📄 Add PDFs for all papers #22
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fwkoch
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Aug 5, 2024
| Machine learning algorithms can be used to predict the spectral features of the NMR spectra of an organic compound based on its chemical structure {cite:p}`https://doi.org/10.1021/acs.jcim.0c00195`. The algorithm would learn the relationship between atoms and bonds in a molecule, and how it affects the NMR spectra of that molecule. Simple neural network algorithms simplify molecules by encapsulating each atom’s features within a vector, albeit at the cost of losing significant information about interatomic interactions. However, a more effective approach to representing molecules for machine learning algorithms emerges with the utilization of graphs {cite:p}`https://doi.org/10.1021/acs.jcim.0c00195`. The graph is composed of nodes, which represent the atoms, that are connected by edges, which represent the bonds between atoms. A graph neural network is a machine learning algorithm that can be used to simulate a compound by mapping out the molecular structure of the compound on a graph. Furthermore, they can be used to predict the chemical shifts of 13C NMR spectra for carbon compounds more accurately than a classic neural network {cite:p}`https://doi.org/10.1021/acs.jcim.0c00195`. The graph neural network algorithm can help scientists while they are producing a new compound as they could use the algorithm to verify if they made the correct compound. | ||
| Machine learning algorithms can be used to predict the spectral features of the NMR spectra of an organic compound based on its chemical structure {cite:p}`https://doi.org/10.1021/acs.jcim.0c00195`. The algorithm would learn the relationship between atoms and bonds in a molecule, and how it affects the NMR spectra of that molecule. Simple neural network algorithms simplify molecules by encapsulating each atom’s features within a vector, albeit at the cost of losing significant information about interatomic interactions. However, a more effective approach to representing molecules for machine learning algorithms emerges with the utilization of graphs {cite:p}`https://doi.org/10.1021/acs.jcim.0c00195`. The graph is composed of nodes, which represent the atoms, that are connected by edges, which represent the bonds between atoms. A graph neural network is a machine learning algorithm that can be used to simulate a compound by mapping out the molecular structure of the compound on a graph. Furthermore, they can be used to predict the chemical shifts of 13C NMR | ||
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To improve PDF formatting, I added these additional breaks. This is currently impacting the web builds, but I will fix that.
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@fwkoch This all looks good to me. If we're good, I'm happy to merge into main. |
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This adds PDFs for all papers, using the NCSSM typst template. I made a few small changes to the articles to ensure things build nicely - but nothing that impacts the content, of course.
There are a few lingering errors in the PDFs, related to typst limitations/bugs - we can figure out how to address these in time. However, none of these render the paper unreadable: