Embedding Methods for Quantum Chemistry: Applications from Materials to Life Sciences

Jones, Leighton O.; Mosquera, Martín A.; Schatz, George C.; Ratner, Mark A.

doi:10.1021/jacs.9b10780

Cited by 114 publications

(103 citation statements)

References 144 publications

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“…One approach that allows an explicit term‐by‐term embedding is by employing potentials that include separate terms for each physical interaction, such as the effective fragment potential (EFP) which can include exchange and charge transfer 115 and using the induced dipole model as in the polarizable embedding model by Mennucci et al, 51 the fragment exchange potential, 116 the QMFF, 117 Exchange fragment potential, 118 among others. See the recent review of QM:MM and QM:QM embedding approaches by Jones et al for a thorough review 119 …”

Section: Qm/mm With Advanced Potentialsmentioning

confidence: 99%

Development and application of quantum mechanics/molecular mechanics methods with advanced polarizable potentials

Nochebuena

Naseem-Khan

Cisneros

2021

WIREs Comput Mol Sci

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Quantum mechanics/molecular mechanics (QM/MM) simulations are a popular approach to study various features of large systems. A common application of QM/MM calculations is in the investigation of reaction mechanisms in condensed‐phase and biological systems. The combination of QM and MM methods to represent a system gives rise to several challenges that need to be addressed. The increase in computational speed has allowed the expanded use of more complicated and accurate methods for both QM and MM simulations. Here, we review some approaches that address several common challenges encountered in QM/MM simulations with advanced polarizable potentials, from methods to account for boundary across covalent bonds and long‐range effects, to polarization and advanced embedding potentials. This article is categorized under: Electronic Structure Theory > Combined QM/MM Methods Molecular and Statistical Mechanics > Molecular Interactions Software > Simulation Methods

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Section: Qm/mm With Advanced Potentialsmentioning

confidence: 99%

Development and application of quantum mechanics/molecular mechanics methods with advanced polarizable potentials

Nochebuena

Naseem-Khan

Cisneros

2021

WIREs Comput Mol Sci

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“…To harvest benefits of both quantum and atomistic simulations, a well-established DC strategy is to treat a small region involved in interested chemical reaction at QM detail and its surrounding with MMFF [ 68 , 69 , 70 , 71 , 72 , 73 ]. This series of pioneering work was awarded Nobel prize in 2013, and QM-MM treatment continues to be the mainstream methodology for computational description of chemical reactions [ 74 , 75 ].…”

Section: DC and “Caching” In Traditional Molecular Modelingmentioning

confidence: 99%

“Dividing and Conquering” and “Caching” in Molecular Modeling

Cao

Tian

2021

IJMS

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Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes “dividing and conquering” and/or “caching” in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of “dividing and conquering” and “caching” along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution “caching” of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for “dividing and conquering” and “caching” in complex molecular systems.

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“…[64][65][66] 3 | PRESENT Density embedding: Recent applications have included charge transfer reactions, [67] charge transfer excitation energies and diabatic couplings, [68] van der Waals interactions, [69] spectroscopy of complex systems and solvatochromatic shifts, [70][71][72][73][74][75] and many more. [21,[76][77][78] Numerically accurate (although inadequate in practical calculations) methods can calculate δT Snad /δn(r) for covalent bonds. [42,76] However, the performance of an approximate self-consistent subsystem DFT for potential energy curves is well understood, especially for weakly interacting systems.…”

Section: Qm/mm and Continuum Dielectrics Embeddingmentioning

confidence: 99%

Quantum embedding electronic structure methods

Wasserman

Pavanello

2020

Int J of Quantum Chemistry

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This editorial serves as an introduction to a series of 10 papers collected under the special issue "Quantum Embedding Electronic Structure Methods." The idea to assemble a special issue came during a symposium at the Spring 2019 meeting of the American Chemical Society where we (Adam and Michele) organized a Physical Chemistry symposium bearing the same name as the special issue. The symposium ran for the entire week and featured many flavors of embedding: from density embedding to embedding within lattice models, as well as continuum models and many-body expansions. While it may seem that these approaches are far removed from each other, the symposium highlighted common goals, such as the reduction of the computational effort through "divide-and-conquer" strategies, and stressed the importance of establishing a common language across all embedding methods. It is clear that human interaction is key for scientific progress, and traveling to conferences is an important aspect of scientific research. Unfortunately, we write this introduction during a pandemic of historic proportions, when working on theoretical and computational chemistry may seem like an unacceptable luxury or an unjustified distraction. It is striking to remember how we interacted prior to the outbreak. Figure 1 provides a glimpse into the social nature of the symposium participants and the massive crowds at the conference. The editorial is organized into three sections: Origins, where we briefly discuss the origins of embedding methods for newcomers to the field; Present, where we highlight some of the current efforts with an emphasis on the contributions submitted to this Special Issue of IJQC; and Future, where we enumerate a few of the open challenges in the field and speculate on some of its possible future directions. 2 | ORIGINS Modern embedding methods can be broadly classified into three groups: density embedding, density-matrix and Green's function (GF) embedding, and classical (quantum mechanics / molecular mechanics (QM/MM) and continuum dielectrics) embedding. We briefly discuss the origins of these three approaches separately. orbitals. The KSCED follows by requiring that E[n] be minimized directly with regard to variations of the n α (r). A different set of equations is

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Embedding Methods for Quantum Chemistry: Applications from Materials to Life Sciences

Cited by 114 publications

References 144 publications

Development and application of quantum mechanics/molecular mechanics methods with advanced polarizable potentials

Development and application of quantum mechanics/molecular mechanics methods with advanced polarizable potentials

“Dividing and Conquering” and “Caching” in Molecular Modeling

Quantum embedding electronic structure methods

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