Academic Articles & Research Papers

Dynamic of upper body sweat distribution in young males wearing fully encapsulated chemical protective ensembles

Abstract Impermeability is a feature of fully encapsulated chemical protective ensembles (FCPE), which can affect people's sweat and affect their clothing's thermal-wet comfort. This study investigated the dynamics of upper-body sweat distr...

Functionalized bioresorbable biliary stents: materials design, coating engineering, and clinical translation.

Materials design strategies for green thermoelectric generators enabling sustainable and scalable energy harvesting.

Anode-Free Sodium Metal Batteries: From Materials Design to System-Level Integration.

Optimal pre-train/fine-tune strategies for accurate material property predictions

Abstract A pathway to overcome limited data availability in materials science is to use the framework of transfer learning, where a pre-trained (PT) machine learning model (on a larger dataset) can be fine-tuned (FT) on a target (smaller) d...

A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning

Abstract The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivit...

Fast Exploring Literature by Language Machine Learning for Perovskite Solar Cell Materials Design

Making computers automatically extract latent scientific knowledge from literature is highly desired for future materials and chemical research in the artificial intelligence era. Herein, the natural language processing (NLP)‐based machine ...

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Abstract Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an ...

Deep learning for computational chemistry

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning,...

Deep Learning in Chemistry

Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns repres...

Learning metal microstructural heterogeneity through spatial mapping of diffraction latent space features

Abstract To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-b...