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Materials science is an interdisciplinary field that examines the structure-property relationships in matter for their application to many areas of science and engineering. As structure-property relationships are investigated through computer simulations in addition to experimentation, computational tools offer complementary features that can enhance research efforts. While nanomaterials are of interest to scientists and have redeeming value for their potential social impact, this size regime is fraught with many challenges found particularly in experimentation.
Computer simulations allow scientists and engineers to perform specialized tests in a large variety of environments limited only by time and computational resources. Molecular dynamics (MD) simulations allow the appropriate time and length scales to study the phenomena of interest in many nanomaterials. Simulations expand the study of materials by removing the constraints of the physical laboratory, however many computational tools lack accessible, intuitive interfaces for research. Enhancement with the graphical display of models, efficient computational algorithms, and graphical processing unit (GPU) based computing complement current simulation efforts. These new graphics devices combine with central processing units efficiently to allow mathematically intensive calculations to be accomplished by the GPU. The result is an effective acceleration of computation on the order of 10x accompanied by a reduction in power consumption of up to 20x.
The goal of this research project was to develop and implement a novel tool for nanoscience investigation that directly connects an interactive interface to MD simulations, materials science analysis and 3D visualization. This innovative system with unique and powerful analysis capabilities has been used for nanoscale research and education at UC Merced, with direct implications to other related STEM fields such as nanotechnology, physics, biology, and geology, and ultimate benefit to education and society.
The 3D/VR Visualization System was implemented as both a research and teaching instrument which allows creation and manipulation of atomic structures in an interactive 3D virtual reality (VR) environment. The system was created from a set of relatively low-cost and accessible components following the model originally developed by Dr. Oliver Kreylos at UC Davis1.
Below is a photo of the final 3D/VR Visualization System layout, with important components labeled (Figure 1). This system was originally established for education purposes at UC Merced in 2009. The implementation of the original 3D/VR system resulted in peer-reviewed publications2-3. Table 1 below summarizes key characteristics for each element of the 3D/VR Visualization System.

Figure 1. 3D/VR Visualization System and main components (left) in the Davila Research Laboratory at UCM and visualization devices (right). Please click here to view a larger version of this figure.
| Item | Component | Functionality in System |
| A | 3D TV | 3D display of modeled molecular structures and on-screen menus. |
| B | Infrared (IR) tracking cameras4 | IR cameras track positions of the Wiimote and 3D viewing goggles in the user workspace in front of 3D TV, allowing virtual 3D manipulation of displayed structures. |
| C | Tracking PC | Runs IR camera tracking software and transmits Wiimote and 3D goggle positions to modeling computer. |
| D | Wiimote | Used for on-screen management of modeling software and to manipulate structures in 3D virtual environment. |
| E | 3D goggles5 | Synchronized with 3D TV IR signal, allow 3D view of structure. Position tracked by IR cameras for accurate 3D view. |
| F | Modeling PC | Runs NCK/VRUI 3D modeling and display software6, accepts goggle / Wiimote position and control signals to create accurate 3D molecular structure view. |
Table 1. Functionality of main elements of the 3D/VR Visualization System at UCM.
Description of 3D/VR Visualization System and Basic Components:
3D/VR Visualization System Overview — The 3D/VR Visualization System consists of a set of IR cameras and tracking software operating in conjunction with 3D modeling software to allow a user to interactively create 3D molecular structures. The IR cameras and software track the 3D location of a Wiimote and 3D viewing goggles using IR markers, and pass this to the modeling software. The modeling software uses the Wiimote control signals and movement to generate 3D molecular structures viewable using the combination of a 3D-capable large format television with synchronized and tracked 3D goggles. This results in a 3D virtual reality workspace within which the user can dynamically create and manipulate virtual molecular structures which reflect real-world physical behavior based on inter-atomic forces used in the modeling software (Figure 2). Special considerations for setting up this system can be found in supplemental materials.

Figure 2. Investigating silica nanomaterials using the 3D/VR Visualization System. (a) A researcher creates an initial cristobalite model (crystalline) before GPU-based simulations. (b) Upon performing a simulated MD melt-quench procedure on model shown in (a), another researcher obtains a silica glass model (non-crystalline). Please click here to view a larger version of this figure.
3D/VR Visualization System Enhancement — MD Simulation Capability:
Molecular dynamics simulation systems are commonly implemented in a multi-nodal fashion, that is, a large workload is distributed or parallelized among tens to thousands of processors. Recently, additional opportunities for accelerated scientific computing have arisen out of developments in computer graphics processing. These advances include a software interface allowing scientists to take advantage of the highly parallel nature of the processing power intrinsic to graphics chips. With the advent of the Compute Unified Device Architecture or CUDA7, scientists can use GPUs8 to enhance the speed at which problems are solved while reducing the cost of infrastructure. A typical GPU may have the equivalent of hundreds to thousands of cores or “nodes” for processing information, and as these can each be used in parallel, a well-coded solution may provide up to 1,000x throughput acceleration against its multi-core counterpart. Though not every problem is well-suited to this approach, current MD simulations have seen up to 15x throughput performance gains9. Details on the 3D/VR visualization system MD-GPU enhancement can be found in supplemental materials.