Contact Map based Crystal Structure Prediction using Global Optimization
Jianjun Hu, Wenhui Yang, Rongzhi Dong, Yuxin Li, Xiang Li, Shaobo Li

TL;DR
This paper introduces CMCrystal, a global optimization algorithm that uses atomic contact maps to improve crystal structure prediction, demonstrating potential but also highlighting the need for additional constraints.
Contribution
The paper presents a novel approach leveraging atomic contact maps for crystal structure prediction, inspired by protein structure methods, and evaluates its effectiveness across multiple algorithms.
Findings
Atomic contact maps can aid in crystal structure reconstruction.
Success varies depending on the material and constraints used.
Additional constraints are necessary for accurate predictions in some cases.
Abstract
Crystal structure prediction is now playing an increasingly important role in discovery of new materials. Global optimization methods such as genetic algorithms (GA) and particle swarm optimization (PSO) have been combined with first principle free energy calculations to predict crystal structures given composition or only a chemical system. While these approaches can exploit certain crystal patterns such as symmetry and periodicity in their search process, they usually do not exploit the large amount of implicit rules and constraints of atom configurations embodied in the large number of known crystal structures. They currently can only handle crystal structure prediction of relatively small systems. Inspired by the knowledge-rich protein structure prediction approach, herein we explore whether known geometric constraints such as the atomic contact map of a target crystal material can…
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Taxonomy
TopicsMachine Learning in Materials Science · Protein Structure and Dynamics · Computational Drug Discovery Methods
