Results 1 to 10 of about 18,676,719 (318)
Graph neural networks for materials science and chemistry [PDF]
arXiv, 2022 Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models.
Reiser P +10 more
arxiv +4 more sources
The Materials Science behind Sustainable Metals and Alloys. [PDF]
Chem 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
First principles phonon calculations in materials science [PDF]
arXiv, 2015 Phonon plays essential roles in dynamical behaviors and thermal properties, which are central topics in fundamental issues of materials science. The importance of first principles phonon calculations cannot be overly emphasized. Phonopy is an open source code for such calculations launched by the present authors, which has been world-widely used.
A. Togo, I. Tanaka
arxiv +3 more sources
Nanoarchitectonics in Materials Science: Method for Everything in Materials Science. [PDF]
Materials (Basel), 2023 The history of mankind has been accompanied by the development of materials science [...]
Ariga K, Fakhrullin R.
europepmc +2 more sources
Data-driven materials science: status, challenges and perspectives [PDF]
Advancement of science, 2019 Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials data sets that are too big or complex for traditional human reasoning - typically with the intent to discover new or improved materials or materials phenomena.
Lauri Himanen +3 more
arxiv +3 more sources
Origami and materials science [PDF]
Philosophical 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
Deep Potentials for Materials Science [PDF]
Materials 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
Interpretable and Explainable Machine Learning for Materials Science and Chemistry [PDF]
Accounts of Materials Research, 2021 While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive
Felipe Oviedo +3 more
semanticscholar +1 more source
Quantum Algorithms for Quantum Chemistry and Quantum Materials Science. [PDF]
Chemical Reviews, 2020 As we begin to reach the limits of classical computing, quantum computing has emerged as a technology that has captured the imagination of the scientific world.
B. Bauer +3 more
semanticscholar +1 more source