Results 11 to 20 of about 19,473,068 (392)
Nanoarchitectonics in Materials Science: Method for Everything in Materials Science. [PDF]
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]
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]
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]
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]
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]
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]
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]
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]
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
induced strain hardening ...
R. W. Cahn
semanticscholar +1 more source