Probabilistic Graphical Models for Predicting Properties of New Materials Based on Their Composition and Structure

Probabilistic graphical model (PGMs) offer a powerful framework for modeling complex relationships between different components. By integrating information on the element composition and structural features, these models enable the inference of materials properties with a probabilistic perspective. This approach holds promising efforts towards accelerating materials discovery design, as it facilitates the predication of diverse materials characteristics, ranging from electronic and mechanical properties to thermal and optical behavior. The use of PGMs in materials science represents a sophisticated methodology for harnessing data-driven insights to guide the exploration of innovative materials with tailored functionalities. The purpose of this paper is to investigate literature for the exploitation of the data science concepts, big data and machine learning that yields computational intelligence. A literature review approach to understand the exploitation and use of computational intelligence in the leading-edge research and innovation of materials science. The findings illustrate that machine learning can be used to intricate chemical problems that otherwise would not be tractable. Leveraging PGMs presents a promising avenue for predicting the properties of new materials based on their composition and structure.