Exploring Crystal Structure Prediction with Quality-Diversity Algorithms
Quality-Diversity algorithms are proposed for crystal structure prediction to identify diverse high-performing solutions efficiently, enabling the discovery of novel materials. The approach combines machine-learning surrogate models and neural networks to predict crystal properties and optimize material structures.
Accurate Crystal Structure Prediction of New 2D Hybrid Organic Inorganic Perovskites
The author presents a machine learning interatomic potential for predicting the structure of new 2D hybrid organic-inorganic perovskites, demonstrating accuracy and efficiency in structure prediction.
Harnessing Multi-Objective Quality-Diversity to Discover Diverse and High-Performing Crystal Structures
Multi-Objective Quality-Diversity algorithms can discover a diverse collection of crystal structures that achieve different trade-offs between stability, magnetism, conductivity, and deformation resistance.
2D Hybrid Organic Inorganic Perovskites Crystal Structure Prediction Study
기계 학습 상호 원자력 잠재력을 사용한 새로운 2D 하이브리드 유기 무기 페로브스카이트의 정확한 결정 구조 예측
AlphaCrystal-II: A Deep Learning Approach for Accurate Crystal Structure Prediction from Material Compositions
AlphaCrystal-II, a novel deep learning model, can accurately predict the crystal structure of materials solely from their chemical compositions by exploiting the abundant inter-atomic interaction patterns found in known crystal structures.
Automated High-Throughput Screening of Organic Crystal Structures Using Population-Based Sampling Methods and Open-Source Software
This paper introduces HTOCSP, an open-source software package that automates the prediction and screening of small organic molecule crystal structures using population-based sampling methods and existing open-source molecular modeling tools.