[HTML][HTML] DFTB+, a software package for efficient approximate density functional theory based atomistic simulations
DFTB+ is a versatile community developed open source software package offering fast and
efficient methods for carrying out atomistic quantum mechanical simulations. By …
efficient methods for carrying out atomistic quantum mechanical simulations. By …
Crystal structure prediction methods for organic molecules: State of the art
DH Bowskill, IJ Sugden… - Annual Review of …, 2021 - annualreviews.org
The prediction of the crystal structures that a given organic molecule is likely to form is an
important theoretical problem of significant interest for the pharmaceutical and agrochemical …
important theoretical problem of significant interest for the pharmaceutical and agrochemical …
Proximity Effect in Crystalline Framework Materials: Stacking‐Induced Functionality in MOFs and COFs
Metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) consist of
molecular building blocks being stitched together by strong bonds. They are well known for …
molecular building blocks being stitched together by strong bonds. They are well known for …
Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules
We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM)
dataset that contains the structural and electronic information of 59,783 low-and high-energy …
dataset that contains the structural and electronic information of 59,783 low-and high-energy …
QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for≈ 4.2
million equilibrium and non-equilibrium structures of small organic molecules with up to …
million equilibrium and non-equilibrium structures of small organic molecules with up to …
Data-efficient machine learning for molecular crystal structure prediction
The combination of modern machine learning (ML) approaches with high-quality data from
quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost …
quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost …
Accurate many-body repulsive potentials for density-functional tight binding from deep tensor neural networks
We combine density-functional tight binding (DFTB) with deep tensor neural networks
(DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and …
(DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and …
A hybrid machine learning approach for structure stability prediction in molecular co-crystal screenings
Co-crystals are a highly interesting material class as varying their components and
stoichiometry in principle allows tuning supramolecular assemblies toward desired physical …
stoichiometry in principle allows tuning supramolecular assemblies toward desired physical …
Nonlocal van der Waals functionals for solids: Choosing an appropriate one
The nonlocal van der Waals (NL-vdW) functionals [M. Dion, Phys. Rev. Lett. 92, 246401
(2004) PRLTAO 0031-9007 10.1103/PhysRevLett. 92.246401] are being applied more and …
(2004) PRLTAO 0031-9007 10.1103/PhysRevLett. 92.246401] are being applied more and …
Treating Semiempirical Hamiltonians as Flexible Machine Learning Models Yields Accurate and Interpretable Results
Quantum chemistry provides chemists with invaluable information, but the high
computational cost limits the size and type of systems that can be studied. Machine learning …
computational cost limits the size and type of systems that can be studied. Machine learning …