Machine Learning Enabled Tailor-Made Design of Application-Specific Metal–Organic Frameworks

In the development of advanced nanoporous materials,
one clear
and unavoidable challenge in hand is the sheer size (in principle,
infinite) of the materials space to be explored. While high-throughput
screening techniques allow us to narrow down the enormous-scale database
of nanoporous materials, there are still practical limitations stemming
from a costly molecular simulation in estimating a material’s
performance and the necessity of a sophisticated descriptor identifying
materials. With an attempt to transition away from the screening-based
approaches, this paper presents a computational approach combining
the Monte Carlo tree search and recurrent neural networks for the
tailor-made design of metal–organic frameworks toward the desired
target applications. In the demonstration cases for methane-storage
and carbon-capture applications, our approach showed significant efficiency
in designing promising and novel metal–organic frameworks.
We expect that this approach would easily be extended to other applications
by simply adjusting the reward function according to the target performance
property.