A Network for Computational Nanotechnology

1. A Network for Computational Nanotechnology Mark Lundstrom Electrical and Computer Engineering Purdue University NSF’s Nanoscale Modeling and Simulation Program The nanoHUB The Network for Computational Nanotechnology Supported by the National Science Foundation, Indiana’s 21st Century Res. and Tech. Fund, and the ARO DURINT program

2. Nanoscale Modeling and Simulation Nanoengineered materials (Balazas, et al., Pittsburg) Patterned Magnetic Nanostructures (Clemens, et al, Stanford) Nanoscale Film Morphology (Rahman, et al., Kansas State) Nanostructured Membranes (Wagner, et al. Deleware) Biomolecules in Microfluidic Devices (De Pablo,et al. Wisconsin) Quantum Computation (Lloyd, et al., MIT) Molecular Electronics (Lundstrom, et al. Purdue)

3. Patterned Magnetic Nanostructures B. Clemens, K. Cho, D. Chrzan, H. Gao, W. Nix Stanford University and U.C. Berkeley Goals: Develop a predictive nanostructure patterning method using multiscale modeling (quantum, atomistic and continuum models) and apply to magnetic nanostructures as a prototype system with critical experimental validation DNA flowing through 8mm channel (Courtesy of D. C. Schwartz) KMC • Accomplishments:• Ab initio study of metal surface kinetics as a function of surface strains • • Strain-dependant kinetic Monte Carlo simulation of nanostructure patterned growth • Identification of micro-structure patterning as nanostructure control technology Ab initio

4. 1-10 mm 100 nm-1 mm 1-5 nm Biomolecules in Microfluidic Devices J. J. de Pablo M.D. Graham University of Wisconsin-Madison DNA flowing through 8mm channel (Courtesy of D. C. Schwartz) Motivation: Emerging nanoscale technologies, such as biodetection /microseparation / DNA sequencing require predictive modeling tools for rational design of single-moleculeflows in devices where molecular and device sizes are comparable Accomplishments:• first predictive model of flowing DNA solutions in a micron-scale channel • first computations of diffusion and flow behavior in the channel Ongoing work: • transport of DNA through nanopores • experimental validation of model • application to single-molecule sequencing • flow-enhanced, directed ligations Vision: tools and principles for in silico rational design of biomolecular processes

5. Molecular Transport in Nanostructured Materials Norman Wagner, Stanley SandlerRaul Lobo,Douglas Doren University of Delaware Henry Foley (PSU) Goal: Develop a predictive, coherent theoretical description of configurational diffusion from first principles. A novel, hierarchical approach will connect ab initio quantum mechanical calculations to mesoscopic diffusivities and thermodynamic solubilities. Applications include gas separation in nanoporous carbons and permeation through polymers. ab initio quantum mechanical calcs. of guest-host interactions Nanoporous Carbon (NPC) for gas separation Molecular Dynamics simulations of diffusion in polymers and NPCs TubeGen: Online Carbon Nanotube gen. program

6. Nanoscale Quantum Simulations Seth Lloyd and David Cory Massachusetts Institute of Technology • Experimental Methods: • NMR is used as a ‘Quantum Analog • Computer’ to simulate complex quantum • systems in large Hilbert spaces. • Both chaotic and regular maps can be implemented in a spin system. • Goals: • Use a quantum information processor (QIP) • to investigate nano and sub-nanostructures. • Explore propagation of information from the • sub-nano to macro scales. Implementation of the quantum baker’s map Density matrices pseudo pure state reverse map decohere bit forward map reverse map Decoherence generates one bit of information

7. Molecular Nanoelectronics:From Hamiltonians to Circuits Mark Lundstrom and Supriyo Datta Purdue University Mark Ratner (Northwestern) and Mark Reed (Yale) SAMFET pseudo pure state Schön, et al., Nature,413,713,2001 L MOSFET CNTFET Bachtold, et al., Science, Nov. 2001

8. Molecular Nanoelectronics:From Hamiltonians to Circuits Chemistry quantum mechanical electrons in isolated molecules at equilibrium Electronic Devices Classical/quantum electrons in an open system far from equilibrium quantum mechanical electron transport in molecular scale devices under bias Nonequilibrium Green’s function(NEGF) approach with an atomic level basis Then on to circuits and systems….

9. Device simulation at thenano/molecular scale Gate silicon dioxide source drain L = 10 nm SiO2 silicon dioxide Gate Contact m2 VD current Xylyl Dithiol energy---> position ---> S. Datta, et al., Phys. Rev. Lett., 79, 2530, 1997

10. Compact models for circuits and systems Gate EF Drain EF - qVDS

11. atomic/molecular Gate Gate circuit models mesoscale devices Computational nanotechnologyis different

12. Why compute? • to understand • to explore • to design

13. Challenges inComputational Nanotechnology • bridging length and time scales • producing and conveying understanding • maintaining close ties with experimentalists • computational demands • solving problems quickly • collaborating and interdisciplinary research • providing users access to simulation tools • education and support

14. nanohub.purdue.edu 100 nodes (200 cpu’s) 1.2 GHz / 1GB RAM www.nanohub.purdue.edu web enabling -network operating system -logical user accounts -virtual file system -resource management system PUNCH middleware Software applications Research codes workstations servers Linux clusters resource management

15. CNTbands

16. The nanoHUB What can you do? • simulate 10-nm scale MOSFETs with nanoMOS • simulate conduction in molecules with Hückel-IV • simulate carbon nanotube transistors with CNT_IV • read “Resistance of a Molecule” and work exercises with Toy_Molecule • Take a 2-day short course: “Electronic Device Simulation at the Nano/Molecular Scale”

17. The nanoHUB Some statistics: PUNCH:~ 2500 users in 35 countries >7M hits / almost 400,000 simulations nanoHUB: 74 users in 22 countries >2000 simulations >150 source downloads

18. The Network for Computational Nanotechnology Mission To address key challenges in nanotechnology by: supporting interdisciplinary research teams focused on three themes that begin at the molecular level and end at the system level. - nanoelectronics - nanoelectro-mechanics - nano/bio operate an infrastructure that supports these teams and the field of nanotechnology (computational and experimental) more generally.

19. Theme projects The Network for Computational Nanotechnology important problems that develop infrastructure and curriculum Supports multi-scale multi-disciplinary research Guide infrastructure development Supporting infrastructure and leadership open source software visualization workshops conferences visitors students nanoHUB Partners in computer science high-performance computing education

20. The Network for Computational Nanotechnology Purdue University: Computing Research Institute Information Technology at Purdue The Computational Electronics Group Partners: University of Illinois, Northwestern University Stanford, Florida NASA Ames and Jet Propulsion Lab Funding: National Science Foundation, ARO DURINT, Indiana 21st Century Fund, Purdue University

21. Conclusions • Computational nanotechnology can plan a key role in realizing the promise of nanotechnology • Rapid progress is occurring (real challenges exist) • A Network for Computational Nanotechnology is being established to support computation and the broader nanotechnology community of researchers, educators, experimentalists, theorists, andstudents.