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Found 7 papers, 2 papers with code
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs).
Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System
Overall, the prediction accuracy as quantified by the root-mean-squared difference $(\sim 0. 003)$ and the $R^2$ $(\sim 0. 99)$ between the reference QCT and predictions of the STD model is high for the test set and off-grid state specific initial conditions and for initial conditions drawn from reactant state distributions characterized by translational, rotational and vibrational temperatures.
The C($^3$P) + O$_2$($^3 Σ_g^-$) $\leftrightarrow$ CO$_2$ $\leftrightarrow$ CO($^1 Σ^+$)+ O($^1$D)/O($^3$P) Reaction: Thermal and Vibrational Relaxation Rates from 15 K to 20000 K
Thermal rates for the C($^3$P) + O$_2$($^3 \Sigma_g^-$) $\leftrightarrow$ CO($^1 \Sigma^+$)+ O($^1$D)/O($^3$P) reaction are investigated over a wide temperature range based on quasi classical trajectory (QCT) simulations on 3-dimensional, reactive potential energy surfaces (PESs) for the $^1$A$'$, $(2)^1$A$'$, $^1$A$''$, $^3$A$'$ and $^3$A$''$ states.
Chemical Physics
MP2 Is Not Good Enough: Transfer Learning ML Models for Accurate VPT2 Frequencies
The calculation of the anharmonic modes of small to medium sized molecules for assigning experimentally measured frequencies to the corresponding type of molecular motions is computationally challenging at sufficiently high levels of quantum chemical theory.
Transfer Learning
Chemical Physics
Site-Selective Dynamics of Azidolysozyme
Attaching azide to alanine residues can yield dynamics that decays to zero on the few ps time scale (i. e. static component $\Delta_0 \sim 0$ ps$^{-1}$) or to a remaining, static contribution of $\sim 0. 5$ ps$^{-1}$ (corresponding to 2. 5 cm$^{-1}$), depending on the local environment on the 10 ps time scale.
Biological Physics
Reactive Dynamics and Spectroscopy of Hydrogen Transfer from Neural Network-Based Reactive Potential Energy Surfaces
It is used to run finite-temperature molecular dynamics simulations, and to determine the infrared spectra and the hydrogen transfer rates for the three molecules.
Chemical Physics
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
Further, two new datasets are generated in order to probe the performance of ML models for describing chemical reactions, long-range interactions, and condensed phase systems.
Ranked #6 on
Formation Energy
on QM9
Drug Discovery
Formation Energy
Chemical Physics
