Future-proofing Software for Chemical Discovery

With the new NSF grant, the team will work to make Amber more powerful using what are known as free energy simulations. The team is also coupling those with other modern advances in both software and hardware. That means making Amber seamlessly compatible with today’s machine learning algorithms and fastest supercomputers – as well as what tomorrow might offer.

“We’re going to deliver state-of-the-art software that you can use on big machines that’s flexible and easy to use,” Merz said. “This will allow anyone to come in and do their job with good accuracy.”

Aktulga is an associate professor in MSU’s Department of Computer Science and Engineering who has worked with a wide range of scientists and engineers, including nuclear physicists and electrical engineers, as well as chemists.

“They’re people with problems that need solutions. We can help deliver those with algorithm and software development,” Aktulga said. “It’s challenging, but it’s also very exciting. My collaborators do an excellent job of presenting their work understandably to computational scientists. We can understand their computational needs and what they’re trying to accomplish.”

The project’s broad vision appealed to York, a Henry Rutgers University Professor in the Department of Chemistry and Chemical Biology, who along with other members of his team was working toward such a scope but with the limitations of independent efforts aimed at different components.

“We were waiting for the opportunity and resources to make the big picture a reality for ourselves and the rest of the world,” York said, noting that the new grant allows for a combined-force approach.

The collaborative nature of the program, along with the NSF support, will also help keep talented students and postdoctoral researchers working in an academic setting, Merz added, noting that recent developments in scientific computation have been largely driven by industry including big names such as Google – a reflection of how well companies are equipped to attract talent and tackle big problems in this space.

Having developed Amber in academia for decades, Merz and York know the value of research experiences for diverse, energetic students and postdoctoral researchers in a learning environment. Merz would like to see more opportunities at universities given their track record of innovation, and the team is thankful for this support  from NSF’s Office of Cyberinfrastructure and Division of Chemistry.

“This is an example of something that allows us to have a bigger software vision in an academic setting,” Merz said. “These grants are really, really hard to get and they’re really, really important.”

This program is supported by the NSF (award nos. 2209717 and 2209718).