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Results: 181
Mapping structure heterogeneities and visualizing moisture degradation of perovskite films with nano-focus WAXS.
Nian Li, Shambhavi Pratap, Volker Körstgens, Sundeep Vema, Lin Song, Suzhe Liang, Anton Davydok, Christina Krywka, Peter Müller-Buschbaum
Nov 06, 2022
Extensive attention has focused on the structure optimization of perovskites, whereas rare research has mapped the structure heterogeneity within mixed hybrid perovskite films. Overlooked aspects include material and structure...
Mapping structure heterogeneities and visualizing moisture degradation of perovskite films with nano-focus WAXS.
Nian Li, Shambhavi Pratap, Volker Körstgens, Sundeep Vema, Lin Song, Suzhe Liang, Anton Davydok, Christina Krywka, Peter Müller-Buschbaum
Dec 08, 2022
Extensive attention has focused on the structure optimization of perovskites, whereas rare research has mapped the structure heterogeneity within mixed hybrid perovskite films. Overlooked aspects include material and structure...
Published by: Nature Communications
Reversal of migration flows
We investigate the dynamic effects of interregional labor market integration on migration flows, capital formation, and the price for housing services. The co-evolution of these variables depends on initial conditions at the...
Reversal of migration flows
We investigate the dynamic effects of interregional labor market integration on migration flows, capital formation, and the price for housing services. The co-evolution of these variables depends on initial conditions at the...
How has external knowledge contributed to lithium-ion batteries for the energy transition?
Innovation in clean-energy technologies is central toward a net-zero energy system. One key determinant of technological innovation is the integration of external knowledge, i.e., knowledge spillovers. However, extant work does...
De novo exploration and self-guided learning of potential-energy surfaces
Abstract: Interatomic potential models based on machine learning (ML) are rapidly developing as tools for material simulations. However, because of their flexibility, they require large fitting databases that are normally...
Automated methods for cell type annotation on scRNA-seq data.
The advent of single-cell sequencing started a new era of transcriptomic and genomic research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type annotation is a crucial step in analyzing single-cell...
Data-driven learning and prediction of inorganic crystal structures.
Crystal structure prediction algorithms, including ab initio random structure searching (AIRSS), are intrinsically limited by the huge computational cost of the underlying quantum-mechanical methods. We have recently shown that...
Data-Driven Learning of Total and Local Energies in Elemental Boron.
The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic...
Hierarchically Structured Allotropes of Phosphorus from Data-Driven Exploration.
The discovery of materials is increasingly guided by quantum-mechanical crystal-structure prediction, but the structural complexity in bulk and nanoscale materials remains a bottleneck. Here we demonstrate how data-driven...
Linguistic Distances in Dialectometric Intensity Estimation
Dialectometric intensity estimation as introduced in Rumpf etal. (2009) and Pickl and Rumpf (2011, 2012) is a method for the unsupervised generation of maps visualizing geolinguistic data on the level of linguistic...
Combining phonon accuracy with high transferability in Gaussian approximation potential models.
Machine learning driven interatomic potentials, including Gaussian approximation potential (GAP) models, are emerging tools for atomistic simulations. Here, we address the methodological question of how one can fit GAP models...
Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures.
So Fujikake, Volker L Deringer, Tae Hoon Lee, Marcin Krynski, Stephen R Elliott, Gábor Csányi
Feb 27, 2018
We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms...
Unravelling Local Atomic Order of the Anionic Sublattice in M(Al1-x Gax )4 with M=Sr and Ba by Using NMR Spectroscopy and Quantum Mechanical Modelling.
Oliver Pecher, Bernhard Mausolf, Volker Peters, Kevin Lamberts, Alexander Korthaus, Frank Haarmann
Nov 04, 2016
Reactivity of Amorphous Carbon Surfaces
Systematic atomistic studies of surface reactivity for amorphous materials have not been possible in the past because of the complexity of these materials and the lack of the computer power necessary to draw representative...
An accurate and transferable machine learning potential for carbon.
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, named GAP-20, describes the properties of the bulk...
Gaussian Process Regression for Materials and Molecules.
Volker L Deringer, Albert P Bartók, Noam Bernstein, David M Wilkins, Michele Ceriotti, Gábor Csányi
Sep 16, 2021
We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in...
Published by: Chemical reviews
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