Computational Materials Science - Uzbekistan
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Materialshunoslikda modellashtirish sirlarini birgalikda o'rganamiz
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Channel Posts
How To Test If We’re Living In A Computer Simulation | IFLScience
https://www.iflscience.com/how-to-test-if-were-living-in-a-computer-simulation-71919
| 2 | Scaling deep learning for materials discovery | Nature
https://www.nature.com/articles/s41586-023-06735-9 | 350 |
| 3 | Anyone clicking on this link before January 13, 2024 will be taken directly to the latest version of your article on ScienceDirect, which they are welcome to read or download. No sign up, registration or fees are required. | 2 |
| 4 | ✍ our new article
https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fauthors.elsevier.com%2Fa%2F1i8i61HxM51Fh4&data=05%7C01%7Cumedjon.khalilov%40uantwerpen.be%7C274cd28ff86b46acafc208dbece87d33%7C792e08fb2d544a8eaf72202548136ef6%7C0%7C0%7C638364257022692524%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=zmBsuLivQ4O3wgKrFvfuhJFjJvW9t2Ss701RyRc4Yqs%3D&reserved=0 | 233 |
| 5 | How to Choose a Programming Language for your Machine Learning Project?
https://www.analyticsinsight.net/how-to-choose-a-programming-language-for-your-machine-learning-project/ | 155 |
| 6 | Twenty trillion-atom simulation.pdf | 167 |
| 7 | Supercomputers and mainframe computers are both powerful computing systems designed for different purposes, and they have distinct differences in terms of architecture, performance, and usage. Here's a comparison between the two:
Supercomputer:
Purpose: Supercomputers are specifically built to handle complex scientific and engineering calculations. They are used for tasks such as weather forecasting, climate research, nuclear simulations, and other applications that require massive computational power.
Performance: Supercomputers are designed to deliver extremely high computational speeds, measured in FLOPS (floating-point operations per second). They are optimized for parallel processing and can handle vast amounts of data simultaneously, enabling them to perform complex calculations at incredible speeds.
Architecture: Supercomputers often use parallel processing techniques, where multiple processors work together on different parts of a problem simultaneously. They may also incorporate specialized accelerators like GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units) to enhance performance for specific tasks, such as machine learning computations.
Scalability: Supercomputers can be highly scalable, allowing additional processing units to be added, increasing their computational power further.
Cost: Supercomputers are typically expensive to build and maintain due to their specialized nature and high-end components.
Examples: IBM's Summit, Cray's Shasta, and Fujitsu's Fugaku are examples of supercomputers.
Mainframe Computer:
Purpose: Mainframe computers are designed to handle large volumes of data and transactions in real-time. They are commonly used by businesses and organizations for tasks such as transaction processing, database management, and enterprise resource planning (ERP) applications.
Performance: Mainframes are optimized for reliability, availability, and scalability. They offer high processing speeds and are capable of handling thousands of simultaneous users and transactions.
Architecture: Mainframes typically use a single central processor with multiple I/O (input/output) channels. They are designed for multitasking and can efficiently manage diverse workloads.
Scalability: Mainframes are highly scalable and can be expanded vertically (adding more resources to the existing system) and horizontally (connecting multiple mainframes in a cluster) to accommodate increasing workloads.
Cost: Mainframes are expensive but offer excellent value for businesses that require continuous, high-volume transaction processing and data management capabilities.
Examples: IBM zSeries mainframes, such as IBM z15, are popular examples of mainframe computers.
In summary, supercomputers excel in handling complex scientific computations and simulations, while mainframe computers are optimized for reliable and high-volume transaction processing in business environments. The choice between the two depends on the specific requirements of the applications and workloads they are intended to support. | 170 |
| 8 | Computational Materials Science combines principles from materials science, physics, chemistry, and computer science to develop and apply computational techniques to understand and predict the properties and behavior of materials. With the aid of advanced computational methods, researchers in this field can model and simulate the behavior of materials at the atomic and molecular levels, allowing for the discovery of new materials with specific properties or the optimization of existing materials for various applications.
Here are a few key areas and techniques within Computational Materials Science that you might find interesting or may want to explore further:
1. Molecular Dynamics (MD) Simulations:
MD simulations are used to study the movement of atoms and molecules over time. This technique is valuable for understanding the dynamic behavior of materials, including phase transitions, diffusion, and mechanical properties.
2. Density Functional Theory (DFT):
DFT is a quantum mechanical method used to investigate the electronic structure of materials. It's widely employed to predict properties such as electronic energy levels, charge density distributions, and magnetic properties of materials.
3. Monte Carlo Simulations:
Monte Carlo methods are probabilistic algorithms used to understand the statistical behavior of materials. They're often used to study phase transitions, thermodynamics, and to model complex systems with many degrees of freedom.
4. High-Performance Computing (HPC):
Computational Materials Science often requires significant computational resources. Familiarity with high-performance computing techniques and parallel computing can greatly enhance the efficiency and scale of simulations.
5. Machine Learning and Data-Driven Approaches:
Machine learning techniques, such as neural networks and regression models, can be applied to predict material properties, discover novel materials, and optimize material compositions. Data-driven approaches are becoming increasingly important in materials science.
6. Materials Informatics:
Materials informatics involves the application of informatics techniques to materials data. This includes data mining, database construction, and the development of algorithms for materials discovery and design.
7. Quantum Computing:
Quantum computing holds promise for solving complex materials science problems that are intractable for classical computers. Researchers are exploring quantum algorithms for tasks like simulating quantum materials and optimizing material properties.
8. Multi-Scale Modeling:
Materials properties often depend on phenomena at multiple length and time scales. Multi-scale modeling techniques bridge these scales, allowing researchers to understand how macroscopic properties emerge from microscopic interactions.
Given the interdisciplinary nature of Computational Materials Science, collaborating with experts from diverse fields can lead to innovative solutions and discoveries. Stay updated with the latest research, as this field is continuously evolving with advancements in both computational methods and materials discovery techniques. | 155 |
| 9 | https://www.growkudos.com/publications/10.1063%25252F5.0160892/reader | 147 |
| 10 | How to test whether we're living in a computer simulation
https://phys.org/news/2022-11-simulation.html | 158 |
| 11 | This article is freely available to the public for a period of 14 days. | 197 |
| 12 | Our new article:
https://pubs.aip.org/aip/jap/article/134/14/144303/2916032/The-role-of-carbon-monoxide-in-the-catalytic | 199 |
| 13 | Zentropy – A New Theory That Could Transform Material Science
https://scitechdaily.com/zentropy-a-new-theory-that-could-transform-material-science/ | 175 |
| 14 |  No text... | 1 |
| 15 | What’s next for the world’s fastest supercomputers | MIT Technology Review
https://www.technologyreview.com/2023/09/21/1079909/whats-next-for-the-worlds-fastest-supercomputers/ | 143 |
| 16 | Our new article:
https://www.tandfonline.com/doi/full/10.1080/08927022.2023.2254393?scroll=top&needAccess=true&role=tab | 147 |
| 17 | https://www.scm.com/news/embedding-quantum-simulations-in-classical-computers/?utm_source=news&utm_medium=Linkedin&utm_campaign=Quantum-computing | 204 |
| 18 | https://www.linkedin.com/posts/rustam-karimjonov-50913920_good-morning-washington-tv-program-is-promoting-ugcPost-7103000779443089409-Lfe3?utm_source=share&utm_medium=member_android | 180 |
| 19 | https://www.edx.org/plp/introduction-computer-science-harvardx-cs50x?utm_source=braze&utm_medium=email&utm_campaign=Wednesday_Dedicated_Jira_20230906&utm_content=Variant%201 | 200 |
| 20 | Think of it like this: Imagine a coin. A regular bit is like that coin lying flat, showing either heads or tails (like 1 or 0). Now, a qubit is like a coin spinning in the air. It's still heads and tails, but we can't know if it's heads or tails until it stops spinning. That's when we find out its final state, kind of like when the spinning coin lands and we see whether it's heads or tails up. | 210 |
