• About Us
  • Disclaimer
  • Contact Us
  • Privacy Policy
Saturday, July 12, 2025
mGrowTech
No Result
View All Result
  • Technology And Software
    • Account Based Marketing
    • Channel Marketing
    • Marketing Automation
      • Al, Analytics and Automation
      • Ad Management
  • Digital Marketing
    • Social Media Management
    • Google Marketing
  • Direct Marketing
    • Brand Management
    • Marketing Attribution and Consulting
  • Mobile Marketing
  • Event Management
  • PR Solutions
  • Technology And Software
    • Account Based Marketing
    • Channel Marketing
    • Marketing Automation
      • Al, Analytics and Automation
      • Ad Management
  • Digital Marketing
    • Social Media Management
    • Google Marketing
  • Direct Marketing
    • Brand Management
    • Marketing Attribution and Consulting
  • Mobile Marketing
  • Event Management
  • PR Solutions
No Result
View All Result
mGrowTech
No Result
View All Result
Home Al, Analytics and Automation

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

Josh by Josh
July 4, 2025
in Al, Analytics and Automation
0
New model predicts a chemical reaction’s point of no return | MIT News
0
SHARES
0
VIEWS
Share on FacebookShare on Twitter



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_link

READ ALSO

New AI system uncovers hidden cell subtypes, boosts precision medicine | MIT News

From Perception to Action: The Role of World Models in Embodied AI Systems

Related Posts

New AI system uncovers hidden cell subtypes, boosts precision medicine | MIT News
Al, Analytics and Automation

New AI system uncovers hidden cell subtypes, boosts precision medicine | MIT News

July 12, 2025
From Perception to Action: The Role of World Models in Embodied AI Systems
Al, Analytics and Automation

From Perception to Action: The Role of World Models in Embodied AI Systems

July 12, 2025
Mistral AI Releases Devstral 2507 for Code-Centric Language Modeling
Al, Analytics and Automation

Mistral AI Releases Devstral 2507 for Code-Centric Language Modeling

July 11, 2025
Medical Image Annotation and Labeling Services Guide 2025
Al, Analytics and Automation

Medical Image Annotation and Labeling Services Guide 2025

July 11, 2025
AI shapes autonomous underwater “gliders” | MIT News
Al, Analytics and Automation

AI shapes autonomous underwater “gliders” | MIT News

July 11, 2025
NVIDIA AI Released DiffusionRenderer: An AI Model for Editable, Photorealistic 3D Scenes from a Single Video
Al, Analytics and Automation

NVIDIA AI Released DiffusionRenderer: An AI Model for Editable, Photorealistic 3D Scenes from a Single Video

July 10, 2025
Next Post
Why millennials can’t recreate “90s summer” for their kids

Why millennials can’t recreate “90s summer” for their kids

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

POPULAR NEWS

Communication Effectiveness Skills For Business Leaders

Communication Effectiveness Skills For Business Leaders

June 10, 2025
7 Best EOR Platforms for Software Companies in 2025

7 Best EOR Platforms for Software Companies in 2025

June 21, 2025
Eating Bugs – MetaDevo

Eating Bugs – MetaDevo

May 29, 2025
Top B2B & Marketing Podcasts to Lead You to Succeed in 2025 – TopRank® Marketing

Top B2B & Marketing Podcasts to Lead You to Succeed in 2025 – TopRank® Marketing

May 30, 2025
App Development Cost in Singapore: Pricing Breakdown & Insights

App Development Cost in Singapore: Pricing Breakdown & Insights

June 22, 2025

EDITOR'S PICK

New Orleans Local’s Guide: Food, Music, and More

New Orleans Local’s Guide: Food, Music, and More

June 8, 2025
Search Live with voice in AI Mode on Google Search

Search Live with voice in AI Mode on Google Search

June 18, 2025
How I Landed a Global LinkedIn Keynote in Vancouver: A Speaker’s Journey from Backyard Video to International Stage

How I Landed a Global LinkedIn Keynote in Vancouver: A Speaker’s Journey from Backyard Video to International Stage

May 28, 2025
Guide to more reach on social media + free tool

Guide to more reach on social media + free tool

June 6, 2025

About

We bring you the best Premium WordPress Themes that perfect for news, magazine, personal blog, etc. Check our landing page for details.

Follow us

Categories

  • Account Based Marketing
  • Ad Management
  • Al, Analytics and Automation
  • Brand Management
  • Channel Marketing
  • Digital Marketing
  • Direct Marketing
  • Event Management
  • Google Marketing
  • Marketing Attribution and Consulting
  • Marketing Automation
  • Mobile Marketing
  • PR Solutions
  • Social Media Management
  • Technology And Software
  • Uncategorized

Recent Posts

  • Sequoia bets on silence | TechCrunch
  • Google Seems More Biased Towards Big Brands Than ChatGPT and Perplexity
  • Bringing AI Mode to Circle to Search for follow-ups, and gaming help
  • New Incremental Attribution Details – Jon Loomer Digital
  • About Us
  • Disclaimer
  • Contact Us
  • Privacy Policy
No Result
View All Result
  • Technology And Software
    • Account Based Marketing
    • Channel Marketing
    • Marketing Automation
      • Al, Analytics and Automation
      • Ad Management
  • Digital Marketing
    • Social Media Management
    • Google Marketing
  • Direct Marketing
    • Brand Management
    • Marketing Attribution and Consulting
  • Mobile Marketing
  • Event Management
  • PR Solutions

Are you sure want to unlock this post?
Unlock left : 0
Are you sure want to cancel subscription?