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.
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.
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.
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.
This article explores the transformative role of AI-powered link prediction in material property discovery, a critical methodology for researchers and drug development professionals.
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.
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.
This article explores the transformative impact of the Materials Genome Initiative (MGI) on biomedical and materials research.
Accelerating the transition from computational design to physical reality is a central challenge in modern drug discovery.
This article explores the pivotal role of convex-hull stability analysis in predicting the synthesizability of new materials and pharmaceutical polymorphs.