Zobrazeno 1 - 10
of 988
pro vyhledávání: '"Butler, Keith A."'
Overcoming the challenge of limited data availability within materials science is crucial for the broad-based applicability of machine learning within materials science. One pathway to overcome this limited data availability is to use the framework o
Externí odkaz:
http://arxiv.org/abs/2406.13142
Autor:
Xie, Weihang, Deng, Zeyu, Liu, Zhengyu, Famprikis, Theodosios, Butler, Keith T., Canepa, Pieremanuele
Publikováno v:
Adv. Energy Mater., 2304230 (2024)
Extended defects, including exposed surfaces and grain boundaries, are critical to the properties of polycrystalline solid electrolytes in all-solid-state batteries (ASSBs). These defects can significantly alter the mechanical and electronic properti
Externí odkaz:
http://arxiv.org/abs/2312.05294
The generation of plausible crystal structures is often the first step in predicting the structure and properties of a material from its chemical composition. Quickly generating and predicting inorganic crystal structures is important for the discove
Externí odkaz:
http://arxiv.org/abs/2307.04340
The traditional display of elements in the periodic table is convenient for the study of chemistry and physics. However, the atomic number alone is insufficient for training statistical machine learning models to describe and extract composition-stru
Externí odkaz:
http://arxiv.org/abs/2307.00784
Publikováno v:
A&A 671, A36 (2023)
Context. Late O-type stars at luminosities $\log L/L_\odot \lesssim 5.2$ show weak winds with mass-loss rates lower than 10$^{-8} M_\odot$ yr$^{-1}$. This implies that their photospheric layers are not strongly affected by the stellar wind. Aims. A h
Externí odkaz:
http://arxiv.org/abs/2301.09462
Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To enable deploy
Externí odkaz:
http://arxiv.org/abs/2212.06444
Autor:
Choudhary, Kamal, DeCost, Brian, Major, Lily, Butler, Keith, Thiyagalingam, Jeyan, Tavazza, Francesca
Classical force fields (FF) based on machine learning (ML) methods show great potential for large scale simulations of materials. MLFFs have hitherto largely been designed and fitted for specific systems and are not usually transferable to chemistrie
Externí odkaz:
http://arxiv.org/abs/2209.05554
Advancements in fast electron detectors have enabled the statistically significant sampling of crystal structures on the nanometre scale by means of Scanning Electron Nanobeam Diffraction (SEND). Characterisation of structural similarity across this
Externí odkaz:
http://arxiv.org/abs/2207.13389
Autor:
Sánchez-Palencia, Pablo, Hamad, Said, Palacios, Pablo, Grau-Crespo, Ricardo, Butler, Keith T.
Ab initio prediction of the variation of properties in the configurational space of solid solutions is computationally very demanding. We present an approach to accelerate these predictions via a combination of density functional theory and machine l
Externí odkaz:
http://arxiv.org/abs/2205.10084
Co-substituted BiFeO3: electronic, ferroelectric, and thermodynamic properties from first principles
Bismuth ferrite, BiFeO3, is a multiferroic solid that is attracting increasing attention as a potential photocatalytic material, because the ferroelectric polarisation enhances the separation of photogenerated carriers. With the motivation of finding
Externí odkaz:
http://arxiv.org/abs/2201.11161