D
David M. Wilkins
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 46
Citations - 2087
David M. Wilkins is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Density functional theory & Dipole. The author has an hindex of 18, co-authored 41 publications receiving 1357 citations. Previous affiliations of David M. Wilkins include University of Oxford & Nanyang Technological University.
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Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
TL;DR: This work introduces a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries, and derives a tensor kernel adapted to rotational symmetry.
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i-PI 2.0: A universal force engine for advanced molecular simulations
Venkat Kapil,Mariana Rossi,Ondrej Marsalek,Ondrej Marsalek,Riccardo Petraglia,Yair Litman,Thomas Spura,Bingqing Cheng,Alice Cuzzocrea,Robert H. Meißner,David M. Wilkins,Benjamin A. Helfrecht,Przemysław Juda,Sébastien P. Bienvenue,Wei Fang,Jan Kessler,Igor Poltavsky,Steven Vandenbrande,Jelle Wieme,Clémence Corminboeuf,Thomas D. Kühne,David E. Manolopoulos,Thomas E. Markland,Jeremy O. Richardson,Alexandre Tkatchenko,Gareth A. Tribello,Veronique Van Speybroeck,Michele Ceriotti +27 more
TL;DR: This second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms that are moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives.
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Gaussian Process Regression for Materials and Molecules.
Volker L. Deringer,Albert P. Bartók,Noam Bernstein,David M. Wilkins,Michele Ceriotti,Gábor Csányi +5 more
TL;DR: In this paper, the authors provide an introduction to Gaussian process regression (GPR) machine learning methods in computational materials science and chemistry, focusing on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian approximation potential (GAP) framework.
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Transferable Machine-Learning Model of the Electron Density
Andrea Grisafi,Alberto Fabrizio,Benjamin Meyer,David M. Wilkins,Clémence Corminboeuf,Michele Ceriotti +5 more
TL;DR: An atom-centered, symmetry-adapted framework is introduced to machine-learn the valence charge density based on a small number of reference calculations, which can be used to interpret experiments, accelerate electronic structure calculations, and compute electrostatic interactions in molecules and condensed-phase systems.
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Electrolytes induce long-range orientational order and free energy changes in the H-bond network of bulk water
Yixing Chen,Halil I. Okur,Nikolaos Gomopoulos,Carlos Macias-Romero,Paul S. Cremer,Poul B. Petersen,Gabriele Tocci,David M. Wilkins,Chungwen Liang,Michele Ceriotti,Sylvie Roke +10 more
TL;DR: A multiscale investigation of the bulk and surface of aqueous electrolyte solutions that extends from the atomic scale to nanoscopic length scales (using bulk and interfacial femtosecond second harmonic measurements) to the macroscopic scale (using surface tension experiments).