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April 15, 2024 In Unkategorisiert By admin

A message passing neural network for predicting dipole moment dependent core electron excitation spectra

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00021H, Communication Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Kiyou Shibata, Teruyasu MizoguchiA message-passing neural network using a unit direction vector in addition to molecular graphs as the input satisfying invariance to space-inversion

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April 15, 2024 In Unkategorisiert By admin

Extended similarity methods for efficient data mining in imaging mass spectrometry

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D3DD00165B, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Nicholas R. Ellin, Yingchan Guo, Ramón Alain Miranda-Quintana, Boone M. PrenticeExtended similarity indices facilitate processing complex IMS data and quickly finding correlations between multiple

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April 15, 2024 In Unkategorisiert By admin

Chebifier: automating semantic classification in ChEBI to accelerate data-driven discovery

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D3DD00238A, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Martin Glauer, Fabian Neuhaus, Simon Flügel, Marie Wosny, Till Mossakowski, Adel Memariani, Johannes Schwerdt, Janna HastingsConnecting chemical structural representations with meaningful categories and semantic

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April 15, 2024 In Unkategorisiert By admin

MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00001C, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Emil T. S. Kjær, Andy S. Anker, Andrea Kirsch, Joakim Lajer, Olivia Aalling-Frederiksen, Simon J. L. Billinge, Kirsten M. Ø. JensenWe present MLstructureMining, a

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April 15, 2024 In Unkategorisiert By admin

Repurposing quantum chemical descriptor datasets for on-the-fly generation of informative reaction representations: application to hydrogen atom transfer reactions

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00043A, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Javier E. Alfonso-Ramos, Rebecca M. Neeser, Thijs StuyverIn this work, we explore how existing datasets of quantum chemical properties can be repurposed to build

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April 15, 2024 In Unkategorisiert By admin

Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D3DD00175J, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Puck van Gerwen, Ksenia R. Briling, Yannick Calvino Alonso, Malte Franke, Clemence CorminboeufWe benchmark various methods for the prediction of computed activation barriers on

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April 15, 2024 In Unkategorisiert By admin

High-throughput Quantum Theory of Atoms in Molecules (QTAIM) for Geometric Deep Learning of Molecular and Reaction Properties

Digital Discovery, 2024, Accepted ManuscriptDOI: 10.1039/D4DD00057A, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Santiago Vargas, Winston Gee, Anastassia AlexandrovaWe present a package, Generator, for geometric molecular property prediction based on topological features of quantum mechanical electron density.

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April 15, 2024 In Unkategorisiert By admin

Automated extraction of synthesis parameters of pulsed laser-deposited materials from scientific literature

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00051J, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Rajan Kumar, Ablokit Joshi, Salman A. Khan, Shikhar MisraAn efficient data-driven literature-based discovery approach is utilized, that is capable of extracting knowledge from materials

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April 15, 2024 In Unkategorisiert By admin

The automated discovery of kinetic rate models – methodological frameworks

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D3DD00212H, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Miguel Ángel de Carvalho Servia, Ilya Orson Sandoval, King Kuok (Mimi) Hii, Klaus Hellgardt, Dongda Zhang, Ehecatl Antonio del Rio ChanonaTwo automated knowledge discovery

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