#DFT

[har-tee]

Me vs density functional theory-a love story written in wavefunctions and coffee stains…

July 26 2025 7:35 pm

diving back into my physics girl era- cracked open the notes tonight for some dft basics and a quantum mechanics refresher—turns out, my brain still lights up for eigenfunctions and electron densities.

[zachre]

Came home and didn't even wash my hands for 4 hours(ew)

[kraniumet]

double feature thursday cancelled for the week. triple feature friday - reservoir dogs (1992), the thomas crown affair (1968), the fast and the furious (2001)

[emilynftgames]

水耕栽培とは？メリット・デメリット、DFTとNFTを解説【養液栽培の固形培地耕との違い】

[govindhtech]

FreeQuantum Pipeline: Quantum Advantage For Drug Discovery

The pioneering FreeQuantum Pipeline set the stage for quantum drug discovery. An multinational team of experts revealed FreeQuantum, a computational pipeline that would revolutionise molecular binding energy calculation in drug discovery and biochemistry. By integrating machine learning, classical simulation, and high-accuracy quantum chemistry into a modular system, this innovative architecture provides a viable roadmap for quantum computers in molecular science and may enable quantum advantage in biology.

Fixing a Biochemical Modelling Bottleneck

For decades, computational biology has struggled with a basic trade-off in free energy estimates, the gold standard for molecular recognition. Even if they are effective and scalable, classical force fields often fail to capture subtle quantum interactions, especially in heavy elements or open-shell systems. However, exponential scaling makes high-accuracy quantum chemical techniques computationally impractical for anything more than a few dozen atoms, notwithstanding their precision. From drug development to protein engineering, accurately predicting the free energy of binding and molecule binding strength is essential. FreeQuantum's Hybrid Method: Needle Threading

This challenge was addressed by carefully planning the FreeQuantum pipeline. Machine learning acts as an intelligent bridge to incorporate accurate quantum-mechanical computations into a larger classical molecular simulation. A three-layer hybrid architecture keeps processing efficiency in some areas while selectively going for quantum-level accuracy where it's essential. In the “quantum core,” highly correlated, wavefunction-based methods estimate the electronic energies of tiny but chemically important subregions. After training with these high-accuracy data, machine learning models may generalise and predict molecular system behaviour. Most importantly, the architecture allows the modelling of the quantum core on quantum computers as they grow and become accessible, demonstrating the transformative potential of quantum advantage. If the conditions are met, FreeQuantum can use quantum computed energies to improve biological process models with quantum computing. This technology uses quantum computers' exponential speedups for simulating interacting electrons to model large molecules using conventional simulation and machine learning.

A Real-World Test: The Ruthenium-Based Anticancer Drug

To confirm their unique approach, the researchers utilised FreeQuantum to model the binding relationship between ruthenium-based anticancer medication NKP-1339 and its protein target, GRP78. Due to their complex open-shell electronic structures and multiconfigurational nature, transition metals like ruthenium constitute the “worst-case scenario” for ordinary classical force fields and are notoriously difficult to describe using density functional theory. The study has numerous phases: Classical molecular dynamics simulations sampled structure configurations using standard force fields.

A selection of these configurations was refined using hybrid quantum/classical methods, starting with DFT-based methods and progressing to wavefunction-based methods like NEVPT2 and linked cluster theory, to compute precise energies at specified sites. Using these precise energy data points, ML1 and ML2 machine learning potentials were trained. The FreeQuantum pipeline predicted a binding free energy of almost −11.3 ± 2.9 kJ/mol using accurate quantum techniques. Classical force fields predicted −19.1 kJ/mol, but this is a substantial deviation. A variation of 5 to 10 kilojoules per mole can determine whether a chemical clings long enough to be a medicine or slips away too quickly, which may seem insignificant but has major implications for drug discovery. This discovery emphasises the need of quantum-level accuracy in biologically relevant systems and shows how sensitive molecule simulations are to electronic structure. A Quantum-Ready Biochemistry Future Despite using high-performance computer resources in its original demonstration, the pipeline architecture is quantum-ready. Researchers have carefully evaluated the prerequisites for quantum computers to seamlessly take over quantum core calculations. The team estimates that a fault-tolerant quantum computer with 1,000 logical qubits could compute the required energy data in 20 minutes per energy point using sophisticated algorithms like quantum phase estimation (QPE) and qubitization and Trotterization. The machine learning model needs roughly 4,000 of these points to train to the benchmark system's accuracy. With appropriate parallelisation, the simulation might end in under 24 hours. Achieving aggressive goals like gate fidelities below 10⁻⁷ and logical gate durations below 10⁻⁷ seconds may be necessary, based on realistic constraints like hardware gate speeds and error rates. These are demanding goals, but fault-tolerant systems may be able to achieve them. The group provided methods for creating high-overlap guiding states, which are needed for successful QPE, showing that low-bond-dimension matrix product states and other approximations can efficiently initialise the quantum system. Open-Source Architecture and Future Plans More than just a theory, FreeQuantum automates and modularises molecular simulation, quantum embedding, machine learning training, and quantum resource management. Due to MongoDB-based data interchange, modules can work on dispersed infrastructure. Due to its design, quantum cores can be simulated using conventional or forthcoming quantum computing backends, allowing quantum and classical subsystems to be interchangeable depending on hardware. The open-sourced codebase will make it easier to build and adapt to new hardware, modelling goals, and approaches. FreeQuantum is an important step, even though conventional quantum chemical methods are limited for systems with large quantum cores or extensive dynamic correlation and quantum computing is years from being used commercially and accurately for drug discovery. Instead of waiting for “quantum supremacy” across molecules, the pipeline deploys quantum resources incrementally when classical approaches fail. This calculated deployment may make quantum advantage in molecular biology more realistic and faster. The research team plans to apply the framework to other high-complexity systems like enzymatic catalysis, redox-active cofactors, and multi-metal active sites as they believe quantum-enhanced simulations will become standard tools in computational chemistry by elevating classical models where they are most useful.

[altem-technologies]

Accelerate Innovation with BIOVIA Material Studio 🧪

Discover the power of BIOVIA Material Studio, the leading modeling and simulation environment for materials science and chemistry. From atomic-scale insights to bulk material properties, Material Studio enables researchers, scientists, and product developers to: ✅ Predict material properties with high accuracy using DFT, MD, and MC simulations ✅ Design and optimize polymers, catalysts, nanomaterials, and formulations ✅ Simulate electronic structure, mechanical strength, thermal stability, and more ✅ Integrate seamlessly into your research or R&D pipeline Whether you are in academia, chemicals, energy, or electronics, Material Studio is your virtual lab for faster, smarter material innovation. Know More: https://altem.com/material-studio/

[davidzuratzi]

Token de Metamorfo 2-2 del bloque de aetherdrift

Este lo dibujé directo sin sketch a lápiz

[ukaviationnews]

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…

[auckam]

Avoid 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

[greenfue]

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…

[zachre]

the last scream before going to school

[kraniumet]

double feature thursday - the cable guy (1996), high fidelity (2000)

[suthecoder]

What are we wearing to the Aetherdrift pre-release?

Gotta get out those makeup tutorials from the 80s!

[ivectormx]

ZORO VECTOR EDITABLE EPS PDF ONE PIECE

[govindhtech]

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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