IA mapeia reações em sólidos em minutos e ajuda a criar materiais avançados
by Michael Matz, Lawrence Berkeley National Laboratory
edited by Lisa Lock, reviewed by Robert Egan
This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:
peer-reviewed publication
A research team at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) has successfully demonstrated an AI modeling approach that accurately and rapidly predicts how reactions between solid materials unfold over time. It is the first predictive model that accounts for how atoms travel through materials during solid-state reactions. Importantly, its predictions provide practical insights into the best recipes for making advanced materials.
"Our new model enables materials scientists and industry stakeholders to make promising new materials dramatically faster—and with higher purity and yield," said Kristin Persson, one of the study's authors. Persson is a senior scientist at Berkeley Lab and a professor in materials science and engineering at the University of California, Berkeley.
Persson added, "The model can help accelerate the advancement of solid materials to cost-effective manufacturing and commercialization. It closes the gap between material discovery and new technologies that benefit society."
The research is published in Nature Materials.
Synthesis: A major bottleneck in material discovery
Technological innovation often relies on the availability of inorganic solid materials that perform valuable functions, such as storing energy, emitting light and catalyzing chemical reactions. Discovering better materials can be a critical step toward commercialization of a broad range of next-generation technologies, such as batteries, sensors and medical devices.
Thanks to advances in AI-driven computational tools, scientists can identify materials with desirable properties for various technological applications. Yet, synthesizing these materials can be extremely difficult. Mixing and heating powders to high temperatures—a common approach to synthesizing inorganic solids—often yields a mixture of unexpected compounds rather than the desired product. It can take weeks to even years of trial-and-error experimentation to figure out the right recipe to make these materials.
How models can help
Computational models can potentially address this synthesis challenge. Existing models predict the outcome of synthesis reactions based on thermodynamics. In other words, they account for which reactions are the most energetically favorable and yield the most stable products.
"Thermodynamics essentially refers to how much two solids 'want' to react with each other," said Persson.
But thermodynamics-based approaches have struggled to make accurate predictions. That's because they have not accounted for how atoms travel through materials as reactions proceed. This factor—known as kinetics—can play a key role in the outcome of solid-state reactions.
"Atoms in solids move more slowly than in liquids, making it harder for them to reach reaction sites," said Persson. "Even if two materials want to react with each other, they may not ultimately do so if the right number of atoms cannot move to the places where reactions occur."
Because of kinetic factors, solid-state reactions often require heating the starting powders to temperatures as high as 800°C (1,470°F). This makes the atoms more mobile.
How the new model works
In addition to considering thermodynamics, the research team's approach incorporates an innovative machine learning model trained to predict how quickly atoms travel through a material. The model's design is based on the researchers' hypothesis that the reaction interface between two solids is highly disordered.
"Imagine the chaotic scene when a big concert is over, and crowds of people are exiting the arena," said Persson. "That's how atoms are at reaction sites. For reactions to occur, atoms need to move through extremely disordered regions."
Inputs into the model include the starting materials, their ratios and the temperature ramp-up. In just minutes, the model simulates the full reaction pathway from start to finish, revealing both intermediate and final products as well as impurities.
Strong agreement with experimental data
The researchers tested the model on barium-titanium oxides. This is a family of well-studied solid materials with important technological applications in electronics.
"We deliberately chose these materials because the reactions are strongly influenced by kinetics," said Persson. "When you heat mixtures of barium- and titanium-containing powders, the new solid compounds that form are extremely close in thermodynamic stability. They all want to form to the same degree, so the reaction outcomes are determined by how easily the atoms can travel to the reaction interface."
The model simulated reaction pathways of various ratios of barium oxide and titanium dioxide at different temperatures over time. The researchers then compared the results with decades of experimental synthesis data from the scientific literature and found strong agreement.
"Remarkably, the model was correct for the full sequence of events, including intermediate compounds, final products and formation of impurities," said Persson.
Taking the model to the next level
The researchers trained the machine learning model specifically to predict the kinetics of barium-titanium oxides. The next research step is to train and demonstrate the model on other classes of solid-state materials.
Eventually, the team plans to use large kinetics datasets to train a foundation model that users can apply to virtually any solid-state material. This would make the model broadly relevant across numerous technologies and industries.
Vir Karan et al, Ion correlations explain kinetic selectivity in diffusion-limited solid-state synthesis reactions, Nature Materials (2026). DOI: 10.1038/s41563-026-02596-5
Journal information: Nature Materials
Provided by Lawrence Berkeley National Laboratory
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