With well-studied battery materials, the simulation process is somewhat easier. They then compare the model with results from real-life experiments, manually tweaking different parts of the model, including the distances between atoms and the strength of chemical bonds, until the simulation matches real life. To create a computer model of these interactions, scientists first make a rough guess at a model using complex quantum mechanics calculations. “You need to know how every atom interacts with every other atom in your system,” Leon says. With these details, scientists can understand why certain combinations do better, guiding their search for high-performing materials.īut building accurate computer simulations is extremely time-intensive, taking years and sometimes even decades. These simulations act as virtual microscopes for batteries, zooming in to see how materials interact at an atomic level. Scientists investigating new battery materials generally use computer simulations to understand how different combinations of materials perform. Leveraging machine learning to research battery materials According to Gómez-Bombarelli, sometimes the two of them even discuss research in Spanish - a “rare treat.” That connection has empowered Leon to freely brainstorm ideas or talk through concerns with his advisor, enabling him to make significant progress in his research. “Because he’s from Spain and my parents are Peruvian, there’s a cultural ease with the way we talk,” Leon says. To help with the transition, Leon sought out a congenial research advisor and found one in Rafael Gómez-Bombarelli, an assistant professor in the Department of Materials Science and Engineering (DMSE). But the transition to a different field was “a really hard process,” Leon says, as he rushed to catch up to his peers. So, when Leon applied to graduate school at MIT, he switched fields to materials science, hoping to satisfy his curiosity. But he wanted to delve deeper, down to the level of atoms, to understand exactly where these behaviors come from.
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There, he learned how to model the behaviors of mechanical systems, such as a metal spring’s stiffness.
In college, Leon followed in his father’s and older brother’s footsteps to become a mechanical engineer, earning his bachelor’s degree at Texas A&M. Meanwhile, his mom worked outside the university, as a family counselor in a neighboring city. Growing up in College Station, Texas, home to Texas A&M University, where his father worked, many of Leon’s friends also had parents who were professors or affiliated with the university. Such batteries would not only improve the range of EVs, but they could also unlock potential in other high-power systems, such as solar energy systems that continuously deliver power, even at night.įrom a young age, Leon knew he wanted to pursue a PhD, hoping to one day become a professor of engineering, like his father. With his tool in hand, Leon plans to help search for new materials to enable the development of powerful and lightweight batteries. To accelerate this process, Leon is developing a machine-learning tool for scientists to automate one of the most time-consuming, yet key, steps in evaluating battery materials. Typically, however, it takes decades for scientists to thoroughly test new battery materials, says Pablo Leon, an MIT graduate student in materials science. One of their main challenges? Figuring out how to make extremely powerful but lightweight batteries. But to convince prospective buyers, manufacturers need to improve how far these cars can go on a single charge. It's what you would expect from a company run by neoliberal upper-middle class women.To help combat climate change, many car manufacturers are racing to add more electric vehicles in their lineups. Not to mention, you get to listen to the head of the department berate the vendors on a regular basis. They'll throw around euphemisms like "autonomous" and "entrepreneurial", but what they mean is that you're on your own and they're unwilling to work with you. One of the ladies was there for two months before the department made her want to change industries entirely. In the eight months I was there, four staff members resigned and there are only seven positions in the department.
The turnover really speaks for this passive-aggression. They will tell you one thing at your onboarding and completely back slide once you're past your 90 day evaluation. At the beginning, the leadership likes to act friendly and accommodating, but the two "leaders" of the department will talk amongst themselves about your performance but not give you any direct or meaningful feedback.
If you're joining the inventory management team, get ready to teach yourself everything.
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