Results 51 to 60 of about 5,428,738 (277)

Ionic species representations for materials informatics

APL Machine Learning
High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials.
Anthony Onwuli, Keith T. Butler, Aron Walsh   +2 more
doaj   +1 more source

Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models

Science and Technology of Advanced Materials: Methods, 2023
To improve the charge – discharge properties of an LiMn2O4 positive electrode active material for a lithium-ion battery, the effect of additive elements was investigated using high-throughput experiments and materials informatics techniques.
Shin Tajima, Mitsutaro Umehara, Kensuke Takechi   +2 more
doaj   +1 more source

Machine Learning Based Methods for Obtaining Correlations between Microstructures and Thermal Stresses

Metals, 2021
The microstructure–property relationship is critical for parts made using the emerging additive manufacturing process where highly localized cooling rates bestow spatially varying microstructures in the material.
Akshay Bhutada, Sunni Kumar, Dayalan Gunasegaram, Alankar Alankar   +3 more
doaj   +1 more source

Estimating the prevalence and incidence of symptomatic hand OA in the Netherlands using primary care electronic health records.

Arthritis Care &Research, Accepted Article.
Objective This study estimated the incidence and prevalence of symptomatic hand osteoarthritis (OA) in the Dutch population using a validated algorithm that integrates free‐text data from primary care electronic health records (EHRs). Methods This retrospective cohort study used the Integrated Primary Care Information (IPCI) database, including EHRs ...
Onur Kaya, Jacoline van den Driest, Laura Struik, Margreet Kloppenburg, Ruud Selles, Daniel Prieto Alhambra, Sita Bierma‐Zeinstra, Dieuwke Schiphof   +7 more
wiley   +1 more source

Multicrystalline informatics: a methodology to advance materials science by unraveling complex phenomena

Science and Technology of Advanced Materials
Multicrystalline materials play a crucial role in our society. However, their microstructure is complicated, and there is no universal approach to achieving high performance. Therefore, a methodology is necessary to answer the fundamental question of how
Noritaka Usami, Kentaro Kutsukake, Takuto Kojima, Hiroaki Kudo, Tatsuya Yokoi, Yutaka Ohno   +5 more
doaj   +1 more source

Differential privacy protection random forest algorithm and its application in steel materials

工程科学学报, 2023
Data-driven material informatics is considered the fourth paradigm of materials research and development (R&D), which can greatly reduce R&D costs and shorten the R&D cycle.
Xue-hui CHEN, Yan FENG, Quan QIAN
doaj   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

The Shape of Informatics: A UK Perspective

, 2006
The term Informatics is not widely used in UK Higher Education. Computing, Computer Science and Information Science are more commonly used descriptors and many other variations exist.
Irons, Alastair, White, Susan A
core   +1 more source

Determining Multi‐Component Phase Diagrams with Desired Characteristics Using Active Learning

Advanced Science, 2021
Herein, we demonstrate how to predict and experimentally validate phase diagrams for multi‐component systems from a high‐dimensional virtual space of all possible phase diagrams involving several elements based on small existing experimental data.
Yuan Tian, Ruihao Yuan, Dezhen Xue, Yumei Zhou, Yunfan Wang, Xiangdong Ding, Jun Sun, Turab Lookman   +7 more
doaj   +1 more source

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source