A transferrable range-separated force field for water: Combining the power of both physically-motivated models and machine learning techniques

Yang, Lan; Li, Jichen; Chen, Feiyang; Yu, Kuang

doi:10.1063/5.0128780

Cited by 13 publications

(17 citation statements)

References 125 publications

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“…Admittedly, a full NN augmentation of the chemical interaction space in the manner we propose will require an intimidating amount of QM calculations. Nonetheless, because the model parameters are extracted from dimer computations only, with only occasional multimer energies as a check, this effort is far more scalable than other similar efforts that employ multimers. ,, …”

Section: Discussionmentioning

confidence: 99%

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions

Illarionov,

Sakipov,

Pereyaslavets

et al. 2023

J. Am. Chem. Soc.

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A key goal of molecular modeling is the accurate reproduction of the true quantum mechanical potential energy of arbitrary molecular ensembles with a tractable classical approximation. The challenges are that analytical expressions found in general purpose force fields struggle to faithfully represent the intermolecular quantum potential energy surface at close distances and in strong interaction regimes; that the more accurate neural network approximations do not capture crucial physics concepts, e.g., nonadditive inductive contributions and application of electric fields; and that the ultra-accurate narrowly targeted models have difficulty generalizing to the entire chemical space. We therefore designed a hybrid wide-coverage intermolecular interaction model consisting of an analytically polarizable force field combined with a short-range neural network correction for the total intermolecular interaction energy. Here, we describe the methodology and apply the model to accurately determine the properties of water, the free energy of solvation of neutral and charged molecules, and the binding free energy of ligands to proteins. The correction is subtyped for distinct chemical species to match the underlying force field, to segment and reduce the amount of quantum training data, and to increase accuracy and computational speed. For the systems considered, the hybrid ab initio parametrized Hamiltonian reproduces the two-body dimer quantum mechanics (QM) energies to within 0.03 kcal/mol and the nonadditive many-molecule contributions to within 2%. Simulations of molecular systems using this interaction model run at speeds of several nanoseconds per day.

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Section: Discussionmentioning

confidence: 99%

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions

Illarionov,

Sakipov,

Pereyaslavets

et al. 2023

J. Am. Chem. Soc.

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“…This water model is based on a multipolar polarizable potential, which is quite similar to AMOEBA and MPID. 37 But the atomic charges and leading dispersion coefficients are not constants but can fluctuate linearly depending on the bond lengths and bond angles. While this combined fluctuating charge and polarizable model can significantly improve the description of the intermolecular electrostatic energy, its implementation requires an intensive modification to the existing MD program.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…To show the convenience brought by DMFF, we give a proof-of-concept example, in which a new water model is implemented. This water model is based on a multipolar polarizable potential, which is quite similar to AMOEBA and MPID . But the atomic charges and leading dispersion coefficients are not constants but can fluctuate linearly depending on the bond lengths and bond angles.…”

Section: Resultsmentioning

confidence: 99%

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DMFF: An Open-Source Automatic Differentiable Platform for Molecular Force Field Development and Molecular Dynamics Simulation

Wang,

Li,

Yang

et al. 2023

J. Chem. Theory Comput.

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In the simulation of molecular systems, the underlying force field (FF) model plays an extremely important role in determining the reliability of the simulation. However, the quality of the state-of-the-art molecular force fields is still unsatisfactory in many cases, and the FF parameterization process largely relies on human experience, which is not scalable. To address this issue, we introduce DMFF, an open-source molecular FF development platform based on an automatic differentiation technique. DMFF serves as a powerful tool for both top-down and bottom-up FF development. Using DMFF, both energies/forces and thermodynamic quantities such as ensemble averages and free energies can be evaluated in a differentiable way, realizing an automatic, yet highly flexible FF optimization workflow. DMFF also eases the evaluation of forces and virial tensors for complicated advanced FFs, helping the fast validation of new models in molecular dynamics simulation. DMFF has been released as an open-source package under the LGPL-3.0 license and is available at https://github.com/deepmodeling/DMFF.

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“…The physics-driven nonbonding terms of PhyNEO are further separated into long-range E lr nb and short-range E sr nb contributions, as done in our and others’ previous studies. ,,, The four parameterization steps of PhyNEO are described below:…”

Section: Methods and Computational Detailsmentioning

confidence: 99%

PhyNEO: A Neural-Network-Enhanced Physics-Driven Force Field Development Workflow for Bulk Organic Molecule and Polymer Simulations

Chen,

2023

J. Chem. Theory Comput.

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An accurate, generalizable, and transferable force field plays a crucial role in the molecular dynamics simulations of organic polymers and biomolecules. Conventional empirical force fields often fail to capture precise intermolecular interactions due to their negligence of important physics, such as polarization, charge penetration, many-body dispersion, etc. Moreover, the parameterization of these force fields relies heavily on top-down fittings, limiting their transferabilities to new systems where the experimental data are often unavailable. To address these challenges, we introduce a general and fully ab initio force field construction strategy, named PhyNEO. It features a hybrid approach that combines both the physics-driven and the data-driven methods and is able to generate a bulk potential with chemical accuracy using only quantum chemistry data of very small clusters. Careful separations of long-/short-range interactions and nonbonding/bonding interactions are the key to the success of PhyNEO. By such a strategy, we mitigate the limitations of pure data-driven methods in long-range interactions, thus largely increasing the data efficiency and the scalability of machine learning models. The new approach is thoroughly tested on poly(ethylene oxide) and polyethylene glycol systems, giving superior accuracies in both microscopic and bulk properties compared to conventional force fields. This work thus offers a promising framework for the development of advanced force fields in a wide range of organic molecular systems.

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A transferrable range-separated force field for water: Combining the power of both physically-motivated models and machine learning techniques

Cited by 13 publications

References 125 publications

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions

DMFF: An Open-Source Automatic Differentiable Platform for Molecular Force Field Development and Molecular Dynamics Simulation

PhyNEO: A Neural-Network-Enhanced Physics-Driven Force Field Development Workflow for Bulk Organic Molecule and Polymer Simulations

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