Real-time feedback from iterative electronic structure calculations

Vaucher, Alain C.; Haag, Moritz P.; Reiher, Markus

doi:10.1002/jcc.24268

Cited by 30 publications

(54 citation statements)

References 16 publications

(25 reference statements)

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“…In recent work, we have shown that exploration of molecular configuration space by human participants using iMD-VR to steer 'on-the-fly' ab initio MD can be used to generate molecular geometries for training GPU-accelerated neural networks (NN) to learn reactive potential energy surfaces (PESs). 13 Video 5 (vimeo.com/311438872) shows our first application using this strategy, focused on hydrogen abstraction reactions of CN radical + isopentane using real-time semi-empirical quantum chemistry through a plugin to the SCINE Sparrow package developed by Reiher and co-workers [85][86][87] (scine.ethz.ch), which includes implementations of tight-binding engines like DFTB alongside a suite of other semi-empirical methods. 47 To obtain the results described herein, we have utilized the SCINE Sparrow implementation of PM6, with the default set of parameters.…”

Section: Using Imd-vr To Train Neural Network To Learn Reactive Pessmentioning

confidence: 99%

Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework

O’Connor

Bennie

Deeks

et al. 2019

The Journal of Chemical Physics

106

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As molecular scientists have made progress in their ability to engineer nano-scale molecular structure, we are facing new challenges in our ability to engineer molecular dynamics (MD) and flexibility. Dynamics at the molecular scale differs from the familiar mechanics of everyday objects, because it involves a complicated, highly correlated, and threedimensional many-body dynamical choreography which is often non-intuitive even for highly trained researchers. We recently described how interactive molecular dynamics in virtual reality (iMD-VR) can help to meet this challenge, enabling researchers to manipulate real-time MD simulations of flexible structures in 3D. In this article, we outline various efforts to extend immersive technologies to the molecular sciences, and we introduce 'Narupa', a flexible, opensource, multi-person iMD-VR software framework which enables groups of researchers to simultaneously cohabit realtime simulation environments to interactively visualize and manipulate the dynamics of molecular structures with atomic-level precision. We outline several application domains where iMD-VR is facilitating research, communication, and creative approaches within the molecular sciences, including training machines to learn reactive potential energy surfaces (PESs), biomolecular conformational sampling, protein-ligand binding, reaction discovery using 'on-the-fly' quantum chemistry, and transport dynamics in materials. We touch on iMD-VR's various cognitive and perceptual affordances, and how these provide research insight for molecular systems. By synergistically combining human spatial reasoning and design insight with computational automation, technologies like iMD-VR have the potential to improve our ability to understand, engineer, and communicate microscopic dynamical behavior, offering the potential to usher in a new paradigm for engineering molecules and nano-architectures.

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Section: Using Imd-vr To Train Neural Network To Learn Reactive Pessmentioning

confidence: 99%

Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework

O’Connor

Bennie

Deeks

et al. 2019

The Journal of Chemical Physics

106

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“…In order to guarantee reliable real-time feedback, we introduced a mediator strategy. 258 The mediator creates surrogate potentials that approximate the potential energy surface and enable high-frequency feedback. In addition, the mediator restricts the reactivity exploration to regions of configuration space for which the available orbitals are reliable until new data becomes eventually available.…”

Section: Interactivity For Quantum Mechanical Calculationsmentioning

confidence: 99%

Exploration of Reaction Pathways and Chemical Transformation Networks

2018

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For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and in an automated fashion. These approaches differ in their application range, the level of completeness of the exploration, as well as the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.

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“…We also showed that a correction to

^{χ} G^{NDDO} ({{\overset{false˜}{bold-italicR}}_{I}^{(n + 1)}})

can be constructed in different ways, departing from a Roby‐Sinanoǧlu‐type approach. We proposed to construct additive corrections Γ J and Γ K to the matrices χ J NDDO and to χ K NDDO , respectively,

\begin{matrix} lefttrue \\ ^{ϕ} G (({}, {\overset{R}{true}}_{normalI}^{((), n + 1)})) \approx & {boldΓ}_{boldJ} (({}, {\overset{R}{true}}_{normalI}^{normaln})) +^{χ} {bold-italicJ}^{NDDO} (({}, {\overset{R}{true}}_{normalI}^{((), normaln + 1)})) + {boldΓ}_{boldK} (({}, {\overset{R}{true}}_{normalI}^{normaln})) +^{χ} {bold-italicK}^{NDDO} (({}, {\overset{R}{true}}_{normalI}^{((), normaln + 1)})) . \end{matrix}

The CISE approach has a potential for application whenever we are interested in obtaining electronic energies for sequences of related structures, for example, in the context of kinetic modeling, in real‐time and automated reaction‐mechanism explorations, or in reaction and first‐principles molecular dynamics simulations. The CISE approach differs conceptually from the existing NDDO‐SEMO models insofar as that no determination of parameters in a statistical calibration is required.…”

Section: Implicit Description Of Electron Correlation Effects Throughmentioning

confidence: 99%

“…Instead, they opened up different areas of application which can broadly be divided into three categories (see also Ref. for a recent review): (1) simulations of very large systems such as proteins and those with thousands of small molecules, (2) calculations for a large number of isolated and unrelated medium‐sized molecules, for example, in virtual high‐throughput screening schemes for materials discovery and docking‐and‐scoring of potential drug candidates, and (3) entirely new applications such as real‐time quantum chemistry where ultra‐fast SEMO models allow the perception of visual and haptic feedback in real time when manipulating medium‐sized molecular structures …”

Section: Introductionmentioning

confidence: 99%

Semiempirical molecular orbital models based on the neglect of diatomic differential overlap approximation

Husch

Vaucher

Reiher

2018

Int J of Quantum Chemistry

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Semiempirical molecular orbital (SEMO) models based on the neglect of diatomic differential overlap (NDDO) approximation efficiently solve the self-consistent field equations by rather drastic approximations. The computational efficiency comes at the cost of an error in the electron-electron repulsion integrals. The error may be compensated by the introduction of parametric expressions to evaluate the electron-electron repulsion integrals, the one-electron integrals, and the core-core repulsion. We review the resulting formalisms of popular NDDO-SEMO models (such as the MNDO(/d), AM1, PMx, and OMx models) in a concise and self-contained manner. We discuss the approaches to implicitly and explicitly describe electron correlation effects within NDDO-SEMO models and we dissect strengths and weaknesses of the different approaches in a detailed analysis. For this purpose, we consider the results of recent benchmark studies. Furthermore, we apply bootstrapping to perform a sensitivity analysis for a selection of parameters in the MNDO model. We also identify systematic limitations of NDDO-SEMO models by drawing on an analogy to Kohn-Sham density functional theory. K E Y W O R D SNDDO approximation, semi-empirical methods | INTRODUCTIONThe driving force for the development of semiempirical molecular orbital (SEMO) models has always been the desire to accelerate quantum chemical calculations. At the outset of the development of SEMO models in the middle of the last century, [1][2][3][4][5][6][7][8][9][10] the goal was to carry out electronic structure calculations for small molecules, which was not routinely possible with ab initio electronic structure methods at that time. Since then, theoretical chemistry has seen a remarkable development not only in terms of computational resources but also in terms of ab initio methodology. [11] One must not forget that most electronic structure methods which we apply routinely today, such as Kohn-Sham density functional theory (KS-DFT) [12] and coupled cluster theory, [13] were developed concurrently with today's SEMO models. As a consequence of algorithmic and methodological developments, [11] accurate ab initio electronic structure methods have long replaced SEMO models in their original areas of application (electronic structure calculations for small molecules). Nevertheless, SEMO models did not become extinct. Instead, they opened up different areas of application which can broadly be divided into three categories (see also Ref. [14] for a recent review): (1) simulations of very large systems such as proteins [15][16][17][18][19][20][21][22] and those with thousands of small molecules, [23,24] (2) calculations for a large number of isolated and unrelated medium-sized molecules, for example, in virtual high-throughput screening schemes for materials discovery [25,26] and docking-and-scoring of potential drug candidates, [27][28][29][30][31][32] and (3) entirely new applications such as real-time quantum chemistry where ultra-fast SEMO models allow the perception of visual and h...

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Real-time feedback from iterative electronic structure calculations

Cited by 30 publications

References 16 publications

Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework

Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework

Exploration of Reaction Pathways and Chemical Transformation Networks

Semiempirical molecular orbital models based on the neglect of diatomic differential overlap approximation

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