July 2022 Edition
Maria Chan, Computational scientist, PSE
One of the biggest barriers to women achieving their dreams can be the stereotypical expectations of others. This was the case for Maria Chan, a computational scientist in the Center for Nanoscale Materials (CNM) in the Physical Sciences and Engineering (PSE) directorate.
“My parents did not expect me to become a scientist, my math teacher in high school said I was ‘pretty good for a girl’, and some professors expressed surprise that I was good at physics,” said Chan. “One manager was startled that I work in theory and modeling. Another colleague told me that women should not be managers. I am not sure I have overcome these attitudes; it’s a work in progress.”
To motivate herself, Chan leans on her intrinsic need to solve puzzles, a desire to help accelerate scientific discovery, and the exciting potential for solving societal problems.
Chan uses quantum mechanical simulations and artificial intelligence/machine learning to understand and design materials. She enjoys the interdisciplinary nature of her work at Argonne, and its many collaborations.
“We bring together physics, chemistry, materials science, applied math, statistics, and other perspectives and approaches,” she explained.
Chan was inspired at a young age to pursue physics when she read a book on special relativity in her native Chinese. The book introduced her to the enticing idea that logical reasoning and mathematics can be used to carry out scientific thought experiments. “Counter to stereotypes, scientists don’t all wear lab coats and handle test tubes,” Chan said. When she inquired about a research position as an undergraduate, she faced mostly rejections, and one professor told her, “You know, not everyone can do research.” Fortunately, she found mentors in notable professors Nina Byers at UCLA and later Millie Dresselhaus at MIT, who made a point of encouraging her and other women to pursue physics. “Without their support, I cannot imagine being able to become a scientist,” said Chan.
The best professional advice she thinks she received came from her PhD advisor who pointed out to her how important it is to avoid a defensive reaction when someone criticizes her work. “If the reviewer did not seem to understand the merits of our work, that means we could have explained it better,” said Chan. “We need to think about it from the other person’s perspective and address their concerns.”