Zobrazeno 1 - 10
of 239
pro vyhledávání: '"Shimizu Koji"'
In materials science, finding crystal structures that have targeted properties is crucial. While recent methodologies such as Bayesian optimization and deep generative models have made some advances on this issue, these methods often face difficultie
Externí odkaz:
http://arxiv.org/abs/2410.08562
Understanding phonon transport properties in defect-laden AlN is important for their device applications. Here, we construct a machine-learning potential to describe phonon transport with $ab$ $initio$ accuracy in pristine and defect-laden AlN, follo
Externí odkaz:
http://arxiv.org/abs/2409.16039
Graph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-informed network must obey certain transformation rules to ensure the
Externí odkaz:
http://arxiv.org/abs/2409.08940
Our goal is to study $p$-adic local systems on a rigid-analytic variety with semistable formal model. We prove that such a local system is semistable if and only if so are its restrictions to the points corresponding to the irreducible components of
Externí odkaz:
http://arxiv.org/abs/2404.19603
We propose a material design method via gradient-based optimization on compositions, overcoming the limitations of traditional methods: exhaustive database searches and conditional generation models. It optimizes inputs via backpropagation, aligning
Externí odkaz:
http://arxiv.org/abs/2403.13627
Understanding the atomistic mechanism of ion conduction in solid electrolytes is critical for the advancement of all-solid-state batteries. Glass-ceramics, which undergo crystallization from a glass state, frequently exhibit unique properties includi
Externí odkaz:
http://arxiv.org/abs/2312.06963
Understanding ionic behaviour under external electric fields is crucial to develop electronic and energy-related devices using ion transport. In this study, we propose a neural network (NN) model to predict the Born effective charges of ions along an
Externí odkaz:
http://arxiv.org/abs/2305.19546
Deep-learning inverse techniques have attracted significant attention in recent years. Among them, the neural adjoint (NA) method, which employs a neural network surrogate simulator, has demonstrated impressive performance in the design tasks of arti
Externí odkaz:
http://arxiv.org/abs/2304.13860
High-accuracy prediction of the physical properties of amorphous materials is challenging in condensed-matter physics. A promising method to achieve this is machine-learning potentials, which is an alternative to computationally demanding ab initio c
Externí odkaz:
http://arxiv.org/abs/2206.13727
Autor:
Shimizu, Koji, Dou, Ying, Arguelles, Elvis F., Moriya, Takumi, Minamitani, Emi, Watanabe, Satoshi
Investigation of charged defects is necessary to understand the properties of semiconductors. While density functional theory calculations can accurately describe the relevant physical quantities, these calculations increase the computational loads s
Externí odkaz:
http://arxiv.org/abs/2203.16789