Guwon Jung
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View article: Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow
Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow Open
Machine learning (ML) methods provide a pathway to accurately predict molecular properties, leveraging patterns derived from structure-property relationships within materials databases. This approach holds significant importance in drug di…
View article: Predictive Modeling of High-Entropy Alloys and Amorphous Metallic Alloys Using Machine Learning
Predictive Modeling of High-Entropy Alloys and Amorphous Metallic Alloys Using Machine Learning Open
High entropy alloys and amorphous metallic alloys represent two distinct classes of advanced alloy materials, each with unique structural characteristics. Their emergence has garnered considerable interest across the materials science and …
View article: Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors
Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors Open
In the quest for advanced superconducting materials, the accurate prediction of critical temperatures (Tc) poses a formidable challenge, largely due to the complex interdependencies between superconducting properties and the chemical and s…
View article: Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials
Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials Open
Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environme…
View article: Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks
Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks Open
Molecular design depends heavily on optical properties for applications such as solar cells and polymer-based batteries. Accurate prediction of these properties is essential, and multiple predictive methods exist, from ab initio to …
View article: Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow
Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow Open
Machine learning (ML) methods can train a model to predict material properties by exploiting patterns in materials databases that arise from structure-property relationships. However, the importance of ML-based feature analysis and selecti…
View article: Gradient boosted and statistical feature selection workflow for materials property predictions
Gradient boosted and statistical feature selection workflow for materials property predictions Open
With the emergence of big data initiatives and the wealth of available chemical data, data-driven approaches are becoming a vital component of materials discovery pipelines or workflows. The screening of materials using machine-learning mo…
View article: Automatic materials characterization from infrared spectra using convolutional neural networks
Automatic materials characterization from infrared spectra using convolutional neural networks Open
Infrared spectroscopy is a technique used to characterize unknown materials by identifying the constituent functional groups of molecules through the analysis of obtained spectra. This analysis has now been automated using artificial intel…