Results 21 to 30 of about 10,388,998 (324)
Enabling deeper learning on big data for materials informatics applications. [PDF]
Sci Rep, 2021 The application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to ...
Jha D +8 more
europepmc +2 more sources
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction [PDF]
arXiv.org, 2023 Recently, the remarkable capabilities of large language models (LLMs) have been illustrated across a variety of research domains such as natural language processing, computer vision, and molecular modeling.
Hongshuo Huang +3 more
semanticscholar +1 more source
Benchmark datasets incorporating diverse tasks, sample sizes, material systems, and data heterogeneity for materials informatics. [PDF]
Data Brief, 2021 Materials discovery via machine learning has become an increasingly popular method due to its ability to rapidly predict materials properties in a time-efficient and low-cost manner. However, one limitation in this field is the lack of benchmark datasets,
Henderson AN, Kauwe SK, Sparks TD.
europepmc +2 more sources
Abdimas Altruis, 2023 The importance of digital literacy and the challenges of the industrial revolution 4.0 that students must face are the main reasons why Informatics become one of subjects in Merdeka Curriculum taught at the junior and senior high school levels in ...
Paulina H. Prima Rosa
doaj +1 more source
MRS Bulletin, 2006 AbstractGlobal markets are pressuring the materials industries to reduce the time span between materials research and materials development. In particular, current approaches to the development and insertion (deployment) of advanced materials in military systems are too time-intensive and expensive.
John R. Rodgers, David Cebon
openaire +1 more source
JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics. [PDF]
Science Bulletin, 2021 Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design. It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek new materials,
Xingang Zhao +15 more
semanticscholar +1 more source
Prompt engineering of GPT-4 for chemical research: what can/cannot be done?
Science and Technology of Advanced Materials: Methods, 2023 This paper evaluates the capabilities and limitations of the Generative Pre-trained Transformer 4 (GPT-4) in chemical research. Although GPT-4 exhibits remarkable proficiencies, it is evident that the quality of input data significantly affects its ...
Kan Hatakeyama-Sato +4 more
doaj +1 more source
An Inverse QSAR Method Based on Linear Regression and Integer Programming
Frontiers in Bioscience-Landmark, 2022 Background: Drug design is one of the important applications of biological science. Extensive studies have been done on computer-aided drug design based on inverse quantitative structure activity relationship (inverse QSAR), which is to infer chemical ...
Jianshen Zhu +5 more
doaj +1 more source
Advanced Science, 2023 A multimodal deep‐learning (MDL) framework is presented for predicting physical properties of a ten‐dimensional acrylic polymer composite material by merging physical attributes and chemical data.
Shun Muroga, Yasuaki Miki, Kenji Hata
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Automatic extraction of materials and properties from superconductors scientific literature
Science and Technology of Advanced Materials: Methods, 2023 The automatic extraction of materials and related properties from the scientific literature is gaining attention in data-driven materials science (Materials Informatics).
Luca Foppiano +5 more
doaj +1 more source