A recent editorial by Dr. Byung-Jun Yoon and co-authors Francis J. Alexander, Meifeng Lin, and Xiaoning Qian published in Patterns from Cell Press discusses how AI/ML, data-driven modeling, and scientific computing can significantly accelerate scientific advances and novel discoveries.

Francis J. Alexander, Meifeng Lin, Xiaoning Qian, and Byung-Jun Yoon, “Accelerating scientific discoveries through data-driven innovations,” Patterns, volume 4, issue 11, 100876, Nov. 10, 2023. https://doi.org/10.1016/j.patter.2023.100876

Developing artificial intelligence (AI) and machine learning (ML) methods that can accelerate scientific discoveries and advance science has become one of the important research directions for the AI/ML research community. It has been gaining increasing attention from researchers in diverse scientific areas, including biomedical science, materials science, climate science, physics, chemistry, and many others. Data-driven AI/ML innovations to enable reliable predictions and optimal decision-making for scientific discoveries face several critical challenges, among which are high system complexity, large search space, incomplete knowledge, and small data, all of which demand novel strategies to effectively address them. Meeting these challenges and thereby accelerating scientific discoveries and industrial innovations, calls for research that can take full advantage of the latest advances in AI/ML to integrate data-driven techniques with scientific knowledge and is able to execute them in modern high-performance computing (HPC) environments at scale.

Those who are interested in learning more about the recent advances in relevant fields are referred to the Patterns Special Collection “Accelerating scientific discoveries through data-driven innovations”, which features articles that showcase the promising roles of AI/ML and data-driven modeling in accelerating scientific discoveries and may inspire the next wave of data-driven innovations in various scientific domains.