Close Menu
    DevStackTipsDevStackTips
    • Home
    • News & Updates
      1. Tech & Work
      2. View All

      AI and its impact on the developer experience, or ‘where is the joy?’

      July 23, 2025

      Google launches OSS Rebuild tool to improve trust in open source packages

      July 23, 2025

      AI-enabled software development: Risk of skill erosion or catalyst for growth?

      July 23, 2025

      BrowserStack launches Figma plugin for detecting accessibility issues in design phase

      July 22, 2025

      Power bank slapped with a recall? Stop using it now – here’s why

      July 23, 2025

      I recommend these budget earbuds over pricier Bose and Sony models – here’s why

      July 23, 2025

      Microsoft’s big AI update for Windows 11 is here – what’s new

      July 23, 2025

      Slow internet speed on Linux? This 30-second fix makes all the difference

      July 23, 2025
    • Development
      1. Algorithms & Data Structures
      2. Artificial Intelligence
      3. Back-End Development
      4. Databases
      5. Front-End Development
      6. Libraries & Frameworks
      7. Machine Learning
      8. Security
      9. Software Engineering
      10. Tools & IDEs
      11. Web Design
      12. Web Development
      13. Web Security
      14. Programming Languages
        • PHP
        • JavaScript
      Featured

      Singleton and Scoped Container Attributes in Laravel 12.21

      July 23, 2025
      Recent

      Singleton and Scoped Container Attributes in Laravel 12.21

      July 23, 2025

      wulfheart/laravel-actions-ide-helper

      July 23, 2025

      lanos/laravel-cashier-stripe-connect

      July 23, 2025
    • Operating Systems
      1. Windows
      2. Linux
      3. macOS
      Featured

      ‘Wuchang: Fallen Feathers’ came close to fully breaking me multiple times — a soulslike as brutal and as beautiful as it gets

      July 23, 2025
      Recent

      ‘Wuchang: Fallen Feathers’ came close to fully breaking me multiple times — a soulslike as brutal and as beautiful as it gets

      July 23, 2025

      Sam Altman is “terrified” of voice ID fraudsters embracing AI — and threats of US bioweapon attacks keep him up at night

      July 23, 2025

      NVIDIA boasts a staggering $111 million in market value per employee — since it became the world’s first $4 trillion company

      July 23, 2025
    • Learning Resources
      • Books
      • Cheatsheets
      • Tutorials & Guides
    Home»Development»Artificial Intelligence»New model predicts a chemical reaction’s point of no return

    New model predicts a chemical reaction’s point of no return

    April 23, 2025

    When chemists design new chemical reactions, one useful piece of information involves the reaction’s transition state — the point of no return from which a reaction must proceed.

    This information allows chemists to try to produce the right conditions that will allow the desired reaction to occur. However, current methods for predicting the transition state and the path that a chemical reaction will take are complicated and require a huge amount of computational power.

    MIT researchers have now developed a machine-learning model that can make these predictions in less than a second, with high accuracy. Their model could make it easier for chemists to design chemical reactions that could generate a variety of useful compounds, such as pharmaceuticals or fuels.

    “We’d like to be able to ultimately design processes to take abundant natural resources and turn them into molecules that we need, such as materials and therapeutic drugs. Computational chemistry is really important for figuring out how to design more sustainable processes to get us from reactants to products,” says Heather Kulik, the Lammot du Pont Professor of Chemical Engineering, a professor of chemistry, and the senior author of the new study.

    Former MIT graduate student Chenru Duan PhD ’22, who is now at Deep Principle; former Georgia Tech graduate student Guan-Horng Liu, who is now at Meta; and Cornell University graduate student Yuanqi Du are the lead authors of the paper, which appears today in Nature Machine Intelligence.

    Better estimates

    For any given chemical reaction to occur, it must go through a transition state, which takes place when it reaches the energy threshold needed for the reaction to proceed. These transition states are so fleeting that they’re nearly impossible to observe experimentally.

    As an alternative, researchers can calculate the structures of transition states using techniques based on quantum chemistry. However, that process requires a great deal of computing power and can take hours or days to calculate a single transition state.

    “Ideally, we’d like to be able to use computational chemistry to design more sustainable processes, but this computation in itself is a huge use of energy and resources in finding these transition states,” Kulik says.

    In 2023, Kulik, Duan, and others reported on a machine-learning strategy that they developed to predict the transition states of reactions. This strategy is faster than using quantum chemistry techniques, but still slower than what would be ideal because it requires the model to generate about 40 structures, then run those predictions through a “confidence model” to predict which states were most likely to occur.

    One reason why that model needs to be run so many times is that it uses randomly generated guesses for the starting point of the transition state structure, then performs dozens of calculations until it reaches its final, best guess. These randomly generated starting points may be very far from the actual transition state, which is why so many steps are needed.

    The researchers’ new model, React-OT, described in the Nature Machine Intelligence paper, uses a different strategy. In this work, the researchers trained their model to begin from an estimate of the transition state generated by linear interpolation — a technique that estimates each atom’s position by moving it halfway between its position in the reactants and in the products, in three-dimensional space.

    “A linear guess is a good starting point for approximating where that transition state will end up,” Kulik says. “What the model’s doing is starting from a much better initial guess than just a completely random guess, as in the prior work.”

    Because of this, it takes the model fewer steps and less time to generate a prediction. In the new study, the researchers showed that their model could make predictions with only about five steps, taking about 0.4 seconds. These predictions don’t need to be fed through a confidence model, and they are about 25 percent more accurate than the predictions generated by the previous model.

    “That really makes React-OT a practical model that we can directly integrate to the existing computational workflow in high-throughput screening to generate optimal transition state structures,” Duan says.

    “A wide array of chemistry”

    To create React-OT, the researchers trained it on the same dataset that they used to train their older model. These data contain structures of reactants, products, and transition states, calculated using quantum chemistry methods, for 9,000 different chemical reactions, mostly involving small organic or inorganic molecules.

    Once trained, the model performed well on other reactions from this set, which had been held out of the training data. It also performed well on other types of reactions that it hadn’t been trained on, and could make accurate predictions involving reactions with larger reactants, which often have side chains that aren’t directly involved in the reaction.

    “This is important because there are a lot of polymerization reactions where you have a big macromolecule, but the reaction is occurring in just one part. Having a model that generalizes across different system sizes means that it can tackle a wide array of chemistry,” Kulik says.

    The researchers are now working on training the model so that it can predict transition states for reactions between molecules that include additional elements, including sulfur, phosphorus, chlorine, silicon, and lithium.

    “To quickly predict transition state structures is key to all chemical understanding,” says Markus Reiher, a professor of theoretical chemistry at ETH Zurich, who was not involved in the study. “The new approach presented in the paper could very much accelerate our search and optimization processes, bringing us faster to our final result. As a consequence, also less energy will be consumed in these high-performance computing campaigns. Any progress that accelerates this optimization benefits all sorts of computational chemical research.”

    The MIT team hopes that other scientists will make use of their approach in designing their own reactions, and have created an app for that purpose.

    “Whenever you have a reactant and product, you can put them into the model and it will generate the transition state, from which you can estimate the energy barrier of your intended reaction, and see how likely it is to occur,” Duan says.

    The research was funded by the U.S. Army Research Office, the U.S. Department of Defense Basic Research Office, the U.S. Air Force Office of Scientific Research, the National Science Foundation, and the U.S. Office of Naval Research.

    Source: Read More 

    Facebook Twitter Reddit Email Copy Link
    Previous ArticleBest antivirus for Mac in 2025: I tested your top software options
    Next Article Clair Obscur: Expedition 33 review and Metacritic roundup — Here’s what critics say about this gorgeous turn-based RPG

    Related Posts

    Repurposing Protein Folding Models for Generation with Latent Diffusion
    Artificial Intelligence

    Repurposing Protein Folding Models for Generation with Latent Diffusion

    July 23, 2025
    Artificial Intelligence

    Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

    July 23, 2025
    Leave A Reply Cancel Reply

    For security, use of Google's reCAPTCHA service is required which is subject to the Google Privacy Policy and Terms of Use.

    Continue Reading

    Here’s a faster way to download files on Linux – without a web browser

    News & Updates

    Salesforce Health Cloud Demo: Provider Search & Network Management in Action

    Development

    L’adozione di RISC-V nelle distribuzioni Enterprise Linux

    Linux

    Steam Deck seems to have quietly gained support for another popular online game — no anticheat block anymore

    News & Updates

    Highlights

    ToyMaker’s Playbook: Cisco Talos Exposes IAB Tactics Leading to Cactus Ransomware

    April 24, 2025

    ToyMaker’s Playbook: Cisco Talos Exposes IAB Tactics Leading to Cactus Ransomware

    Image: Cisco Talos
    Cisco Talos’ 2023 incident response report unveils the operations of “ToyMaker,” a financially motivated Initial Access Broker (IAB) whose behind-the-scenes activity opened the floo …
    Read more

    Published Date:
    Apr 25, 2025 (1 hour, 55 minutes ago)

    Vulnerabilities has been mentioned in this article.

    CVE-2025-22604

    CVE-2022-46169

    Everything you need to know about Lottie animations

    April 28, 2025

    Making Animations Smarter with Data Binding

    July 15, 2025

    CVE-2025-49015 – “Couchbase .NET SDK TLS Hostname Verification Weakness”

    June 18, 2025
    © DevStackTips 2025. All rights reserved.
    • Contact
    • Privacy Policy

    Type above and press Enter to search. Press Esc to cancel.