Materials Inference and Innovation
Accelerate materials science innovation by developing accurate and interpretable ML-driven methods
Our group focuses on developing novel informatic approaches of machine learning (ML) and applied
statistics to
accelerate the search and discovery of new materials and to better understand material structures
and
their
relationship
to material properties. The use of ML in materials science and engineering (MSE) is often hampered by a lack of
accuracy
and low interpretability of predictive models. In particular, the poor interpretability of most ML models
prevents
deeper mechanistic understanding, such as finding important constituents for desired material properties.
To
make
accurate predictions, ML methods further rely on an adequately large volume of standardized and unbiased
datasets
for
training and testing which also necessitates the integration of research data management (RDM) tools. Our goal
is to
develop ML models that provide both accurate prediction and good interpretability to increase our
understanding
the
link
between structure and properties of a material. This is of great importance to make critical advances in
data-driven
materials research: the development of accurate and interpretable AI-based methods will enable MSE researchers
to
better
understand the behavior and mechanisms of materials, design new and safe materials, and to explain relevant
material
properties that were previously poorly understood.
In particular, we are active in the following areas:
In particular, we are active in the following areas: