Results 51 to 60 of about 79,268 (265)

Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy

Advanced Engineering Materials, EarlyView.
Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang, Jianxiang Zhao, Zhaoqi Chen, Hao Gong, Yinsen Yan, Junlei Tao, Yong Yang   +6 more
wiley   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

Advanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin, Marta Campolucci, Nicolas de Andrade Ishiki, Nathan Mirgot, Nadir Zhamantay, Frédéric Kanoufi, Jennifer Peron, Jean Charlety, Emiliano Fonda, Valérie Briois, Marco Faustini, Maria‐Letizia De Marco, Christine Goyhenex, Ovidiu Ersen, Hervé Bulou   +14 more
wiley   +1 more source

Self‐Sintering Ionogel Binder for Flexible, Recyclable, and Healable Printed Giant Magnetoresistive Sensors

Advanced Functional Materials, EarlyView.
ABSTRACT Electronic waste has emerged as a major environmental challenge, driven by the massive consumption and a limited lifetime of modern electronic devices, stimulating the development of sustainable electronics. Here, an all‐biomaterial gelatin‐choline‐citric acid ([Ch][CA]) ionogel is developed as an active binder to realize self‐sintered ...
Lin Guo, Xiaotao Wang, Guannan Mu, Proloy Taran Das, Igor Veremchuk, Conrad Schubert, Aaron D. Cross, Yevhen Zabila, Xuan Peng, Larysa Baraban, Denys Makarov   +10 more
wiley   +1 more source

LEAD: Literature Enhanced Ab Initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions

Advanced Materials, EarlyView.
Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam, William S. Rogers, Chen‐Yu Hu, Ming‐Yuan Song, Xinyu Bao, Shehrin Sayed, Jean Anne C. Incorvia   +6 more
wiley   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Research on Enterprise Credit Risk Prediction Based on Text Information

Journal of Risk Analysis and Crisis Response (JRACR), 2022
: This paper uses the text data mining method to separate the intonation in the annual reports of credit risk enterprises and non-credit risk enterprises, quantify it, and study the impact of annual report intonation on the effectiveness of credit risk ...
Haonan Zhang, Hongmei Zhang, Mu Zhang
doaj   +1 more source

Text Mining Perspectives in Microarray Data Mining [PDF]

ISRN Computational Biology, 2013
Current microarray data mining methods such as clustering, classification, and association analysis heavily rely on statistical and machine learning algorithms for analysis of large sets of gene expression data. In recent years, there has been a growing interest in methods that attempt to discover patterns based on multiple but related data sources ...
openaire   +1 more source

Auditory–Tactile Congruence for Synthesis of Adaptive Pain Expressions in RoboPatients

Advanced Robotics Research, EarlyView.
In this work, we explore auditory–tactile congruence for synthesizing adaptive vocal pain expressions in robopatients. Using a robopatient platform that integrates vocal pain sounds with palpation forces, we conducted 7680 trials across 20 participants.
Saitarun Nadipineni, Chapa Sirithunge, Yue Xie, Fumiya Iida, Thilina Dulantha Lalitharatne   +4 more
wiley   +1 more source