YSM Issue 96.2

Computer Science
FOCUS
TEACHING
MACHINE
LEARNING
PHOTOGRAPHY COURTESY OF PAUL-ALEXANDER LEJAS
Machine learning is often thought of as the silver bullet
for solving puzzles in science. With enough data in
any given subject, a model with impressive predictive
properties can be created to produce an abundance of helpful
information. But sometimes, machine learning isn’t perfect. In their
recent paper, Guannan Liu and his colleagues at the Schroers Lab
at Yale explore the limitations of machine learning models and how
we can incorporate human learning into new models to strengthen
their predictive power.
Liu, a Ph.D. candidate in Mechanical Engineering and Materials
Science, has spent most of his time at Yale studying machine learning
and its ability to solve complex materials science problems. The
study of glass-forming ability is a canonical example of one of these
problems. It is quantified by the minimum cooling rate required to
prevent the formation of undesired crystalline structures, resulting
in a glass with an amorphous atomic structure. Studying glassforming
ability by performing hands-on experiments in the lab
can be tedious, and this is where machine learning comes in to
potentially accelerate the process. “Simply put, machine learning is
trying to make inferences from data, and maybe predict something
from [that] data,” Liu said.
But there’s a catch—previous machine learning models designed
to predict the glass-forming ability of metallic glasses have fallen
short of providing useful insights on the subject. Metallic glasses
are alloys, which are made by combining two or more elements.
“You have to have meaningful features that describe the particular
alloy after the mixing of elements,” Liu said. Liu contextualized this
idea by offering an example. “For atomic size, it’s not the average
[element size] that matters but the difference in size,” Liu said.
“This allows space to be filled as much as possible, favorable for
glass formation.” Models that lack such information and instead
arbitrarily use statistical functions to construct features do not truly
capture the essence of the alloy’s glass-forming ability.
The other issue with the previous machine learning model for
the glass-forming ability of alloys was its limited capacity to make
new predictions. “[In] the previous model, usually the task was
interpolation—the model was only predicting things that were
www.yalescientific.org
Improving a machine learning
model to better predict
metallic glass formation
BY MAYA KHURANA
similar to the dataset,” Liu said. In other words, the model could not
make any predictions for new and unfamiliar data—it could only
work inside the bounds of the dataset.
To rectify these errors, Liu and his team worked on a new machine
learning model: one that incorporated scientific insights from
human learning. “Our model used extrapolation, [or] prediction
into unknown space,” Liu said. This way, they were able to align their
machine learning model with the reality that they have observed in
the lab. Take, for instance, the property of atomic size again. Larger
differences in size result in better glass-forming ability because the
atoms are able to pack in more tightly. It is properties such as these
that Liu and his team were better able to account for in their model,
and their approach worked. “We found that our model was actually
very successful in predicting glass-forming ability,” Liu said.
Unlike its predecessors, this model was much better at
extrapolation. “Our model can predict alloys that are more distinct
from the training set,” Liu said. “We concluded that physical insights
are really needed [to develop effective machine learning models].”
This project is far from the end of Liu’s work with machine
learning for materials science. He has three main goals moving
forward. “The first is to use machine learning to test [our] current
understanding of complex material science problems,” Liu said. It
can be hard to quantify the efficacy of foundational rules within
material science, so Liu plans to use machine learning to evaluate
these guiding principles. Secondly, Liu strives to combine machine
learning and high-throughput fabrication methods to discover
new metallic glasses.
Finally, Liu will investigate the contexts in which machine
learning can be helpful. His goal is to determine what kinds of
problems it can help solve and what circumstances limit its utility.
“[We want to] have a viewpoint that can be meaningful for the
community as to what machine learning is useful for [versus]
situations where machine learning would be hard to use,” Liu said.
This research will ensure that machine learning models are taking
all possible human insights into account while making inferences
from data. Clearly, machine learning can be an extremely valuable
tool if it is wielded skillfully. ■
May 2023 Yale Scientific Magazine 9