Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Alexander Goscinski"'
Autor:
Philip Loche, Alexander Goscinski, Guillaume Fraux, Victor Paul Principe, Benjamin Aaron Helfrecht, Sergei Kliavinek, Michele Ceriotti, Rose Kathleen Cersonsky
Publikováno v:
Open Research Europe, Vol 3 (2023)
Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields,
Externí odkaz:
https://doaj.org/article/0fba537f22354630beea592541b90f61
Autor:
Alexander Goscinski, Victor Paul Principe, Guillaume Fraux, Sergei Kliavinek, Benjamin Aaron Helfrecht, Philip Loche, Michele Ceriotti, Rose Kathleen Cersonsky
Publikováno v:
Open Research Europe
Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b7f5544e04fc3f7bb02a867f7d855a1
https://doi.org/10.12688/openreseurope.15789.1
https://doi.org/10.12688/openreseurope.15789.1
Autor:
Guillaume Fraux, Markus Stricker, Michael J. Willatt, Alexander Goscinski, Max Veit, Félix Musil, Michele Ceriotti, Till Junge
Physically-motivated and mathematically robust atom-centred representations of molecular structures are key to the success of modern atomistic machine learning (ML) methods. They lie at the foundation of a wide range of methods to predict the propert
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7479cb20122e9e823c5b124b818e699f
https://refubium.fu-berlin.de/handle/fub188/34517
https://refubium.fu-berlin.de/handle/fub188/34517
The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular represent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d70e4651f45b420242a4ccff5eb4f33
https://aip.scitation.org/doi/10.1063/5.0057229
https://aip.scitation.org/doi/10.1063/5.0057229
Efficient, physically-inspired descriptors of the structure and composition of molecules and materials play a key role in the application of machine-learning techniques to atomistic simulations. The proliferation of approaches, as well as the fact th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ca8602795ddfb3f649a1860a1a91c4e
https://infoscience.epfl.ch/record/286327
https://infoscience.epfl.ch/record/286327