Evolutive screening of candidates for new materials using genetic algorithms and deep learning

Authors

  • Tatis Posada, David
  • Ramos Álamo, María
  • Sierra, Heidy
  • Arzuaga, Emmanuel

DOI:

https://doi.org/10.18687/LACCEI2023.1.1.651

Keywords:

genetic algorithm, deep learning, materials discovery, materials properties, combinatorial screening

Abstract

Different mechanisms are used for the discovery of materials. These include creating a material by trial-and-error process without knowing its properties. Other methods are based on computational simulations or mathematical and statistical approaches, such as Density Functional Theory (DFT). A well-known strategy combines elements to predict their properties and selects a set of those with the properties of interest. Carrying out exhaustive calculations to predict the properties of these found compounds may require a high computational cost. Therefore, there is a need to create methods for identifying materials with a desired set of properties while reducing the search space and, consequently, the computational cost. In this work, we present a genetic algorithm that can find a higher percentage of compounds with specific properties than state-of-the-art methods, such as those based on combinatorial screening. Both methods are compared in the search for ternary compounds in an unconstrained space, using a Deep Neural Network (DNN) to predict properties such as formation enthalpy, band gap, and stability we will focus on formation enthalpy. As a result, we provide a genetic algorithm capable of finding up to 60% more compounds with atypical values of properties, using DNNs for their prediction.

Downloads

Published

2024-04-16

Issue

Section

Articles