Bridging Computer Science and Molecular Science: Caitlin Whitter’s Journey as a MolSSI Software Fellow

Caitlin Whitter didn’t take the traditional path into molecular science. As a Ph.D. student in computer science at Purdue University, she always knew she wanted to work on interdisciplinary research. Instead of sticking purely to computing, she was drawn to solving complex problems in the natural sciences. That curiosity led her to computational molecular science, where she now applies machine learning to better understand molecular and atomic properties.

Diving Into Computational Science
Whitter’s research focuses on building machine learning models that can accurately and efficiently predict molecular and atomic properties using quantum-mechanical datasets. These predictions have big implications for fields like drug discovery, materials science, and chemistry, where understanding molecular interactions is key. By developing interpretable machine learning pipelines, she hopes to make these computational tools more effective and accessible for scientists.

The MolSSI Fellowship Experience
Becoming a MolSSI Software Fellow has been a game-changer for Whitter. The fellowship gave her the funding to focus entirely on her MolSSI project for a year, allowing her to dive deeper into her research without worrying about additional financial support. One of the highlights of the experience has been working with her MolSSI Software Scientist mentor, Dr. Benjamin Pritchard. Their bi-weekly meetings have provided valuable insights, especially when it comes to working with molecular science datasets. The MolSSI summer bootcamp was another major plus—it was not only a great learning experience but also a chance to connect with other fellows and discuss research from different perspectives.

Looking Ahead
As she works toward finishing her Ph.D., Whitter is excited about continuing research in machine learning and computational science. Whether in academia, industry, or national labs, she hopes to contribute to cutting-edge developments in the field. Outside of research, she enjoys singing in a musical ensemble at Purdue, reading, exploring new places, and spending time with friends and family. Looking back, Whitter is proud of the collaborations she’s been a part of and the opportunities she’s had to share her research with a wider audience. Being selected as a MolSSI Software Fellow has been one of the most rewarding experiences of her Ph.D. journey, and she highly encourages others interested in computational molecular science to consider applying.

To connect with Caitlin Whitter or learn more about her research, visit her LinkedIn profile