Researchers at Sandia National Laboratories and Brown University have developed a transformative method to accelerate computer simulations, significantly speeding up scientific research across multiple fields. This new approach, recently published in npj Computational Materials, has the potential to enhance the performance of nearly any type of simulation—from drug discovery to space exploration.
“What’s remarkable is that, from the user’s perspective, there’s no difference in how you run your simulation,” said Rémi Dingreville, a Sandia researcher. “The difference lies in the time it takes to get your results—it’s dramatically faster.”
In a demonstration of the accelerator, the team achieved materials science simulations that ran 16 times faster than conventional methods. Beyond materials science, the tool promises wide-ranging applications, from climate change research to autonomous vehicle navigation and hardware acceleration. “This universal approach could lead to more efficient and sustainable technologies,” added Vivek Oommen of Brown University, the study’s lead author.
A Breakthrough with Broad Impacts
While Dingreville has previously engineered simulations to run up to 40,000 times faster, the current 16-fold increase is unique due to its broad applicability. Unlike other accelerators that are limited to specific problem types, this new method can enhance various simulation tools in diverse fields.
“Physics, chemistry, weather prediction—it doesn’t matter. Our approach can accelerate simulations across the board,” Dingreville explained. The breakthrough could revolutionize how scientists approach their research, cutting down computational time and costs while improving efficiency.
Funded by Sandia’s Laboratory Directed Research and Development program, and supported by both the Center for Integrated Nanotechnologies and Brown’s Center for Computation and Visualization, the research is poised to make a significant impact.
Enabling New Research Horizons
In addition to saving time and resources, this new tool opens up the possibility of modeling slow-developing phenomena that were previously too time-consuming to simulate. For example, studying processes like glacial melting could now become feasible with far less computational strain.
“The current standard involves direct numerical solvers, which, while accurate, are incredibly slow,” Dingreville noted. By integrating traditional numerical methods with artificial intelligence, the researchers hope to overcome such challenges.
Looking ahead, the team is eager to see their methods applied in fields such as energy, biotechnology, and environmental science. “I’d love to see this used in geoscience,” Dingreville added, emphasizing the broad potential for the technology to reshape research across domains.
This breakthrough represents a major leap forward in simulation technology, offering scientists a powerful tool to accelerate discoveries and tackle complex problems more efficiently than ever before.
By Impact Lab
