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Partial Density of States Representation for Accurate Deep Neural Network Predictions of X-ray Spectra
The performance of a Machine Learning (ML) algorithm for chemistry is highly contingent upon the architect’s choice of input representation. This work introduces the partial density of states (p-DOS) descriptor: a novel, quantuminspired structural representation which encodes relevant electronic information for machine learning models seeking to simulate X-ray spectroscopy…
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Ultrafast electron diffraction of photoexcited gas-phase cyclobutanone predicted by ab initio multiple cloning simulations
- Dmitry V. Makhov, Lewis Hutton, Adam Kirrander and Dmitry Shalashilin
- Publication , Prediction challenge , Project collaboration
We present the result of our calculations of ultrafast electron diffraction (UED) for cyclobutanone excited into the S2 electronic state, which is based on the non-adiabatic dynamics simulations with the Ab Initio Multiple Cloning (AIMC) method with the electronic structure calculated at the SA(3)-CASSCF(12,12)/aug-cc-pVDZ level of theory…
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