President Asshole has turned the Oval Office into a 2nd-Hand Shop.
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Era Penemuan Dipercepat: AI Tidak Lagi ‘Meniru’, Tapi ‘Mencipta’ Pengetahuan Baru
Oktober 2025 memperlihatkan lompatan sains yang sangat nyata. Meta FAIR merilis OMat24—kumpulan >110 juta perhitungan DFT berikut model EquiformerV2—yang menembus posisi puncak Matbench Discovery untuk prediksi stabilitas/energi formasi material; rilis ini membuka jalan skrining kandidat superkonduktor, material baterai, hingga katalis skala masif. Di sisi medis, Google Research memperkenalkan…
Era Penemuan Dipercepat: AI Tidak Lagi ‘Meniru’, Tapi ‘Mencipta’ Pengetahuan Baru
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[05/10/2025]
I moved finally it was hectic to say the least and now after like 5 days I think I’m settling in a little but man the humidity is killing me. I stayed the past 4 days for atleast a few hours in the lab which is a huge improvement but since I don’t have the stamina to sit that long I get very tired when I get back to my room. I also need to study for the the exam which is in few months and aaa I don’t feel very confident right now about anything. Tomorrow the week starts after a long holiday break and it will be very crowded and the other lab people will also come back and I don’t work well when I get overstimulated so I’m scared that I will lose the momentum I have managed to keep till now. I will also meet my professor tomorrow for the first time aside from a few emails and messages, he doesn’t even know how I look. I haven’t read all the papers that I was supposed to but I think that’s alright I still have to pick my specific project and I don’t know if I am ready for the kind of research freedom that comes with this. What if I choose something right now and end up too stupid to make any progress in it? The prof said I can choose what I want to work and take my time to make myself familiar with everything, he seems chill but I don’t want to take advantage of that as I did for my ms thesis which ended up badly. The one labmate which is the only one I’ve met till now is cool and pretty helpful which is nice ig but my introvert tendencies come out cause I don’t want to spend all my time awake with other people, I need my alone time to roam around too. I hope the dynamic changes. I sound so bratty right now but I really don’t want to be overstimulated cause it messes the whole day and night away and I have work that needs to be done. I also wish I can make proper friends here to just sit with them without it being acquaintances from the lab. It was very deserted in college till today but now the students are coming back and the shops are opening up and it’s getting fuller on the streets too which is nice in the evening. I have started smoking again but like 3 atmost in a day which is fine and it doesn’t really make me sick, but I only smoke after 5pm. My compulsive thoughts are still there but I can distract myself a little right now to not think too much about them, I still get bouts of anger that I just have to ride out but it’s still much better than last month. I have my own desktop so I have two screens I can work with. I think I’ll try to complete this one paper and read the summaries of the other on surface so I don’t make a fool of myself tomorrow.
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Quantum news: What is Exchange Correlation Functional in DFT
Quantum Chemistry’s Main Problem: Exchange Correlation Functional
Understanding material behaviour is crucial to developing new chemical and materials science technologies like quantum computers, better batteries, and stronger drugs. This understanding requires accurately simulating electrons, the subatomic particles responsible for chemical bonding, electrical properties, and practically all material behaviours. Highly accurate simulation methods are often too computationally expensive. DFT is a more helpful approach, but its precision depends on recognising the exchange correlation functional.
How is Exchange Correlation Functional?
The exchange-correlation (XC) functional is crucial to DFT. DFT, a quantum mechanical modelling technique, simplifies complex computations by focussing on electron density, the possibility of finding an electron in a certain position, rather than tracking each electron individually. DFT is much more computationally efficient than “quantum many-body” computations, which can only mimic small atoms and molecules.
The XC functional describes electron interactions in quantum mechanics. Essentially, it considers two crucial quantum effects:
The Exchange Interaction: The Pauli Exclusion Principle states that two electrons with the same spin cannot share a quantum state. Electrons’ quantum nature creates a form of “repulsion” rather than electric charge.
Electrons avoid each other due to their reciprocal electrical repulsion in the “correlation interaction”. Their movements are linked.
The DFT equations can be solved because the XC functional condenses several crucial but complex quantum behaviours into one mathematical term.
Seeking a “Universal” Functional
Scientists struggle because the XC functional’s mathematical form is unknown. Researchers say there is a single, perfect equation called a “universal functional” that can accurately explain electron interactions in any semiconductor, molecule, or metal. Identification of this universal functional is a major goal in chemistry and materials research because it would greatly improve DFT models’ predictive power.
Scientists must utilise approximations without this ideal equation. These approximate XC functionals are often customised for specific purposes, which may limit simulation accuracy and generalisability. U.S. national laboratories spend one-third of their supercomputer time perfecting approximations and getting closer to the universal functional.
Machine Learning Improves XC Functionality
A new machine learning-based strategy from University of Michigan researchers has advanced the search for a more precise XC functional. Instead of approximating, they inverted the problem.
Start with the “Right Answer”: Start with the “Right Answer”: The precise but computationally expensive quantum many-body theory was used to calculate electron behaviour in tiny atoms and compounds including lithium, carbon, dihydrogen, and lithium hydride. A standard for accurate results was provided.
Work Backward with Machine Learning: The group then used machine learning to determine the precise, accurate results the XC functional would need in the more effective DFT framework. According to University of Michigan professor Paul Zimmerman, his team streamlined and accelerated many-body results while maintaining accuracy.
The new XC feature generated utilising machine learning was really accurate. DFT often uses a “ladder” metaphor to describe accuracy, with each rung indicating greater precision. The Michigan team’s functional improved efficiency and accuracy by using only “second-rung” computing labour while reaching “third-rung” accuracy.
Accurate XC Functionality’s Wide Effect
Creating a more exact XC functional has many effects. Since it is material-agnostic, the functional is valuable in many scientific fields. It’s relevant for researchers researching novel battery materials, pharmaceuticals, and quantum computers, says study first author Bikash Kanungo.
Researchers at Michigan have developed a more accurate XC functional that can be utilised in simulations by other scientists. It also shows a promising new approach for functional discovery, which may involve integrating more advanced electrical features or employing the same process for solid materials. This research advances the ongoing attempt to model the quantum environment accurately.
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Moved back home…
Me vs density functional theory — a love story written in wavefunctions and coffee stains…
September 8 2025 · 8:39 pm
still in my physics girl era — wrapped up the DFT basics tonight. now it’s just a little bit of quantum mechanics and solid state physics standing between me and finally diving into the electrochemistry side of it all. feels like unlocking the next level of a very nerdy video game.
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Token de Metamorfo 2-2 del bloque de aetherdrift
Este lo dibujé directo sin sketch a lápiz
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Government outlines plans for biggest redesign of UK airspace since 1950
The UK Government is setting out plans in Parliament today to undertake the largest redesign of UK airspace since it was formed in 1950 when it had to handle just 200,000 flights per year.
The results could see shorter flight times, particularly around congested areas such as London, fewer delays and for residents living near airports, lower noise levels.
Another aim of the redesign is to…
Government outlines plans for biggest redesign of UK airspace since 1950
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WOAHZIES oh yeah THANK YOU FOR INCLUDING ME I VERY MUCH ENJOY DINOSAUR FACT TIME I’ve actually been meaning to learn more about dinos….!!!!! at first i felt bad that I didn’t know any dink facts BUT THEN i remembered that i know of a discord server with dino lovers in it!!! i haven’t interacted a lot with the members and vice versa but im sure they’ll welcome their fellow dino lovers with open arms !!!! i hope this shall suffice for dino fact time and have a wonderful day!!!!
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ALTAvoid costly delays and failures in your hardware development process by steering clear of these 6 common design and testing pitfalls. This infographic from Auckam Technologies highlights critical mistakes like skipping DFT (Design for Testability), vague hardware requirements, bad component selection, incomplete prototyping, and poor manufacturing handoff. Perfect for electronics manufacturers, engineers, and product developers who want smoother, faster time to market.
🌐 Learn more at: www.auckam.com
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بطاريات تتنفس الكربون.. تقدم ثوري صديق للبيئة من جامعة سري
حقق علماء في جامعة سري تقدمًا هائلًا في مجال البطاريات الصديقة للبيئة، التي لا تقتصر على تخزين المزيد من الطاقة فحسب، بل قد تُسهم أيضًا في الحد من انبعاثات غازات الاحتباس الحراري.
تُطلق بطاريات الليثيوم-ثاني أكسيد الكربون “التنفسية” الطاقة أثناء احتجاز ثاني أكسيد الكربون، مما يُوفر بديلاً صديقًا للبيئة قد يتفوق يومًا ما على بطاريات الليثيوم-أيون الحالية.
حتى الآن، واجهت بطاريات الليثيوم-ثاني…
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What are we wearing to the Aetherdrift pre-release?
Gotta get out those makeup tutorials from the 80s!
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Introducing Generative Chemistry and Accelerated DFT
Generative Chemistry and Accelerated DFT Arrive in Azure Quantum Elements
In Azure Quantum Elements, Microsoft is pleased to introduce two significant new capabilities: accelerated density functional theory (DFT) and generative chemistry.
Through the integration of new tools based on generative AI and high-performance computing, Azure Quantum Elements is facilitating faster, easier, and more productive research in chemistry and materials science.
Microsoft wants to enable every individual and every organization on the planet to reach their full potential. By providing scientific capabilities based on AI and cloud high-performance computing (HPC), Azure Quantum Elements supports this goal. By significantly lowering the effort and knowledge required to complete previously difficult tasks, these user-friendly technologies significantly boost the efficiency of scientific research and remove obstacles on the route to scientific discovery. More specifically, these features make Azure Quantum Elements more widely available and speed up the resolution of challenging scientific issues by utilizing Copilot for Azure Quantum, a natural-language interface that is user-friendly for both professionals and novices.
Microsoft’s most pressing problems will require the combined genius of the world’s population, and they are thrilled to be able to offer scientists, students, and institutions like Unilever new tools so that everyone can help make scientific discoveries that improve the world.
Azure Quantum Elements has been instrumental in assisting scientists in making significant discoveries that have opened the door to more environmentally friendly batteries and advancements in the pharmaceutical sector since its launch. Today, Microsoft is introducing two brand-new, specially designed features in Azure Quantum Elements: Accelerated DFT and Generative Chemistry, which will significantly boost the accessibility and productivity of chemistry and materials science research.
Scientists can find new, synthesizable, and practical compounds more quickly thanks to generative chemistry.
There are still numerous undiscovered molecular entities and compounds among the hundreds of millions of known ones. Reducing the vast number of potential molecules to the few that are most appropriate for a given application is a significant task in the science of chemistry. The streetlight effect is the consequence of this issue; it is the process by which the enormous number of options are narrowed down to a manageable size by concentrating solely on compounds that have been previously researched, rather than on the characteristics of the compounds themselves.
By limiting the search space and revealing only known compounds as potential candidates for particular uses, databases are used to find appropriate molecules. In order to provide scientists with innovative candidates that are likely to fulfil the specified objective, generative AI helps illuminate a considerably bigger fraction of the estimated 1060 potential combinations of atoms.
Today, the Microsoft Azure Quantum team is announcing Generative Chemistry, an emerging technology that might transform product innovation productivity by helping scientists find and develop novel compounds with desired attributes more quickly.
The end-to-end workflow known as “Generative Chemistry” will be accessible through the Azure Quantum Elements private preview and consists of several steps:
For each particular application, you give details on the needed molecular properties. Furthermore, if you already have a few options in mind, you can provide reference compounds.
Using a dataset and the information you supply, seed molecules are created. These seed molecules are then utilised to start the guided artificial intelligence process of creating candidate molecules for your application. Several AI models are used in conjunction with a special technique to find new chemicals that meet your requirements. You can select the most pertinent generative AI model, specify the amount of molecules to be formed, indicate the important chemical features, and screen compounds for toxicity, among other configuration options, in this stage.
AI-based screening models forecast candidate molecule characteristics like density, solubility, and boiling point that are crucial for practical uses. The directed AI creation receives this information via a feedback loop, which modifies the candidate molecule selection process. You can also adjust the AI models in this phase to better fit your particular use case.
A crucial stage that determines if the molecules can be made in a lab is the use of AI-guided synthesis planning to further reduce the pool of viable possibilities. This is because certain novel molecules with desirable features could be challenging to synthesise. In this step, potential chemical pathways are forecasted and candidate compounds are sorted according to their ease of production.
On the best candidates, extremely precise HPC simulations are run. Candidates can be screened using accelerated DFT for electronic characteristics including polarizability, ionisation potential, and dielectric constant. Not only can AutoRXN forecast chemical stability or reactivity, but it can also offer insights into potential synthesis paths.
When it comes to laboratory synthesis and testing, you can choose the most promising of the final candidate compounds that are offered to you.
A discovery pipeline that simulates thousands of previously unknown molecules and filters them through a series of screening steps to suggest several promising candidates for specific applications.
Image credit to Microsoft Azure
The entire procedure takes only a few days, saving months or even years of labor-intensive laboratory testing that were previously necessary to get this far. With the help of generative chemistry, scientists can discover completely new substances and concentrate only on those that are suitable for their intended use, which saves time, money, and effort. The creation of innovative medicines, sustainable materials, and other things will advance more quickly thanks to this new capabilities.
When compared to previous density functional theory algorithms, accelerated DFT provides noticeably faster results
The efficiency and accuracy of density functional theory (DFT) in modelling quantum-mechanical features make it one of the most widely used techniques in computational chemistry. By simulating and examining the electronic structures of atoms, molecules, and nanoparticles as well as surfaces and interfaces, it enables scientists to forecast attributes like polarizability, ionisation potential, and dielectric constant. Scientists can then modify those characteristics to best suit particular uses.
Despite its great value for research and product design, DFT algorithms typically require user intervention to run on HPC clusters, which can be a challenging task. Furthermore, DFT gets limited as the complexity and size of the molecules being investigated or created increase and demands a significant amount of compute power when done on conventional HPC gear.
Accelerated DFT is a code that simulates the electronic structure of molecules and was created by Azure Quantum and Microsoft Research to streamline and enhance this process. Within hours, hundreds of atoms of a molecule can have its properties determined using Accelerated DFT. It outperforms existing DFT programmes and provides an average speed gain of 20 times over PySCF, a popular open-source DFT code.
Because Accelerated DFT is available as a service and doesn’t require user configuration or code compilation, it’s easy to set up. It also has a simpler API that speeds up the calculating process. DFT calculations can also be easily integrated into complex chemistry workloads by researchers thanks to the seamless integration provided by a Python Software Development Kit (SDK) into a wide range of computational chemistry settings. Accelerated DFT is currently accessible through the private preview of Azure Quantum Elements and will be integrated into Generative Chemistry.
By utilising Azure’s cloud architecture, Accelerated DFT may significantly accelerate research in a variety of chemical disciplines. AI models, which need a lot of training data, can be improved by using the enormous and extremely accurate datasets of molecular characteristics that are produced by accelerated DFT. Innovations in medicines, sustainable products, and other fields might result from the quick generation of training data, which also makes it possible to find new compounds and enhance existing ones. A vast basis set and innovative hybrid functionals can be used effectively with Accelerated DFT thanks to its user-friendly Python interface and faster computations. This means that important thermodynamic properties can be estimated in a few hours.
Utilising quantum computing, Azure Quantum Elements
By utilising AI, HPC, and cutting-edge hybrid computing technologies that apply the power of quantum computing to scientific problems, Azure Quantum Elements grows more useful as new features are added. Recently, they used Microsoft’s qubit-virtualization system, Quantinuum’s H1 hardware, AI, and conventional supercomputers to mimic a chemical catalyst. In the upcoming months, they will bring sophisticated logical qubit capabilities to the Azure Quantum Elements private preview from Quantinuum and Microsoft. This offering of hybrid computing, combining elements of classical and quantum physics, builds on our quantum computing milestone of creating the most dependable logical qubits ever, with an error rate 800 times lower than that of the corresponding physical qubits, using Quantinuum.
Scientific problems around the world may be resolved with the aid of developments in AI and quantum computing. Microsoft intend to provide a quantum supercomputer in the future that can replicate quantum interactions between molecules and atoms, which are not possible with classical computers. Many sectors’ research and innovation are predicted to change as a result of this capability. In order to promote the secure use of these technologies, Microsoft shall guarantee their responsible development and implementation. As these capabilities advance, Microsoft will keep enacting careful protections, strengthening their dedication to responsible AI, and adopting responsible computing practices.
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