Results 81 to 90 of about 5,428,738 (277)

Deep learning in-depth analysis of crystal graph convolutional neural networks: A new era in materials discovery and its applications

Nanotechnology Reviews
Materials informatics is essential for precise material property prediction, enhancing experimental efficiency. Traditional methods like density functional theory are slow and ineffective for complex relationships.
Qureshi Nilam, Bang Jinhong, Doh Jaehyeok   +2 more
doaj   +1 more source

Bayesian sparse modeling for interpretable prediction of hydroxide ion conductivity in anion-conductive polymer membranes

Science and Technology of Advanced Materials: Methods
Anion-conductive polymer membranes have attracted significant attention as solid electrolytes for alkaline fuel cells and electrolyzers. Their ionic conductivity varies depending on the type and arrangement of quaternary ammonium groups, as well as the ...
Ryo Murakami, Kenji Miyatake, Ahmed Mohamed Ahmed Mahmoud, Hideki Yoshikawa, Kenji Nagata   +4 more
doaj   +1 more source

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Advanced Materials Technologies, EarlyView.
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz, Yaroslav Mudryk, Jun Cui, Lucas Griffith, Duane D. Johnson, Julie Slaughter   +5 more
wiley   +1 more source

Multi-modal cascade feature transfer for polymer property prediction

Machine Learning: Science and Technology
In this paper, we propose a novel transfer learning approach called multi-modal cascade model with feature transfer for polymer property prediction.
Kiichi Obuchi, Yuta Yahagi, Kiyohiko Toyama, Shukichi Tanaka, Kota Matsui   +4 more
doaj   +1 more source

Combinatorial Experimentation and Materials Informatics

, 2006
High-throughput experimentation is effective in systematically producing large and diverse data sets. The marriage of combinatorial materials science and informatics is a natural one, and results are beginning to emerge from the integration of elements ...
Ichiro Takeuchi, Yuji Matsumoto, Mikk Lippmaa   +2 more
core   +1 more source

Laser‐Induced Local De‐Doping in Organic Semiconductors

Advanced Materials Technologies, EarlyView.
The polymer PBTTT‐C14 doped with the small molecule acceptor F4TCNQ is considered as a benchmark material system. Mask‐free, microscale patterning of the doping level via laser‐induced heating and sublimation of dopant molecules is shown. Controlling laser irradiance and swath spacing allows dopant level control with laser‐limited resolution, opening ...
Christian Rainer, Aleksandr Perevedentsev, Daniel Ryklin, Rasmus R. Schröder, Gerardo Hernandez‐Sosa, Uli Lemmer   +5 more
wiley   +1 more source

Physics-Informed Descriptor Engineering and Explainable Machine Learning Reveal the Quantum Mechanical Origins of Electronic Dielectric Response in Oxide Materials

Chemistry
A physics-informed machine-learning model was created to forecast the dielectric constant of oxide materials. The study explores the physicochemical descriptors that govern the response of the materials.
Mega Novita, Alok Singh Chauhan, Nancy Agarwal, Deepak Gupta, Wardatul Jannah, Imadudin Harjanto   +5 more
doaj   +1 more source

Light‐Driven Quadrupedal Walking Biohybrid Robot With Antagonistic Muscle‐Rings and Inclined Joints

Advanced Robotics Research, EarlyView.
This work presents a light‐driven quadrupedal walking biohybrid robot powered by antagonistic muscle‐rings that achieve alternating walking gait. Optical training improved reproducibility of cultured muscle tissues, while caffeine treatment enhanced contractile force.
Shotaro Saito, Byeongwook Jo, Tomohiro Morita, Shota Noda, Minghao Nie, Shoji Takeuchi   +5 more
wiley   +1 more source

Why machine learning struggles with magnetism? The role of temperature, disorder, and spin physics

Cell Reports Physical Science
Summary: Machine learning (ML) has transformed materials informatics by enabling rapid prediction, screening, and discovery across vast chemical spaces. However, its application to magnetic materials remains fundamentally constrained.
Varun Chaudhary
doaj   +1 more source

Romanian National Olympiads in Informatics and Training

, 2008
The attached archive contains a few examples of algorithmic tasks proposed at Romanian contests and olympiads in informatics (including model solutions).International audienceThe paper discusses the Romanian experience, including two on-line forums which
Cerchez, Emanuela, Andreica, Mugurel Ionut   +1 more
core