5 hasil untuk "Tim Mueller"
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Artikel Jurnal
Identifying models of dielectric breakdown strength from high-throughput data via genetic programming
Abstract The identification of models capable of rapidly predicting material properties enables rapid screening of large numbers of materials and facilitates the design of new materials. One of the leading challenges for computational researchers is determining the best ways to analyze large…
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Artikel Jurnal
Machine learning for interatomic potential models
The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal…
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Artikel Jurnal
A database of low-energy atomically precise nanoclusters
Abstract The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties…
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Artikel Jurnal
Accelerated prediction of atomically precise cluster structures using on-the-fly machine learning
Abstract The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but predicting the stable structures of clusters can be computationally expensive. In this work, we present a procedure for rapidly predicting low-energy structures of…
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Artikel Jurnal
Materials cartography: A forward-looking perspective on materials representation and devising better maps
Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate computation, automate data analysis, and predict materials properties. The representation of input material features is critical to the accuracy, interpretability, and generalizability of data-driven models…