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Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Presentation Title Structure Prediction and Property-based Optimization of Molecular Crystals with GAtor
Author(s) Noa Marom
On-Site Speaker (Planned) Noa Marom
Abstract Scope Molecular crystal structure prediction is challenging due to the high accuracy required for the small energy differences between polymorphs and the high dimensionality of the configuration space. To predict the structure of molecular crystals we combine quantum mechanical simulations with genetic algorithm (GA) optimization and machine learning in the GAtor package. GAtor offers a variety of crossover and mutation operators, designed for molecular crystals, to create offspring by combining/modifying the structural genes of parent structures. Massive parallelization is achieved by spawning several GA replicas that run in parallel and share a common population. GAtor performs evolutionary niching by using machine learning for dynamic clustering of the population. A cluster-based fitness function is then used to steer the GA to undersampled low-energy regions of the configuration space. This helps overcome initial pool biases and selection biases. Property-based fitness functions are used to search for potentials polymorphs with enhanced electronic properties.
Proceedings Inclusion? Definite: At-meeting proceedings

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Phase Field Regularization for Optimal Grain Reconstruction of Noisy Images
Python For Glass Genomics (PyGGi): A Machine Learning Package to Predict the Properties of Glasses
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