arXiv subjects

David R. Glowacki

Publications and source records attributed to David R. Glowacki.

At least 19 recordsLinked to original sources

arXiv2026

Sensing the properties of virtual objects without physical feedback

People who have interacted with simulated worlds and simulated objects in extended reality (XR) often have a sense that they can 'feel' the objects being simulated despite them not being physical. Our sense of touch is essential for how we 'feel' the physical world, however, there is an open question as to what it means to 'feel' virtual objects when interacting with them in immersive digital environments. In prior research, we have reported that participants often describe a subjective experience of 'feeling' the properties of simulated molecular objects while using interactive molecular dynamics in extended reality (iMD-XR), a field-based interaction paradigm for manipulating real-time simulations of molecular objects without haptic feedback. To better understand these subjective reports of 'feeling', we used a psychophysics approach to quantify the threshold at which participants perceive differences in the rigidity of simulated molecular objects (C$_{60}$ molecules) in iMD-XR. To evaluate this, we carried out experiments to compare the just-noticeable differences (JNDs) in two conditions: (1) via direct interaction with a real-time C$_{60}$ simulation, and (2) via observation-only$\unicode{x2013}$i.e. watching another person interacting with the simulations. Our findings show that direct interaction enabled participants to perceive more subtle rigidity differences of 11.5%, compared to 18.5% for observation-only. Furthermore, participants who undertook interaction first were better able to distinguish rigidity differences in the subsequent observation-only condition, suggesting that interaction trained participants to better perceive differences in molecular properties. These findings demonstrate a novel and flexible approach for sensing the properties of virtual objects in XR, and offer new insights into iMD-XR's potential in molecular research and education.

cs.HC↗

arXiv2026

NanoVer: An open-source framework for interactive molecular dynamics in extended reality (iMD-XR) on commodity hardware

This article outlines 'NanoVer', an open-source software framework which enables groups of people to co-habit the same virtual space and manipulate real-time MD (Molecular Dynamics) simulations of flexible 3D molecular structures with atomic-level precision as if they were tangible objects, an approach that we call 'interactive Molecular Dynamics in eXtended Reality' (iMD-XR). Distinct from our earlier iMD work that relied on tethered PC-VR systems with large graphics cards, NanoVer represents a change in philosophy, emphasizing compatibility with standalone mobile consumer XR hardware and corresponding software APIs. The NanoVer architecture enables multiple XR clients and/or Python clients to simultaneously communicate with a flexible server architecture that can carry out a range of tasks, including for example: recording iMD-XR sessions, static structure visualization, and MD trajectory visualization. NanoVer allows researchers, educators, and students to fluidly move between AR and VR environments, to explore creative new approaches to molecular research and education, including for example: molecular conformational sampling, protein-ligand binding, molecular psychophysics, training AI agents to sample molecular transitions, and a new interface which allows iMD-XR participants to sketch 3D conformational paths which automated agents can then follow. As an immersive platform that offers new ways to understand, engineer, communicate, and interact with dynamical behaviour at the nanoscale, NanoVer invites us to imagine new ways for combining human intelligence (e.g., spatial cognition and design reasoning) with machine intelligence. To expand NanoVer's accessibility, we have published a version to the Meta Horizon Store, for easy download by those with a Meta Quest 3/3S headset, to explore pre-recorded iMD-XR trajectory visualizations and set up their own multi-user system.

physics.chem-ph↗

arXiv2025

AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems

Molecular dynamics (MD) simulations are a crucial computational tool for researchers to understand and engineer molecular structure and function in areas such as drug discovery, protein engineering, and material design. Despite their utility, MD simulations are expensive, owing to the high dimensionality of molecular systems. Interactive molecular dynamics in virtual reality (iMD-VR) has recently emerged as a "human-in-the-loop" strategy for efficiently navigating hyper-dimensional molecular systems. By providing an immersive 3D environment that enables visualization and manipulation of real-time molecular simulations running on high-performance computing architectures, iMD-VR enables researchers to reach out and guide molecular conformational dynamics, in order to efficiently explore complex, high-dimensional molecular systems. Moreover, iMD-VR simulations generate rich datasets that capture human experts' spatial insight regarding molecular structure and function. This paper explores the use of researcher-generated iMD-VR datasets to train AI agents via imitation learning (IL). IL enables agents to mimic complex behaviours from expert demonstrations, circumventing the need for explicit programming or intricate reward design. In this article, we review IL across robotics and Multi-agents systems domains which are comparable to iMD-VR, and discuss how iMD-VR recordings could be used to train IL models to interact with MD simulations. We then illustrate the applications of these ideas through a proof-of-principle study where iMD-VR data was used to train a CNN network on a simple molecular manipulation task; namely, threading a small molecule through a nanotube pore. Finally, we outline future research directions and potential challenges of using AI agents to augment human expertise in navigating vast molecular conformational spaces.

cs.LG↗

arXiv2024

Measuring the limit of perception of bond stiffness of interactive molecules in VR via a gamified psychophysics experiment

Molecular dynamics (MD) simulations provide crucial insight into molecular interactions and biomolecular function. With interactive MD simulations in VR (iMD-VR), chemists can now interact with these molecular simulations in real-time. Our sense of touch is essential for exploring the properties of physical objects, but recreating this sensory experience for virtual objects poses challenges. Furthermore, employing haptics in the context of molecular simulation is especially difficult since \textit{we do not know what molecules actually feel like}. In this paper, we build upon previous work that demonstrated how VR-users can distinguish properties of molecules without haptic feedback. We present the results of a gamified two-alternative forced choice (2AFC) psychophysics user study in which we quantify the threshold at which iMD-VR users can differentiate the stiffness of molecular bonds. Our preliminary analysis suggests that participants can sense differences between buckminsterfullerene molecules with different bond stiffness parameters and that this limit may fall within the chemically relevant range. Our results highlight how iMD-VR may facilitate a more embodied way of exploring complex and dynamic molecular systems, enabling chemists to sense the properties of molecules purely by interacting with them in VR.

cs.HC↗

arXiv2023

Free energy along drug-protein binding pathways interactively sampled in virtual reality

We describe a two-step approach for combining interactive molecular dynamics in virtual reality (iMD-VR) with free energy (FE) calculation to explore the dynamics of biological processes at the molecular level. We refer to this combined approach as iMD-VR-FE. Stage one involves using a state-of-the-art iMD-VR framework to generate a diverse range of protein-ligand unbinding pathways, benefitting from the sophistication of human spatial and chemical intuition. Stage two involves using the iMD-VR-sampled pathways as initial guesses for defining a path-based reaction coordinate from which we can obtain a corresponding free energy profile using FE methods. To investigate the performance of the method, we apply iMD-VR-FE to investigate the unbinding of a benzamidine ligand from a trypsin protein. The binding free energy calculated using iMD-VR-FE is similar for each pathway, indicating internal consistency. Moreover, the resulting free energy profiles can distinguish energetic differences between pathways corresponding to various protein-ligand conformations (e.g., helping to identify pathways that are more favourable) and enable identification of metastable states along the pathways. The two-step iMD-VR-FE approach offers an intuitive way for researchers to test hypotheses for candidate pathways in biomolecular systems, quickly obtaining both qualitative and quantitative insight.

physics.chem-ph↗

arXiv2022

Interactivity: the missing link between virtual reality technology and drug discovery pipelines

The potential of virtual reality (VR) to contribute to drug design and development has been recognised for many years. Hardware and software developments now mean that this potential is beginning to be realised, and VR methods are being actively used in this sphere. A recent advance is to use VR not only to visualise and interact with molecular structures, but also to interact with molecular dynamics simulations of 'on the fly' (interactive molecular dynamics in VR, IMD-VR), which is useful not only for flexible docking but also to examine binding processes and conformational changes. iMD-VR has been shown to be useful for creating complexes of ligands bound to target proteins, e.g., recently applied to peptide inhibitors of the SARS-CoV-2 main protease. In this review, we use the term 'interactive VR' to refer to software where interactivity is an inherent part of the user VR experience e.g., in making structural modifications or interacting with a physically rigorous molecular dynamics (MD) simulation, as opposed to simply using VR controllers to rotate and translate the molecule for enhanced visualisation. Here, we describe these methods and their application to problems relevant to drug discovery, highlighting the possibilities that they offer in this arena. We suggest that the ease of viewing and manipulating molecular structures and dynamics, and the ability to modify structures on the fly (e.g., adding or deleting atoms) makes modern interactive VR a valuable tool to add to the armoury of drug development methods.

q-bio.BM↗

arXiv2021

Dissolving yourself in connection to others: shared experiences of ego attenuation and connectedness during group VR experiences can be comparable to psychedelics

With a growing body of research highlighting the therapeutic potential of experiential phenomenology which diminishes egoic identity and increases one's sense of connectedness, there is significant interest in how to elicit such 'self-transcendent experiences' (STEs) in laboratory contexts. Psychedelic drugs (YDs) have proven particularly effective in this respect, producing subjective phenomenology which reliably elicits intense STEs. With virtual reality (VR) emerging as a powerful tool for constructing new perceptual environments, we describe a VR framework called 'Isness-distributed' (Isness-D) which harnesses the unique affordances of distributed multi-person VR to blur conventional self-other boundaries. Within Isness-D, groups of participants co-habit a shared virtual space, collectively experiencing their bodies as luminous energetic essences with diffuse spatial boundaries. It enables moments of 'energetic coalescence', a new class of embodied phenomenological intersubjective experience where bodies can fluidly merge, enabling participants to have an experience of including multiple others within their self-representation. To evaluate Isness-D, we adopted a citizen science approach, coordinating an international network of Isness-D 'nodes'. We analyzed the results (N = 58) using 4 different self-report scales previously applied to analyze subjective YD phenomenology (the inclusion of community in self scale, ego-dissolution inventory, communitas scale, and the MEQ30 mystical experience questionnaire). Despite the complexities associated with a distributed experiment like this, the Isness-D scores on all 4 scales were statistically indistinguishable from recently published YD studies, demonstrating that distributed VR can be used to design intersubjective STEs where people dissolve their sense of self in the connection to others.

cs.HC↗

arXiv2021

AutoMeKin2021: An open-source program for automated reaction discovery

AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (J. Comput. Chem. 2018, 39, 1922-1930). This release features a number of new capabilities: rare-event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond-order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at: https://rxnkin.usc.es/index.php/AutoMeKin

physics.chem-ph↗

arXiv2021

ChemDyME: Kinetically Steered, Automated Mechanism Generation Through Combined Molecular Dynamics and Master Equation Calculations

In many scientific fields, there is an interest in understanding the way in which complex chemical networks evolve. The chemical networks which researchers focus upon, have become increasingly complex and this has motivated the development of automated methods for exploring chemical reactivity or conformational change in a black box manner, harnessing modern computing resources to automate mechanism discovery. In this work we present a new approach to automated mechanism generation implemented which couples molecular dynamics and statistical rate theory to automatically find kinetically important reactions and then solve the time evolution of the species in the evolving network. Key to this ChemDyME approach is the novel concept of kinetic convergence whereby the search for new reactions is constrained to those species which are kinetically favorable at the conditions of interest. We demonstrate the capability of the new approach for two systems, a well-studied combustion system, and a multiple oxygen addition system relevant to atmospheric aerosol formation.

physics.chem-ph↗

arXiv2021

Nonadiabatic kinetics in the intermediate coupling regime: comparing molecular dynamics to an energy grained master equation

Here we outline and test an extension of the energy grained master equation (EGME) for treating nonadiabatic (NA) hopping between different potential energy surfaces, which enables us to model the competition between stepwise collisional relaxation and kinetic processes which transfer population between different potential energy surfaces of the same spin symmetry. By incorporating Zhu-Nakamura theory into the EGME, we are able to treat nonadiabatic passages beyond the simple Landau-Zener approximation, along with corresponding treatments of zero-point energy and tunnelling probability. To evaluate this NA-EGME approach, we carried out detailed studies of the UV photodynamics of the volatile organic compound C6-hydroperoxyaldehyde (C6-HPALD) using on-the-fly ab initio molecular dynamics and trajectory surface hopping. For this multi-chromophore molecule, we show that the EGME is able to quantitatively capture important aspects of the dynamics, including kinetic timescales, and diabatic trapping. Such an approach provides a promising and efficient strategy for treating the long-time dynamics of photo-excited molecules in regimes which are difficult to capture using atomistic on-the-fly molecular dynamics.

physics.chem-ph↗

arXiv2020

A community-powered search of machine learning strategy space to find NMR property prediction models

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published "in-house" efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties.

physics.comp-ph↗

arXiv2020

Training atomic neural networks using fragment-based data generated in virtual reality

The ability to understand and engineer molecular structures relies on having accurate descriptions of the energy as a function of atomic coordinates. Here we outline a new paradigm for deriving energy functions of hyperdimensional molecular systems, which involves generating data for low-dimensional systems in virtual reality (VR) to then efficiently train atomic neural networks (ANNs). This generates high quality data for specific areas of interest within the hyperdimensional space that characterizes a molecule's potential energy surface (PES). We demonstrate the utility of this approach by gathering data within VR to train ANNs on chemical reactions involving fewer than 8 heavy atoms. This strategy enables us to predict the energies of much higher-dimensional systems, e.g. containing nearly 100 atoms. Training on datasets containing only 15K geometries, this approach generates mean absolute errors around 2 kcal/mol. This represents one of the first times that an ANN-PES for a large reactive radical has been generated using such a small dataset. Our results suggest VR enables the intelligent curation of high-quality data, which accelerates the learning process.

physics.chem-ph↗

arXiv2020

Subtle Sensing: Detecting Differences in the Flexibility of Virtually Simulated Molecular Objects

During VR demos we have performed over last few years, many participants (in the absence of any haptic feedback) have commented on their perceived ability to 'feel' differences between simulated molecular objects. The mechanisms for such 'feeling' are not entirely clear: observing from outside VR, one can see that there is nothing physical for participants to 'feel'. Here we outline exploratory user studies designed to evaluate the extent to which participants can distinguish quantitative differences in the flexibility of VR-simulated molecular objects. The results suggest that an individual's capacity to detect differences in molecular flexibility is enhanced when they can interact with and manipulate the molecules, as opposed to merely observing the same interaction. Building on these results, we intend to carry out further studies investigating humans' ability to sense quantitative properties of VR simulations without haptic technology.

cs.HC↗

arXiv2020

OMG-VR: Open-source Mudra Gloves for Manipulating Molecular Simulations in VR

As VR finds increasing application in scientific research domains like nanotechnology and biochemistry, we are beginning to better understand the domains in which it brings the most benefit, as well as the gestures and form factors that are most useful for specific applications. Here we describe Open-source Mudra Gloves for Virtual Reality (OMG-VR): etextile gloves designed to facilitate research scientists and students carrying out detailed and complex manipulation of simulated 3d molecular objects in VR. The OMG-VR is designed to sense when a user pinches together their thumb and index finger, or thumb and middle finger, forming a "mudra" position. Tests show that they provide good positional tracking of the point at which a pinch takes place, require no calibration, and are sufficiently accurate and robust to enable scientists to accomplish a range of tasks that involve complex spatial manipulation of molecules. The open source design offers a promising alternative to existing controllers and more costly commercial VR data gloves.

cs.HC↗

arXiv2020

Isness: Using Multi-Person VR to Design Peak Mystical-Type Experiences Comparable to Psychedelics

Studies combining psychotherapy with psychedelic drugs (PsiDs) have demonstrated positive outcomes that are often associated with PsiDs' ability to induce 'mystical-type' experiences (MTEs) - i.e., subjective experiences whose characteristics include a sense of connectedness, transcendence, and ineffability. We suggest that both PsiDs and virtual reality can be situated on a broader spectrum of psychedelic technologies. To test this hypothesis, we used concepts, methods, and analysis strategies from PsiD research to design and evaluate 'Isness', a multi-person VR journey where participants experience the collective emergence, fluctuation, and dissipation of their bodies as energetic essences. A study (N=57) analyzing participant responses to a commonly used PsiD experience questionnaire (MEQ30) indicates that Isness participants had MTEs comparable to those reported in double-blind clinical studies after high doses of psilocybin & LSD. Within a supportive setting and conceptual framework, VR phenomenology can create the conditions for MTEs from which participants derive insight and meaning.

cs.HC↗

arXiv2020

Interactive molecular dynamics in virtual reality for accurate flexible protein-ligand docking

Simulating drug binding and unbinding is a challenge, as the rugged energy landscapes that separate bound and unbound states require extensive sampling that consumes significant computational resources. Here, we describe the use of interactive molecular dynamics in virtual reality (iMD-VR) as an accurate low-cost strategy for flexible protein-ligand docking. We outline an experimental protocol which enables expert iMD-VR users to guide ligands into and out of the binding pockets of trypsin, neuraminidase, and HIV-1 protease, and recreate their respective crystallographic protein-ligand binding poses within 5 - 10 minutes. Following a brief training phase, our studies shown that iMD-VR novices were able to generate unbinding and rebinding pathways on similar timescales as iMD-VR experts, with the majority able to recover binding poses within 2.15 Angstrom RMSD of the crystallographic binding pose. These results indicate that iMD-VR affords sufficient control for users to carry out the detailed atomic manipulations required to dock flexible ligands into dynamic enzyme active sites and recover crystallographic poses, offering an interesting new approach for simulating drug docking and generating binding hypotheses.

physics.bio-ph↗

arXiv2019

IMPRESSION -- Prediction of NMR Parameters for 3-dimensional chemical structures using Machine Learning with near quantum chemical accuracy

The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar Information Of Nuclei) machine learning system provides an efficient and accurate route to the prediction of NMR parameters from 3-dimensional chemical structures. Here we demonstrate that machine learning predictions, trained on quantum chemical computed values for NMR parameters, are essentially as accurate but computationally much more efficient (tens of milliseconds per molecule) than quantum chemical calculations (hours/days per molecule). Training the machine learning systems on quantum chemical, rather than experimental, data circumvents the need for existence of large, structurally diverse, error-free experimental databases and makes IMPRESSION applicable to solving 3-dimensional problems such as molecular conformation and isomerism

physics.chem-ph↗

arXiv2019

Enhancing automated reaction discovery with boxed molecular dynamics in energy space

The rare event acceleration method BXDE is interfaced in the present work with the automated reaction discovery method AutoMeKin. To test the efficiency of the combined AutoMeKin-BXDE procedure, the ozonolysis of a-pinene is studied in comparison with standard AutoMeKin. AutoMeKin-BXDE locates intermediates and transition states that are more densely connected with each other and approximately 50 kcal/mol more stable than those found with standard AutoMeKin. Other than the different density of edges between the nodes, both networks are scale-free and display small-world properties, mimicking the network of organic chemistry. Finally, while AutoMeKin-BXDE finds more transition states than those previously reported for O3 + a-pinene, the standard procedure fails to locate some of the previously published reaction pathways using the same simulation time of 2.5 ns. In summary, the mixed procedure is very promising and clearly outperforms the standard simulation algorithms implemented in AutoMeKin. BXDE will be available in the next release of AutoMekin.

physics.chem-ph↗