Automated Parameterization of Quantum Mechanically Derived Force Fields for Soft Materials and Complex Fluids: Development and Validation
procedure
Chemical Sciences not elsewhere classified
Information Systems not elsewhere classified
Biophysics
macroscopic properties
01 natural sciences
Mathematical Sciences not elsewhere classified
Lengthy atomistic MD simulations
Space Science
5 CB systems
QMD
phase
molecular dynamics (MD) simulations
fluid
Quantum Mechanically Derived Force .
QM
QMD-FF
ab initio quantum mechanical (QM) data
FF parameterization protocol
fragmentation reconstruction method
541
0104 chemical sciences
ab initio quantum
intramolecular FF term
Medicine
adopted force field (FF)
FRM
Physical Sciences not elsewhere classified
Biotechnology
Biological Sciences not elsewhere classified
DOI:
10.1021/acs.jctc.1c00213
Publication Date:
2021-06-29T20:43:53Z
AUTHORS (5)
ABSTRACT
The reliability of molecular dynamics (MD) simulations in predicting macroscopic properties complex fluids and soft materials, such as liquid crystals, colloidal suspensions, or polymers, relies on the accuracy adopted force field (FF). We present an automated protocol to derive specific accurate FFs, fully based ab initio quantum mechanical (QM) data. integration Joyce Picky procedures, recently proposed by our group provide description simple liquids, is here extended larger molecules, capable exhibiting more fluid phases. While standard employed parameterize intramolecular FF term, a new procedure handle computational cost QM calculations required for parameterization intermolecular term. latter thus obtained integrating old with fragmentation reconstruction method (FRM) that allows reliable, yet computationally feasible sampling energy surface at level. whole tested benchmark crystal, performances resulting mechanically derived (QMD) were compared those delivered general-purpose, transferable one, third, "hybrid" FF, where only bonded terms refined against Lengthy atomistic MD are carried out each 5CB systems both isotropic nematic phases, eventually validating comparing other models experiments. QMD-FF yields best performances, reproducing phases correct range temperatures well describing their structure, dynamics, thermodynamic properties, providing clear may be explored predict materials.
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