Electrostatically Embedded Generalized Molecular Fractionation with Conjugate Caps Method for Full Quantum Mechanical Calculation of Protein Energy

Wang, Xianwei; Liu, Jinfeng; Zhang, John Z. H.; He, Xiao

doi:10.1021/jp400779t

Cited by 109 publications

(187 citation statements)

References 59 publications

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“…29,[36][37][38] Our FRAGME∩T-based implementation of the n-body expansion computes E (n) self according to Eq. (3.1) and then subtracts it from E (n) in Eq.…”

Section: Methodsmentioning

confidence: 99%

Understanding the many-body expansion for large systems. I. Precision considerations

Richard

Lao

Herbert

2014

The Journal of Chemical Physics

174

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Electronic structure methods based on low-order "n-body" expansions are an increasingly popular means to defeat the highly nonlinear scaling of ab initio quantum chemistry calculations, taking advantage of the inherently distributable nature of the numerous subsystem calculations. Here, we examine how the finite precision of these subsystem calculations manifests in applications to large systems, in this case, a sequence of water clusters ranging in size up to (H₂O)₄₇. Using two different computer implementations of the n-body expansion, one fully integrated into a quantum chemistry program and the other written as a separate driver routine for the same program, we examine the reproducibility of total binding energies as a function of cluster size. The combinatorial nature of the n-body expansion amplifies subtle differences between the two implementations, especially for n ⩾ 4, leading to total energies that differ by as much as several kcal/mol between two implementations of what is ostensibly the same method. This behavior can be understood based on a propagation-of-errors analysis applied to a closed-form expression for the n-body expansion, which is derived here for the first time. Discrepancies between the two implementations arise primarily from the Coulomb self-energy correction that is required when electrostatic embedding charges are implemented by means of an external driver program. For reliable results in large systems, our analysis suggests that script- or driver-based implementations should read binary output files from an electronic structure program, in full double precision, or better yet be fully integrated in a way that avoids the need to compute the aforementioned self-energy. Moreover, four-body and higher-order expansions may be too sensitive to numerical thresholds to be of practical use in large systems.

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“…29,[36][37][38] Our FRAGME∩T-based implementation of the n-body expansion computes E (n) self according to Eq. (3.1) and then subtracts it from E (n) in Eq.…”

Section: Methodsmentioning

confidence: 99%

Understanding the many-body expansion for large systems. I. Precision considerations

Richard

Lao

Herbert

2014

The Journal of Chemical Physics

174

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“…We apply a subsystem-based parametrization scheme with a molecular partitioning strategy similar to the GMFCC approach by Zhang and coworkers. [90][91][92] The system is divided into two halves at a bond close to its center and the ends of the resulting subsystems are saturated with hydrogen atoms by placing them along the cut bond vector at a distance determined by the van der Waals radii of the involved elements. Figure 3: Three different partitionings of the pseudo-one-dimensional molecular structure OCE for the subsystem parametrization procedure.…”

Section: Ii4 Fragmentation Of Large Systems For Parametrizationmentioning

confidence: 99%

Self-Parametrizing System-Focused Atomistic Models

Brunken

Reiher

2020

J. Chem. Theory Comput.

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Computational studies of chemical reactions in complex environments such as proteins, nanostructures, or on surfaces require accurate and efficient atomistic models applicable to the nanometer scale. In general, an accurate parametrization of the atomistic entities will not be available for arbitrary system classes, but demands a fast automated system-focused parametrization procedure to be quickly applicable, reliable, flexible, and reproducible. Here, we develop and combine an automatically parametrizable quantum chemically derived molecular mechanics model with machine-learned corrections under autonomous uncertainty quantification and refinement. Our approach first generates an accurate, physically motivated model from a minimum energy structure and its corresponding Hessian matrix by a partial Hessian fitting procedure of the force constants. This model is then the starting point to generate a large number of configurations for which additional off-minimum reference data can be evaluated on the fly. A ∆-machine learning model is trained on these data to provide a correction to energies and forces including uncertainty estimates. During the procedure, the flexibility of the machine learning model is tailored to the amount of available training data. The parametrization of large systems is enabled by a fragmentation approach. Due to their modular nature, all model construction steps allow for model improvement in a rolling fashion. Our approach may also be employed for the generation of system-focused electrostatic molecular mechanics embedding environments in a quantum mechanical molecular-mechanical hybrid model for arbitrary atomistic structures at the nanoscale.

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“…144–146 Ideas have been proposed for nonempirically incorporating short-range nonbond repulsions by introducing inter-fragment orthogonality restraints into X-Pol 147 but practical applications of those ideas have not yet been explored.…”

Section: Inter-fragment Coupling Schemesmentioning

confidence: 99%

Quantum mechanical force fields for condensed phase molecular simulations

Giese

York

2017

J. Phys.: Condens. Matter

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Molecular simulations are powerful tools for providing atomic-level details into complex chemical and physical processes that occur in the condensed phase. For strongly interacting systems where quantum many-body effects are known to play an important role, density-functional methods are often used to provide the model for the potential energy used to drive dynamics. These methods, however, suffer from two major drawbacks. First, they are often too computationally intensive to practically apply to large systems over long time scales, limiting their scope of application. Second, there remain challenges for these models to obtain the necessary level of accuracy for weak non-bonded interactions to obtain quantitative accuracy for a wide range of condensed phase properties. Quantum mechanical force fields (QMFFs) provide a potential solution to both of these limitations. In this review, we address recent advances in the development of QMFFs for condensed phase simulations. In particular, we examine the development of QMFF models using both approximate and ab initio density-functional models, the treatment of short-ranged non-bonded and long-ranged electrostatic interactions, and stability issues in molecular dynamics calculations. Example calculations are provided for crystalline systems, liquid water, and ionic liquids. We conclude with a perspective for emerging challenges and future research directions.

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Electrostatically Embedded Generalized Molecular Fractionation with Conjugate Caps Method for Full Quantum Mechanical Calculation of Protein Energy

Cited by 109 publications

References 59 publications

Understanding the many-body expansion for large systems. I. Precision considerations

Understanding the many-body expansion for large systems. I. Precision considerations

Self-Parametrizing System-Focused Atomistic Models

Quantum mechanical force fields for condensed phase molecular simulations

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