Feature Optimization for Atomistic Machine Learning Yields A Data-Driven
Construction of the Periodic Table of the Elements

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Feature Optimization for Atomistic Machine Learning Yields A Data-Driven Construction of the Periodic Table of the Elements

30 June 2018

Michele Ceriotti

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Papers citing "Feature Optimization for Atomistic Machine Learning Yields A Data-Driven Construction of the Periodic Table of the Elements"

9 / 9 papers shown

Title
A smooth basis for atomistic machine learning Filippo Bigi Kevin K. Huguenin-Dumittan Michele Ceriotti D. Manolopoulos 61 6 0 05 Sep 2022
Optimal radial basis for density-based atomic representations Alexander Goscinski Félix Musil Sergey Pozdnyakov Michele Ceriotti 64 18 0 18 May 2021
Multi-scale approach for the prediction of atomic scale properties Andrea Grisafi Jigyasa Nigam Michele Ceriotti 41 31 0 27 Aug 2020
Learning the electronic density of states in condensed matter Chiheb Ben Mahmoud A. Anelli Gábor Csányi Michele Ceriotti 42 55 0 21 Jun 2020
Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles M. Veit D. Wilkins Yang Yang R. DiStasio Michele Ceriotti 78 94 0 27 Mar 2020
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning Marcel F. Langer Alex Goessmann M. Rupp AI4CE 73 99 0 26 Mar 2020
DScribe: Library of Descriptors for Machine Learning in Materials Science Lauri Himanen M. Jäger Eiaki V. Morooka F. F. Canova Y. S. Ranawat D. Gao Patrick Rinke A. Foster 69 591 0 18 Apr 2019
Compressing physical properties of atomic species for improving predictive chemistry John E. Herr Kevin J Koh Kun Yao John A. Parkhill AI4CE 55 20 0 31 Oct 2018
Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks T. Xie Jeffrey C. Grossman AI4CE 42 65 0 09 Jul 2018