From 2bdb5cb9b5d13f9012b1782270dd49c3f6a43551 Mon Sep 17 00:00:00 2001 From: Arslan Mazitov Date: Fri, 9 Feb 2024 00:40:31 +0100 Subject: [PATCH] Linting fix --- README.rst | 5 +++-- docs/src/architectures/alchemical-model.rst | 16 ++++++++++------ 2 files changed, 13 insertions(+), 8 deletions(-) diff --git a/README.rst b/README.rst index 7f205a868..3a0aee528 100644 --- a/README.rst +++ b/README.rst @@ -64,9 +64,10 @@ atomistic model. * - SOAP BPNN - A Behler-Parrinello neural network with SOAP features - + * - Alchemical Model - - A Behler-Parrinello neural network with SOAP features and Alchemical Compression of the composition space + - A Behler-Parrinello neural network with SOAP features + and Alchemical Compression of the composition space .. marker-documentation diff --git a/docs/src/architectures/alchemical-model.rst b/docs/src/architectures/alchemical-model.rst index d45c92c99..7b4446406 100644 --- a/docs/src/architectures/alchemical-model.rst +++ b/docs/src/architectures/alchemical-model.rst @@ -3,14 +3,18 @@ Alchemical Model ========= -This is an implementation of Alchemical Model: a Behler-Parrinello neural network -with SOAP features and Alchemical Compression of the composition space. This model -is extremely useful for simulating systems with large amount of chemical elements. +This is an implementation of Alchemical Model: a Behler-Parrinello neural network +with SOAP features and Alchemical Compression of the composition space. This model +is extremely useful for simulating systems with large amount of chemical elements. For further details, please refer to the original papers: -- Willatt, Michael J., Félix Musil, and Michele Ceriotti. "Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements." Physical Chemistry Chemical Physics 20.47 (2018): 29661-29668. -- Lopanitsyna, Nataliya, et al. "Modeling high-entropy transition metal alloys with alchemical compression." Physical Review Materials 7.4 (2023): 045802. -- Mazitov, Arslan, et al. "Surface segregation in high-entropy alloys from alchemical machine learning." arXiv preprint arXiv:2310.07604 (2023). +- Willatt, Michael J., Félix Musil, and Michele Ceriotti. "Feature optimization for + atomistic machine learning yields a data-driven construction of the periodic table of + the elements." Physical Chemistry Chemical Physics 20.47 (2018): 29661-29668. +- Lopanitsyna, Nataliya, et al. "Modeling high-entropy transition metal alloys with + alchemical compression." Physical Review Materials 7.4 (2023): 045802. +- Mazitov, Arslan, et al. "Surface segregation in high-entropy alloys from alchemical + machine learning." arXiv preprint arXiv:2310.07604 (2023). Installation