Computation sparks chemical discovery

doi:10.1038/s41467-020-18651-x

Cited by 1 publication

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“…[ 17 ] The trend to perform chemical in silico calculations for the prediction of molecular and material properties shifted in recent years from methods like density functional theory (DFT) or other ab initio methods to data‐driven methods from the field of machine learning. [ 18 ] One of the main reasons is that once the model is trained, which might take a very long time, each prediction takes a fraction of the time of classical calculations (e.g., DFT), which are then much more computation‐intensive. [ 19 ]…”

Section: Introductionmentioning

confidence: 99%

Prediction of Nanoparticle Sizes for Arbitrary Methacrylates Using Artificial Neuronal Networks

et al. 2021

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Particle sizes represent one of the key factors influencing the usability and specific targeting of nanoparticles in medical applications such as vectors for drug or gene therapy. A multi-layered graph convolutional network combined with a fully connected neuronal network is presented for the prediction of the size of nanoparticles based only on the polymer structure, the degree of polymerization, and the formulation parameters. The model is capable of predicting particle sizes obtained by nanoprecipitation of different poly(methacrylates). This includes polymers the network has not been trained with, indicating the high potential for generalizability of the model. By utilizing this model, a significant amount of time and resources can be saved in formulation optimization without extensive primary testing of material properties.

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Section: Introductionmentioning

confidence: 99%