Research Articles
Validating Predicted Topological Semimetals: From Computational Discovery to Experimental Confirmation
This article provides a comprehensive framework for the validation of predicted topological semimetals, addressing the critical gap between computational prediction and experimental confirmation.
Data Cleaning for Materials Informatics: Advanced Techniques to Overcome Sparse, Noisy Data and Accelerate Discovery
This article provides a comprehensive guide to data cleaning techniques specifically tailored for the unique challenges in materials informatics.
Overcoming Anthropogenic Bias in Materials Datasets: Strategies for Equitable AI-Driven Discovery
This article addresses the critical challenge of anthropogenic bias in materials science datasets, which can skew AI predictions and hinder the discovery of novel materials.
Hierarchical Nonnegative Matrix Factorization: Unraveling Complex Material Structures for Advanced Research
This article provides a comprehensive exploration of Hierarchical Nonnegative Matrix Factorization (HNMF), a powerful unsupervised machine learning technique for discerning multi-level structures within complex scientific data.
Ensuring Data Veracity in Text-Mined Synthesis Recipes: A Guide for Biomedical Researchers
This article addresses the critical challenge of data veracity in text-mined materials synthesis recipes, a growing concern for researchers and drug development professionals leveraging AI for accelerated discovery.
Gaussian Process Models for Material Property Prediction: A Guide for Biomedical Researchers
This article provides a comprehensive overview of Gaussian Process (GP) models for predicting material properties, with a special focus on applications relevant to drug development.
AI-Driven Link Prediction: Accelerating Material Property Discovery and Drug Development
This article explores the transformative role of AI-powered link prediction in material property discovery, a critical methodology for researchers and drug development professionals.
Optimal Experimental Design for Materials Discovery: Bayesian Methods, AI, and Self-Driving Labs
This article provides a comprehensive overview of optimal experimental design (OED) frameworks that are transforming materials discovery from a traditional, trial-and-error process into an efficient, informatics-driven practice.
Bridging Theory and Experiment: A Practical Guide to Validating DFT Predictions with Experimental Synthesis
This article provides a comprehensive guide for researchers and drug development professionals on the critical process of validating Density Functional Theory (DFT) predictions through experimental synthesis.
The Materials Genome Initiative: Accelerating Biomedical Innovation from Discovery to Clinical Deployment
This article explores the transformative impact of the Materials Genome Initiative (MGI) on biomedical and materials research.