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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
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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
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Slide 24 - Elliott Wave International
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