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Quantum chemistry structures and properties of 134 kilo molecules
Computational de novo design of new drugs and materials requires rigorous and unbiased exploration of chemical compound space. However, large uncharted territories persist due to its size scalingExpand
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Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.
Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategyExpand
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Nonadiabatic Excited-State Dynamics with Machine Learning
We show that machine learning (ML) can be used to accurately reproduce nonadiabatic excited-state dynamics with decoherence-corrected fewest switches surface hopping in a 1-D model system. We proposeExpand
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Calculating distribution coefficients based on multi-scale free energy simulations: an evaluation of MM and QM/MM explicit solvent simulations of water-cyclohexane transfer in the SAMPL5 challenge
One of the central aspects of biomolecular recognition is the hydrophobic effect, which is experimentally evaluated by measuring the distribution coefficients of compounds between polar and apolarExpand
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Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Theory, Implementation, and Parameters
Semiempirical orthogonalization-corrected methods (OM1, OM2, and OM3) go beyond the standard MNDO model by explicitly including additional interactions into the Fock matrix in an approximate mannerExpand
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Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Benchmarks for Ground-State Properties
The semiempirical orthogonalization-corrected OMx methods (OM1, OM2, and OM3) go beyond the standard MNDO model by including additional interactions in the electronic structure calculation. WhenExpand
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Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels.
We present an efficient approach for generating highly accurate molecular potential energy surfaces (PESs) using self-correcting, kernel ridge regression (KRR) based machine learning (ML). WeExpand
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Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations
We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class ofExpand
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The relationship between threshold voltage and dipolar character of self-assembled monolayers in organic thin-film transistors.
We report a quantitative study that describes and correlates the threshold voltage of low-voltage organic field-effect transistors with the molecular structure of self-assembled monolayerExpand
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Doped polycyclic aromatic hydrocarbons as building blocks for nanoelectronics: a theoretical study.
Density functional theory (DFT) and semiempirical UHF natural orbital configuration interaction (UNO-CI) calculations are used to investigate the effect of heteroatom substitution at the centralExpand
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