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Articles by Zekun Ren
Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI) - npj Computational Materials
Abstract Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset.
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Highlights • We present an inverse-design framework for generative design of materials • The invertible crystal representation includes real- and reciprocal-space features • We generate 142 new crystals with target ground- and excited-state properties • We validate our results with first-principles calculations Summary Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties.
Two-step machine learning enables optimized nanoparticle synthesis - npj Computational Materials
Abstract In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum.
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
Abstract X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns.
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