CrySPAI: A new Crystal Structure Prediction Software Based on Artificial Intelligence

TL;DR

CrySPAI is an AI-driven software that predicts stable inorganic crystal structures from chemical compositions by integrating evolutionary algorithms, DFT calculations, and deep neural networks within a distributed framework.

Contribution

CrySPAI introduces a novel AI-based crystal structure prediction package combining multiple modules for efficient and accurate predictions of inorganic materials.

Findings

Successfully predicts stable crystal structures for various inorganic compounds.

Integrates AI, DFT, and evolutionary algorithms into a unified framework.

Automated workflow enhances prediction efficiency and scalability.

Abstract

Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most of these approaches are limited to predicting specific systems, which hinders their application to unknown or unexplored domains. In this paper, we present CrySPAI, a crystal structure prediction package developed using artificial intelligence (AI) to predict energetically stable crystal structures of inorganic materials given their chemical compositions. The software consists of three key modules, an evolutionary optimization algorithm (EOA) that searches for all possible crystal structure configurations, density functional theory (DFT) that provides the accurate energy values for these structures, and a deep neural network (DNN) that learns the relationship between crystal structures and their corresponding energies. To optimize the process across these modules, a distributed framework is implemented to parallelize tasks, and an automated workflow has been integrated into CrySPAI for seamless execution. This paper reports the development and implementation of AI AI-based CrySPAI Crystal Prediction Software tool and its unique features.