arXiv subjects

David R. Glowacki

Publications and source records attributed to David R. Glowacki.

24 records · Page 2Linked to original sources

arXiv2019

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

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 three-dimensional 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 efforts to extend immersive technologies to the molecular sciences, and we introduce 'Narupa', a flexible, open-source, multi-person iMD-VR software framework which enables groups of researchers to simultaneously cohabit real-time 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.

physics.chem-ph↗

arXiv2019

Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality

Whilst the primary bottleneck to a number of computational workflows was not so long ago limited by processing power, the rise of machine learning technologies has resulted in a paradigm shift which places increasing value on issues related to data curation - i.e., data size, quality, bias, format, and coverage. Increasingly, data-related issues are equally as important as the algorithmic methods used to process and learn from the data. Here we introduce an open source GPU-accelerated neural network (NN) framework for learning reactive potential energy surfaces (PESs), and investigate the use of real-time interactive ab initio molecular dynamics in virtual reality (iMD-VR) as a new strategy for rapidly sampling geometries along reaction pathways which can be used to train NNs to learn reactive PESs. Focussing on hydrogen abstraction reactions of CN radical with isopentane, we compare the performance of NNs trained using iMD-VR data versus NNs trained using a more traditional method, namely molecular dynamics (MD) constrained to sample a predefined grid of points along hydrogen abstraction reaction coordinates. Both the NN trained using iMD-VR data and the NN trained using the constrained MD data reproduce important qualitative features of the reactive PESs, such as a low and early barrier to abstraction. Quantitatively, learning is sensitive to the training dataset. Our results show that user-sampled structures obtained with the quantum chemical iMD-VR machinery enable better sampling in the vicinity of the minimum energy path (MEP). As a result, the NN trained on the iMD-VR data does very well predicting energies in the vicinity of the MEP, but less well predicting energies for 'off-path' structures. The NN trained on the constrained MD data does better in predicting energies for 'off-path' structures, given that it included a number of such structures in its training set.

physics.chem-ph↗

arXiv2019

Somatic Practices for Understanding Real, Imagined, and Virtual Realities

In most VR experiences, the visual sense dominates other modes of sensory input, encouraging non-visual senses to respond as if the visual were real. The simulated visual world thus becomes a sort of felt actuality, where the 'actual' physical body and environment can 'drop away', opening up possibilities for designing entirely new kinds of experience. Most VR experiences place visual sensory input (of the simulated environment) in the perceptual foreground, and the physical body in the background. In what follows, we discuss methods for resolving the apparent tension which arises from VR's prioritization of visual perception. We specifically aim to understand how somatic techniques encouraging participants to 'attend to their attention' enable them to access more subtle aspects of sensory phenomena in a VR experience, bound neither by rigid definitions of vision-based virtuality nor body-based corporeality. During a series of workshops, we implemented experimental somatic-dance practices to better understand perceptual and imaginative subtleties that arise for participants whilst they are embedded in a multi-person VR framework. Our preliminary observations suggest that somatic methods can be used to design VR experiences which enable (i) a tactile quality or felt sense of phenomena in the virtual environment (VE), (ii) lingering impacts on participant imagination even after the VR headset is taken off, and (iii) an expansion of imaginative potential.

cs.HC↗

arXiv2018

Sonifying stochastic walks on biomolecular energy landscapes

Translating the complex, multi-dimensional data from simulations of biomolecules to intuitive knowledge is a major challenge in computational chemistry and biology. The so-called "free energy landscape" is amongst the most fundamental concepts used by scientists to understand both static and dynamic properties of biomolecular systems. In this paper we use Markov models to design a strategy for mapping features of this landscape to sonic parameters, for use in conjunction with visual display techniques such as structural animations and free energy diagrams.

cs.HC↗

arXiv2018

Sampling molecular conformations and dynamics in a multi-user virtual reality framework

We describe a framework for interactive molecular dynamics in a multiuser virtual reality environment, combining rigorous cloud-mounted physical atomistic simulation with commodity virtual reality hardware, which we have made accessible to readers (see isci.itch.io/nsb-imd). It allows users to visualize and sample, with atomic-level precision, the structures and dynamics of complex molecular structures 'on the fly', and to interact with other users in the same virtual environment. A series of controlled studies, wherein participants were tasked with a range of molecular manipulation goals (threading methane through a nanotube, changing helical screw-sense, and tying a protein knot), quantitatively demonstrate that users within the interactive VR environment can complete sophisticated molecular modelling tasks more quickly than they can using conventional interfaces, especially for molecular pathways and structural transitions whose conformational choreographies are intrinsically 3d. This framework should accelerate progress in nanoscale molecular engineering areas such as drug development, synthetic biology, and catalyst design. More broadly, our findings highlight VR's potential in scientific domains where 3d dynamics matter, spanning research and education.

physics.chem-ph↗

arXiv2014

A parallel multistate framework for atomistic non-equilibrium reaction dynamics of solutes in strongly interacting organic solvents

We describe a parallel linear-scaling computational framework developed to implement arbitrarily large multi-state empirical valence bond (MS-EVB) calculations within CHARMM. Forces are obtained using the Hellman-Feynmann relationship, giving continuous gradients, and excellent energy conservation. Utilizing multi-dimensional Gaussian coupling elements fit to CCSD(T)-F12 electronic structure theory, we built a 64-state MS-EVB model designed to study the F + CD3CN -> DF + CD2CN reaction in CD3CN solvent. This approach allows us to build a reactive potential energy surface (PES) whose balanced accuracy and efficiency considerably surpass what we could achieve otherwise. We use our PES to run MD simulations, and examine a range of transient observables which follow in the wake of reaction, including transient spectra of the DF vibrational band, time dependent profiles of vibrationally excited DF in CD3CN solvent, and relaxation rates for energy flow from DF into the solvent, all of which agree well with experimental observations. Immediately following deuterium abstraction, the nascent DF is in a non-equilibrium regime in two different respects: (1) it is highly excited, with ~23 kcal mol-1 localized in the stretch; and (2) not yet Hydrogen bonded to the CD3CN solvent, its microsolvation environment is intermediate between the non-interacting gas-phase limit and the solution-phase equilibrium limit. Vibrational relaxation of the nascent DF results in a spectral blue shift, while relaxation of its microsolvation environment results in a red shift. These two competing effects result in a post-reaction relaxation profile distinct from that observed when DF vibration excitation occurs within an equilibrium microsolvation environment. The parallel software framework presented in this paper should be more broadly applicable to a range of complex reactive systems.

physics.chem-ph↗