Evaluation test of the most popular models of methanol using selected thermodynamic, dynamic and structural properties

Khasawneh, Khitam; Obeidat, Abdalla; Abu-Ghazleh, Hind; Al-Salman, Rakan; Al-Ali, Manal

doi:10.1016/j.molliq.2019.111914

Cited by 8 publications

(9 citation statements)

References 46 publications

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“…Water, 64 acetonitrile, 65 and methanol 66,67 reference RDF curves are from neutron diffraction experiments. Results from classical MD simulations 68–74 are also shown in Fig. 3.…”

Section: Resultsmentioning

confidence: 99%

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Modeling molecular ensembles with gradient-domain machine learning force fields

et al. 2023

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“…Water, 64 acetonitrile, 65 and methanol 66,67 reference RDF curves are from neutron diffraction experiments. Results from classical MD simulations 68–74 are also shown in Fig. 3.…”

Section: Resultsmentioning

confidence: 99%

“…63 Note that NVT simulations could articially bias intermolecular distances due to the volume constraint. NPT simulations would be a more rigorous metric, but these are not yet implemented in mbGDML, and this will be [68][69][70][71][72][73][74] are also shown in Fig. 3.…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

Modeling molecular ensembles with gradient-domain machine learning force fields

et al. 2023

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“…Water, 55 acetonitrile, 56 and methanol 57,58 reference RDF curves are from neutron diffraction experiments. Results from classical MD simulations [59][60][61][62][63][64][65] are also shown in Fig. 3.…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 98%

“…Classical simulations using GROMOS96 and OPLS/AA potentials have significantly more ordered liquid structure. 65 For instance, their g OH (r) peaks are around 1.24 higher in intensity than mbGDML. While the g OO (r) is in good agreement with the experiment beyond 5 Å, the g OH (r) and g HH (r) curves are missing long-range liquid structure.…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

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Modeling molecular ensembles with gradient-domain machine learning force fields

Maldonado

Poltavsky

Vassilev-Galindo

et al. 2023

Preprint

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Gradient-domain machine learning (GDML) force fields have shown excellent accuracy, data efficiency, and applicability for molecules with hundreds of atoms, but the employed global descriptor limits transferability to ensembles of molecules. Many-body expansions (MBEs) should provide a rigorous procedure for size-transferable GDML by training models on fundamental n-body interactions. We developed many-body GDML (mbGDML) force fields for water, acetonitrile, and methanol by training 1-, 2-, and 3-body models on only 1000 MP2/def2-TZVP calculations each. Our mbGDML force field includes intramolecular flexibility and intermolecular interactions, providing that the reference data properly describes these effects. We also compare this mbGDML approach to GAP, SchNet, and NequIP potentials. Energy and force predictions of clusters containing up to 20 molecules are within 0.38 kcal/mol per monomer of reference supersystem calculations. This deviation partially arises from the restriction of the mbGDML model to 3-body interactions. Given these approximations, our automated mbGDML training schemes also resulted in fair agreement with reference radial distribution functions (RDFs) of bulk solvents. These results highlight mbGDML as valuable for modeling explicitly solvated systems with quantum-mechanical accuracy.

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