Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. We introduce a transferable high-fidelity neural network representation of such data i...
BACKGROUND: Despite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexit...
This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree-Fock computations. A MOB pairwise decomposition ...
Physical chemistry chemical physics : PCCP
36069366
We provide a comprehensive overview of the chemical information from electron density: not only how to extract information, but also how to obtain and how to assess the quality of the electron density itself. After introducing several indexes derived...
Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. The technique has several limitations, however, that make analyzing the data it generates time-intensiv...
Journal of chemical theory and computation
36317712
We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small data sets (≈60 DMC calculations in total). The first uses voxel deep neural networks (VDNNs) to predict DMC energy densities ...
During the past 10 years, biological electron cryo-microscopy (cryoEM) has undergone a process of rapid transformation. Many things we could only dream about a decade ago have now become almost routine. Nevertheless, a number of challenges remain, to...
Rapid communications in mass spectrometry : RCM
36098223
RATIONALE: Gas chromatography-mass spectrometry (GC-MS) is an analytical technique widely used in materials science, biomedicine, and other fields. The target compound in the experiment is identified by searching for its mass spectrum in a large mass...
We report the development of deep-learning coherent electron diffractive imaging at subangstrom resolution using convolutional neural networks (CNNs) trained with only simulated data. We experimentally demonstrate this method by applying the trained ...
Accurately determining the second-order rate constant with e (k) for organic compounds (OCs) is crucial in the e induced advanced reduction processes (ARPs). In this study, we collected 867 k values at different pHs from peer-reviewed publications an...