META has released OMOL25, the largest open data set of AI-driven chemistry to date, and introduced UMA, a universal AI model designed to predict the chemical properties of molecules and materials.
OMOL25 contains data from over 100 million high-precision molecular calculations. This exceeds the size of the previous open dataset in the field. According to Meta, it took over 6 billion hours of supercomputing time to generate the dataset. The dataset covers a wide range of molecules, ranging from small organic compounds and biomolecules (such as protein fragments and DNA segments) to metal complexes and electrolytes. It also includes charged and spin states, multiple conformations (spatial arrangements), and chemical reactions.
The goal is to help AI models learn how molecules work in a wide range of scenarios. OMOL25 can be used openly for drug discovery, battery material development, and Catalyst Research. In addition to energy and force values, the dataset includes additional details such as charge distribution, orbitals, and other chemical properties.
The OMOL25 dataset is available for embracing your face.
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UMA: Universal AI Models of Molecules and Materials
Alongside the OMOL25, Meta is launching UMA (Atoms Universal Model), a new AI model trained on OMOL25 and other datasets. UMA can predict chemical properties at the atomic level, and according to Meta, it is done much faster than traditional approaches.
Unlike previous methods that require specialized models for each task, UMA handles a wide range of applications, from molecular simulations (such as drug discovery) to materials and catalytic research. Built on modern graph neural networks, it combines speed and precision using a “mixed linear expert” architecture. In benchmark tests, UMA achieved results previously only possible with specially tweaked models.
UMA allows you to complete simulations and calculations that once took several days in seconds. Meta says researchers can be screened for thousands of new molecules for potential as drugs and battery materials before lab integration is needed.
The UMA model can be used with a face hug.
A new way to speed AI simulation
Traditional AI models typically require a large amount of training data to generate new molecular structures. Meta is currently introducing a new method called “Admint Sampling,” which allows AI models to learn and propose new structures even when no real examples exist.
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The technique is based on concepts from stochastic control theory, and Meta’s team states that it is particularly suitable for simulating molecules. Adjunctive sampling allows rapid search of many structural variants in several calculations.
In early testing, this method was able to generate molecular structures that are not only consistent but often outperform classic software performance, especially for molecules with many flexible components.
Models, code, and other information is available through Face and Github embrace.
Despite these advances, Meta points out that there are still open challenges. Some areas of chemistry, such as polymers, specific metals, or complex protonation states, are not yet fully covered. AI models also need to improve their ability to predict charge, spin, and long-range interactions.
