Interpretable Surrogate Learning for Electronic Material Generation

Despite
many accessible AI models that have been developed, it
is an open challenge to fully exploit interpretable insights to enable
effective materials design and develop materials with desired properties
for target applications. Here, we introduce an interpretable surrogate
learning framework that can actively design and generate electronic
materials (EMGen), akin to producing updated materials with requirements
by screening all possible elements and fractions. Taking the materials
system with required band gaps as a case study, EMGen exhibits a benchmarking
predictive error and a running time of 1.7 min for designing and producing
a structure with a desired band gap. Using EMGen, we establish a large
hybrid functional band gap database, and more uplifting is that the
proposed EMGen effectively designs GaxOy with a wide band gap (>5.0 eV)
for
deep ultraviolet (DUV) optoelectronic devices, enabling a breakthrough
extension of the applicability of GaxOy films in photodetectors to DUV light below
240 nm. The augmented band gap also helps improve the breakdown voltage
and the heat resilience performance of the amorphous GaxOy film, thereby achieving
considerable potential within the realm of power electronics applications.
The proposed EMGen, as a specialized, interpretable AI model for the
generation of electronic materials, is demonstrated to be an essential
tool for on-demand semiconductor materials design.