Search Results - "Csányi, Gábor"

Physics-Inspired Structural Representations for Molecules and Materials by Musil, Felix, Grisafi, Andrea, Bartók, Albert P, Ortner, Christoph, Csányi, Gábor, Ceriotti, Michele

“…The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale…”
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Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials by John, S. T, Csányi, Gábor

“…We introduce a computational framework that is able to describe general many-body coarse-grained (CG) interactions of molecules and use it to model the free…”
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A general-purpose machine-learning force field for bulk and nanostructured phosphorus by Deringer, Volker L., Caro, Miguel A., Csányi, Gábor

“…Elemental phosphorus is attracting growing interest across fundamental and applied fields of research. However, atomistic simulations of phosphorus have…”
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Gaussian approximation potentials: A brief tutorial introduction by Bartok, Albert P, Csanyi, Gábor

“…We present a swift walk‐through of our recent work that uses machine learning to fit interatomic potentials based on quantum mechanical data. We describe our…”
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Machine Learning Interatomic Potentials as Emerging Tools for Materials Science by Deringer, Volker L., Caro, Miguel A., Csányi, Gábor

“…Atomic‐scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of…”
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Machine learning potentials for extended systems: a perspective by Behler, Jörg, Csányi, Gábor

“…In the past two and a half decades machine learning potentials have evolved from a special purpose solution to a broadly applicable tool for large-scale…”
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Machine learning in chemical reaction space by Stocker, Sina, Csányi, Gábor, Reuter, Karsten, Margraf, Johannes T.

“…Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 10 60 molecules. While intractable as a whole, modern…”
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Performance and Cost Assessment of Machine Learning Interatomic Potentials by Zuo, Yunxing, Chen, Chi, Li, Xiangguo, Deng, Zhi, Chen, Yiming, Behler, Jörg, Csányi, Gábor, Shapeev, Alexander V, Thompson, Aidan P, Wood, Mitchell A, Ong, Shyue Ping

“…Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a…”
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Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression by Mones, Letif, Bernstein, Noam, Csányi, Gábor

“…Practical free energy reconstruction algorithms involve three separate tasks: biasing, measuring some observable, and finally reconstructing the free energy…”
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Machine Learning a General-Purpose Interatomic Potential for Silicon by Bartók, Albert P., Kermode, James, Bernstein, Noam, Csányi, Gábor

“…The success of first-principles electronic-structure calculation for predictive modeling in chemistry, solid-state physics, and materials science is…”
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Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems by Grisafi, Andrea, Wilkins, David M, Csányi, Gábor, Ceriotti, Michele

“…Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for…”
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Data-Driven Learning of Total and Local Energies in Elemental Boron by Deringer, Volker L, Pickard, Chris J, Csányi, Gábor

“…The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure…”
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On representing chemical environments by Bartók, Albert P., Kondor, Risi, Csányi, Gábor

“…We review some recently published methods to represent atomic neighborhood environments, and analyze their relative merits in terms of their faithfulness and…”
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Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning by Caro, Miguel A, Aarva, Anja, Deringer, Volker L, Csányi, Gábor, Laurila, Tomi

“…Systematic atomistic studies of surface reactivity for amorphous materials have not been possible in the past because of the complexity of these materials and…”
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Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions by Cisneros, Gerardo Andrés, Wikfeldt, Kjartan Thor, Ojamäe, Lars, Lu, Jibao, Xu, Yao, Torabifard, Hedieh, Bartók, Albert P, Csányi, Gábor, Molinero, Valeria, Paesani, Francesco

“…Almost 50 years have passed from the first computer simulations of water, and a large number of molecular models have been proposed since then to elucidate the…”
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Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics by Deringer, Volker L, Bernstein, Noam, Bartók, Albert P, Cliffe, Matthew J, Kerber, Rachel N, Marbella, Lauren E, Grey, Clare P, Elliott, Stephen R, Csányi, Gábor

“…Amorphous silicon (a-Si) is a widely studied noncrystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show…”
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Accuracy and transferability of Gaussian approximation potential models for tungsten by Szlachta, Wojciech J., Bartók, Albert P., Csányi, Gábor

“…We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the Gaussian approximation potential framework, fitted to a…”
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Origins of structural and electronic transitions in disordered silicon by Deringer, Volker L., Bernstein, Noam, Csányi, Gábor, Ben Mahmoud, Chiheb, Ceriotti, Michele, Wilson, Mark, Drabold, David A., Elliott, Stephen R.

“…Structurally disordered materials pose fundamental questions 1 – 4 , including how different disordered phases (‘polyamorphs’) can coexist and transform from…”
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Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression by Stecher, Thomas, Bernstein, Noam, Csányi, Gábor

“…We demonstrate how the Gaussian process regression approach can be used to efficiently reconstruct free energy surfaces from umbrella sampling simulations. By…”
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Atomic cluster expansion: Completeness, efficiency and stability by Dusson, Geneviève, Bachmayr, Markus, Csányi, Gábor, Drautz, Ralf, Etter, Simon, van der Oord, Cas, Ortner, Christoph

“…•Polynomial approximation of functions that are invariant under permutations and isometry.•Guarantees that the basis is complete but not…”
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