Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Faber, Felix A."'
Autor:
Batatia, Ilyes, Schaaf, Lars L., Chen, Huajie, Csányi, Gábor, Ortner, Christoph, Faber, Felix A.
Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications. However, MPNNs face challenges when modeling non-local interactions in graphs suc
Externí odkaz:
http://arxiv.org/abs/2310.10434
The large amount of powder diffraction data for which the corresponding crystal structures have not yet been identified suggests the existence of numerous undiscovered, physically relevant crystal structure prototypes. In this paper, we present a sch
Externí odkaz:
http://arxiv.org/abs/2309.16454
Conventional kernel-based machine learning models for ab initio potential energy surfaces, while accurate and convenient in small data regimes, suffer immense computational cost as training set sizes increase. We introduce QML-Lightning, a PyTorch pa
Externí odkaz:
http://arxiv.org/abs/2206.01580
We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarking approach is to evaluate raw
Externí odkaz:
http://arxiv.org/abs/2112.02287
Autor:
Goodall, Rhys E. A., Parackal, Abhijith S., Faber, Felix A., Armiento, Rickard, Lee, Alpha A.
A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that machine learning can be used to learn such relationships, allowing the st
Externí odkaz:
http://arxiv.org/abs/2106.11132
An assessment of the structural resolution of various fingerprints commonly used in machine learning
Autor:
Parsaeifard, Behnam, De, Deb Sankar, Christensen, Anders S., Faber, Felix A., Kocer, Emir, De, Sandip, Behler, Joerg, von Lilienfeld, Anatole, Goedecker, Stefan
Publikováno v:
Machine Learning: Science and Technology (2020)
Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many approaches to the construction of such fingerprints, also c
Externí odkaz:
http://arxiv.org/abs/2008.03189
Autor:
Westermayr, Julia, Faber, Felix A., Christensen, Anders S., von Lilienfeld, O. Anatole, Marquetand, Philipp
Excited-state dynamics simulations are a powerful tool to investigate photo-induced reactions of molecules and materials and provide complementary information to experiments. Since the applicability of these simulation techniques is limited by the co
Externí odkaz:
http://arxiv.org/abs/1912.08484
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Akademický článek
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We introduce the FCHL19 representation for atomic environments in molecules or condensed-phase systems. Machine learning models based on FCHL19 are able to yield predictions of atomic forces and energies of query compounds with chemical accuracy on t
Externí odkaz:
http://arxiv.org/abs/1909.01946