Accelerating science with AI

It can take years for humans to solve complex scientific problems. With AI, it can take a fraction of the time.

Dr. Shuiwang Ji, a professor in the Department of Computer Science and Engineering at Texas A&M University and a leading expert in the emerging field of AI for science and engineering — commonly referred to as AI4Science — is at the forefront of using AI to accelerate scientific problem solving. 

Ji, along with other Texas A&M researchers, has recently published a paper in Foundations and Trends in Machine Learning outlining the uses and benefits of AI4Science. This collaborative paper features more than 60 authors from 15 universities, and contains over 500 pages of information on using AI for science. 

The paper highlights the importance of using AI to solve complex equations, which can be applied to many different areas of science and engineering. For example, the famed Schrodinger’s equation can be solved with AI, improving efficiency and accuracy in many research areas, including drug discovery, material design, battery materials, and catalyst design.

“The goal of natural sciences is to understand the world on different temporal and physical scales, leading to three main systems: quantum, atomic, and continuum,” said Ji, who is also a Presidential Impact Fellow and Chancellor EDGES Fellow. “The fundamentals of these systems are ruled by differential equations, but the complexity of these equations significantly increases as the systems grow.” 

These differential equations, such as Schrodinger’s, can be solved analytically on a small scale, testing the dynamics of two particles, like electrons. As the number of particles being tested increases, the complexity of equations grows exponentially, making them impossible to solve for any systems of practically useful sizes.

By implementing AI to solve these equations, large-scale systems can be analyzed effectively in a fraction of the time it would take with traditional methods. 

“We are using AI to accelerate our understanding of science and design better engineering systems,” said Ji. Ji is also director of Texas A&M’s Research in Artificial Intelligence for Science and Engineering (RAISE) Initiative. With over 85 faculty members from Texas A&M, the RAISE Initiative is promoting collaborative research in AI.

“I have a curiosity for fundamental science, as it drives many areas of science and engineering research thanks to shared underlying principles and governing equations,” said Ji.

Funding for this research is administered by the Texas A&M Engineering Experiment Station (TEES), the official research agency for Texas A&M Engineering.

 By Alyssa Schaechinger, Texas A&M University College of Engineering

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