Academic Articles & Research Papers

Complications and Management of Deep Anterior Lamellar Keratoplasty

Objectives: To report the intraoperative and postoperative follow-up complications and management of these in deep anterior lamellar keratoplasty (DALK) surgery. Materials and Methods: Two hundred eighty-four eyes of 252 patients followed...

Some results of the digital analysis of the semantic similarity of audiovisual translation texts (based on the films directed by Denis Villeneuve)

The article analyses different translation versions of science fiction films directed by Denis Villeneuve with the aim of determining their semantic similarity through NLP analysis. There are four groups of metrics used in text analysis: se...

Towards Neural‐Network‐Based Optical Temperature Sensing of Semiconductor Membrane External Cavity Laser

Abstract A machine‐learning (ML) non‐contact method to determine the temperature of a laser gain medium via its laser emission with a trained few‐layer neural‐network (NN) model is presented. The training of the feed‐forward NN enables the ...

Machine Learning in Chemical Engineering: A Perspective

Abstract The transformation of the chemical industry to renewable energy and feedstock supply requires new paradigms for the design of flexible plants, (bio‐)catalysts, and functional materials. Recent breakthroughs in machine learning (ML)...

Machine learning and soil sciences: a review aided by machine learning tools

Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proxi...

Machine learning for renewable energy materials

Achieving the 2016 Paris agreement goal of limiting global warming below 2 °C and securing a sustainable energy future require materials innovations in renewable energy technologies. Machine learning has demonstrated many successes to accel...

Robust optical metasurface heterostructures for prevention and treatment of peri-implantitis.

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...

Data-driven performance prediction of polymer solar cells using multi-layer perceptron with Bayesian hyperparameter tuning

Polymer solar cells (PSCs) rely on labor-intensive trial-and-error experiments to optimize power conversion efficiency (PCE), due to complex coupling of molecular electronic properties, blend morphology and fabrication parameters. This work...

Introduction to Machine Learning

R is one of the top-rated problem-solving tools for machine learning, data science, data analytics, data mining, and statistical analysis. R ’s popularity has resulted in the development of thousands of tutorials. Unfortunately, it’s easy t...

Machine learning for advanced energy materials

The screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low su...

Performance Analysis and Optimization of Terahertz Metamaterial Absorbers Using Machine Learning-Based Inverse Modeling

We present a tunable broadband terahertz (THz) metamaterial absorber with a structurally simple, single-layered vanadium dioxide (VO2) elliptical ring resonator. This design achieves a wide, near-perfect absorption band (3.5–5 THz) w...

Machine learning unifies the modeling of materials and molecules

Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a...

Machine Learning for Electronically Excited States of Molecules

Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculati...

Ultra-broadband and polarization-insensitive metasurface absorber with behavior prediction using machine learning

The solar spectrum energy absorption is very important for designing any solar absorber. The need for absorbing visible, infrared, and ultraviolet regions is increasing as most of the absorbers absorb visible regions. We propose a metasurfa...

Machine Learning‐Driven Biomaterials Evolution

Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to achieve desired biological responses. While there is constant evolution and innovation in materials with time, biomaterials research has been ham...

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better unde...

Machine Learning for Computational Heterogeneous Catalysis

Abstract Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the developme...

Artificial Neural Network (ANN) Approach to Predict Tensile Properties of Longitudinally Placed Fiber Reinforced Polymeric Composites including Interphase

Machine Learning has become prevalent nowadays for predicting data on the mechanical properties of various materials and is widely used in various polymeric applications. In the present study, Artificial Neural Network (ANN), a computationa...

Opportunities and Challenges for Machine Learning in Materials Science

Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid chang...