A Network for Computational Nanotechnology Mark Lundstrom Electrical and Computer Engineering Purdue University Supported by the National Science Foundation, Indiana’s 21st Century Res. and Tech. Fund, and the ARO DURINT program 1.NSF’s Nanoscale Modeling and Simulation Program 2.The nanoHUB 3.The Network for Computational Nanotechnology

Purdue University Nanoscale Modeling and Simulation 1. Nanoengineered materials (Balazas, et al., Pittsburg) 2. Patterned Magnetic Nanostructures (Clemens, et al, Stanford) 3. Nanoscale Film Morphology (Rahman, et al., Kansas State) 4. Nanostructured Membranes (Wagner, et al. Deleware) 5. Biomolecules in Microfluidic Devices (De Pablo,et al. Wisconsin) 6. Quantum Computation (Lloyd, et al., MIT) 7. Molecular Electronics (Lundstrom, et al. Purdue)

Purdue University 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 8  m channel (Courtesy of D. C. Schwartz) 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 Patterned Magnetic Nanostructures KMC Ab initio

Purdue University J. J. de Pablo M.D. Graham University of Wisconsin-Madison Motivation: Emerging nanoscale technologies, such as biodetection /microseparation / DNA sequencing require predictive modeling tools for rational design of single-molecule flows in devices where molecular and device sizes are comparable 1-5 nm 100 nm-1  m  m DNA flowing through 8  m channel (Courtesy of D. C. Schwartz) 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 Biomolecules in Microfluidic Devices Vision: tools and principles for in silico rational design of biomolecular processes

Purdue University Norman Wagner, Stanley Sandler Raul 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. Molecular Transport in Nanostructured Materials Nanoporous Carbon (NPC) for gas separation TubeGen: Online Carbon Nanotube gen. program ab initio quantum mechanical calcs. of guest-host interactions Molecular Dynamics simulations of diffusion in polymers and NPCs

Purdue University Seth Lloyd and David Cory Massachusetts Institute of Technology Goals: Use a quantum information processor (QIP) to investigate nano and sub-nanostructures. Explore propagation of information from the sub-nano to macro scales. Nanoscale Quantum Simulations reverse map Decoherence generates one bit of information Density matrices pseudo pure state decohere bit reverse map Implementation of the quantum baker’s map forward map 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.

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

Purdue University Molecular Nanoelectronics: From Hamiltonians to Circuits Electronic Devices Classical/quantum electrons in an open system far from equilibrium Chemistry quantum mechanical electrons in isolated molecules at 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….

Purdue University VDVD Contact  2 current Device simulation at the nano/molecular scale silicon dioxide Gate drainsource SiO 2 L = 10 nm Xylyl Dithiol S. Datta, et al., Phys. Rev. Lett., 79, 2530, 1997 position ---> energy--->

Purdue University Compact models for circuits and systems EFEF E F - qV DS Gate Drain

Purdue University Computational nanotechnology is different atomic/molecular Gate mesoscale devices circuit models

Purdue University Why compute? to understand to explore to design

Purdue University Challenges in Computational 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

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

Purdue University CNTbands

Purdue University 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”

Purdue University 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

Purdue University The Network for Computational Nanotechnology Mission To address key challenges in nanotechnology by: 1) 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 2) operate an infrastructure that supports these teams and the field of nanotechnology (computational and experimental) more generally.

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

Purdue University 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

Purdue University 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, and students.