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[1]. Towards the Rational Design of Mid-Infrared Nonlinear Optical Materials with Targeted Properties via a Multi-Level Data-Driven Approach
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[2]. Unbiased Screening of Novel Infrared Nonlinear Optical Materials with High Thermal Conductivity: Long-neglected Nitrides and Popular Chalcogenides
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[3]. Target-Driven Design of Deep-UV Nonlinear Optical Materials via Interpretable Machine Learning
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[4]. A prediction-driven database to enable rapid discovery of nonlinear optical materials
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[5]. New Ways to Discover Novel Nonlinear Optical Materials: Scaling Machine Learning with Chemical Descriptors Information
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[6]. Graph neural network guided design of novel deep-ultraviolet optical materials with high birefringence
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[7]. A Machine-Learning-Assisted Crystalline Structure Prediction Framework To Accelerate Materials Discovery
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[8]. Functional Modules Map of Unexplored Chemical Space: Guiding the Discovery of Giant Birefringent Materials
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