Lifelong Machine Learning Potentials
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Marco Eckhoff and Markus Reiher (2023)
Highlighted by Jan Jensen
While machine learning potentials (MLPs) can give you DFT accuracy at FF
costs, they als...
Post-hoc correction of generated representations
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The performance of generative models based on SMILES is much improved
compared to the early days when only 5-20% of the SMILES generated would be
conside...
dJ-DP4 and iJ-DP4: including coupling constants
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I have written quite a number of posts on using quantum mechanics
computations to predict NMR spectra that can aid in identifying chemical
structure. Perha...
Learning Computational Chemistry from Prof. Zipse
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Professor Hendrik Zipse, Ludwig Maximilians Universität-München, has posted
a nice set of computational chemistry teaching resources on his research
group’...
New Input Generator Framework in Avogadro 2
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Avogadro 1.x had quite a large number of input generators that came from
very humble beginnings. They were designed to be easy to write, and to give
a s...