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- Journal Article
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Machine learning potentials for metal-organic frameworks using an incremental learning approach
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The influence of heuristic and ad-hoc choices on the determination of lubricant viscosities from equilibrium molecular dynamics simulations
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- Journal Article
- A1
- open access
DFT-quality adsorption simulations in metal-organic frameworks enabled by machine learning potentials
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- Conference Paper
- C3
- open access
Reliable calculation of lubricant properties from equilibrium molecular dynamics simulations
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- Journal Article
- A1
- open access
Modeling electronic response properties with an explicit-electron machine learning potential
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- Journal Article
- A1
- open access
GloMPO (Globally Managed Parallel Optimization) : a tool for expensive, black-box optimizations, application to ReaxFF reparameterizations
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- Conference Paper
- C3
- open access
Reliable calculation of thermophysical lubricant properties from equilibrium molecular dynamics simulations
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- Journal Article
- A1
- open access
Super-ions of sodium cations with hydrated hydroxide anions : inorganic structure-directing agents in zeolite synthesis
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- Conference Paper
- C3
- open access
Molecular Dynamics simulations for constitutive modelling of TEHL
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- Journal Article
- A1
- open access
Extension of the QuickFF force field protocol for an improved accuracy of structural, vibrational, mechanical and thermal properties of metal-organic frameworks
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- Journal Article
- A1
- open access
The Monomer Electron Density Force Field (MEDFF) : a physically inspired model for noncovalent interactions
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- Journal Article
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- open access
Methane adsorption in Zr-based MOFs : comparison and critical evaluation of force fields
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- Journal Article
- A1
- open access
QuickFF: a program for a quick and easy derivation of force fields for metal-organic frameworks from ab initio input
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Ab initio parametrized force field for the metal-organic framework MIL-53(Al) to study its framework flexibility
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Valence force fields for microporous materials