Results 11 to 20 of about 19,473,068 (392)

Nanoarchitectonics in Materials Science: Method for Everything in Materials Science. [PDF]

open access: yesMaterials (Basel), 2023
The history of mankind has been accompanied by the development of materials science [...]
Ariga K, Fakhrullin R.
europepmc   +3 more sources

Unsupervised word embeddings capture latent knowledge from materials science literature. [PDF]

open access: yesNature, 2019
The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods.
Ceder, Gerbrand   +8 more
core   +2 more sources

Origami and materials science [PDF]

open access: yesPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2021
Origami, the ancient art of folding thin sheets, has attracted increasing attention for its practical value in diverse fields: architectural design, therapeutics, deployable space structures, medical stent design, antenna design and robotics. In this survey article, we highlight its suggestive value for the design of materials.
H. Liu   +3 more
openaire   +3 more sources

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon [PDF]

open access: yesDigital Discovery, 2023
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon.
K. Jablonka   +51 more
semanticscholar   +1 more source

The Materials Science behind Sustainable Metals and Alloys. [PDF]

open access: yesChem Rev, 2023
Production of metals stands for 40% of all industrial greenhouse gas emissions, 10% of the global energy consumption, 3.2 billion tonnes of minerals mined, and several billion tonnes of by-products every year.
Raabe D.
europepmc   +2 more sources

Graph neural networks for materials science and chemistry [PDF]

open access: yesCommunications Materials, 2022
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural
Patrick Reiser   +10 more
semanticscholar   +1 more source

Self-Driving Laboratories for Chemistry and Materials Science. [PDF]

open access: yesChem Rev
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in ...
Tom G   +15 more
europepmc   +2 more sources

Deep Potentials for Materials Science [PDF]

open access: yesMaterials Futures, 2022
To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine
T. Wen   +4 more
semanticscholar   +1 more source

Recent advances and applications of deep learning methods in materials science [PDF]

open access: yesnpj Computational Materials, 2021
Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities.
K. Choudhary   +12 more
semanticscholar   +1 more source

Materials science and engineering

open access: yesNature, 2023
induced strain hardening ...
R. W. Cahn
semanticscholar   +1 more source

Home - About - Disclaimer - Privacy