Feynman Prize in Nanotechnology

The Feynman Prize in Nanotechnology is an award given by the Foresight Institute for significant advances in nanotechnology. Two prizes are awarded annually, in the categories of experimental and theoretical work. There is also a separate challenge award for making a nanoscale robotic arm and 8-bit adder.

Feynman Prize in Nanotechnology
Awarded forExperimental and theoretical advancements in nanotechnology research
CountryUnited States
Presented byForesight Institute
First awarded1993
Websitewww.foresight.org/prize

Overview

The Feynman Prize consists of annual prizes in experimental and theory categories, as well as a one-time challenge award. They are awarded by the Foresight Institute, a nanotechnology advocacy organization. The prizes are named in honor of physicist Richard Feynman, whose 1959 talk There's Plenty of Room at the Bottom is considered by nanotechnology advocates to have inspired and informed the start of the field of nanotechnology.[1]

The annual Feynman Prize in Nanotechnology is awarded for pioneering work in nanotechnology, towards the goal of constructing atomically precise products through molecular machine systems. Input on prize candidates comes from both Foresight Institute personnel and outside academic and commercial organizations. The awardees are selected mainly by an annually changing body of former winners and other academics.[1] The prize is considered prestigious,[1][2] and authors of one study considered it to be reasonably representative of notable research in the parts of nanotechnology under its scope.[1]

The separate Feynman Grand Prize is a $250,000 challenge award to the first persons to create both a nanoscale robotic arm capable of precise positional control, and a nanoscale 8-bit adder, conforming to given specifications. It is intended to stimulate the field of molecular nanotechnology.[3][4][5]

History

The Feynman Prize was instituted in the context of Foresight Institute co-founder K. Eric Drexler's advocacy of funding for molecular manufacturing.[1] The prize was first given in 1993. Before 1997, one prize was given biennially. From 1997 on, two prizes were given each year in theory and experimental categories.[1] By awarding these prizes early in the history of the field, the prize increased awareness of nanotechnology and influenced its direction.[6]: 60 [7][8]

The Grand Prize was announced in 1995 at the Fourth Foresight Conference on Molecular Nanotechnology and was sponsored by James Von Ehr and Marc Arnold.[9][10] In 2004, X-Prize Foundation founder Peter Diamandis was selected to chair the Feynman Grand Prize committee.[3]

Recipients

Single prize

YearLaureateInstitutionScope of work
1993Charles MusgraveCalifornia Institute of TechnologyMolecular modelling of atomically precise manufacturing[11][12]
1995Nadrian C. SeemanNew York UniversityDNA nanotechnology[8][13][14]

Experimental category

YearLaureateInstitutionScope of work
1997James K. GimzewskiIBM Zurich Research LaboratoryScanning probe microscopy for atomically precise manufacturing[6]: 55, 182 [15]
Reto Schlittler
Christian JoachimCEMES/French National Centre for Scientific Research
1998M. Reza GhadiriScripps Research InstituteMolecular self-assembly[16][17]
1999Phaedon AvourisIBM Watson Research CenterMolecular scale electronics using carbon nanotubes[18][19][20]
2000R. Stanley WilliamsHP LabsSwitches for molecular scale electronics[20][21]
Philip Kuekes
James R. HeathUniversity of California, Los Angeles
2001Charles M. LieberHarvard UniversitySynthesis and characterization of carbon nanotubes[20][22]
2002Chad MirkinNorthwestern UniversitySpherical nucleic acid nanoparticles[6]: 163 [20][23][24]
2003Carlo MontemagnoUniversity of California, Los AngelesIntegration of biological molecular motors with silicon devices[25]
2004Homme HellingaDuke UniversityAtomically precise manufacturing[26]
2005Christian SchafmeisterUniversity of PittsburghSynthesis of designed macromolecules[27][28]
2006Erik WinfreeCalifornia Institute of TechnologyDNA computing using algorithmic self-assembly[6]: 140 [29]
Paul W. K. Rothemund
2007J. Fraser StoddartUniversity of California, Los AngelesSynthesis and assembly of molecular machines[30]
2008James TourRice UniversitySynthesis of nanocars and other molecular machines[31]
2009Yoshiaki SugimotoOsaka UniversityNon-contact atomic force microscopy for manipulation of single atoms[32][33]
Masayuki Abe
Oscar CustanceJapanese National Institute for Materials Science
2010Masakazu AonoMANA Center, Japanese National Institute for Materials ScienceScanning probe microscopy for manipulation of atoms[34]
2011Leonhard GrillFritz Haber Institute of the Max Planck SocietyScanning probe microscopy for characterization and manipulation of molecules[35][36]
2012Gerhard MeyerIBM Zurich Research LaboratoryImaging and manipulation of molecular orbitals using scanning probe microscopy[36][37]
Leo Gross
Jascha Repp
2013Alexander ZettlUniversity of California, BerkeleyNanoscale electromechanical systems[38]
2014Joseph W. LydingUniversity of Illinois at Urbana–ChampaignHydrogen depassivation lithography using scanning tunneling microscopes[39]
2015Michelle Y. SimmonsUniversity of New South WalesFabrication of single-atom transistors[40][41]
2016Franz J. GiessiblUniversity of RegensburgImaging and manipulation of individual atoms using scanning probe microscopy[42]
2017William ShihHarvard UniversityDNA nanotechnology[43]
2018Christopher LutzIBM Almaden Research CenterManipulating atoms and small molecules for data storage and computation[44]
Andreas J. HeinrichCenter for Quantum Nanoscience, Institute for Basic Science
2019Lulu QianCalifornia Institute of TechnologyMolecular robotics, self-assembly of DNA structures, and biochemical circuits[45]
2020Hao YanArizona State UniversityUse of DNA as designer molecular building blocks for programmable molecular self-assembly.[46]
2021Anne-Sophie DuwezUniversity of LiègeDeveloped tools and technologies to interface synthetic functional molecules with AFM to study their operation and her other single-molecule research.[47][48]
2022Sergei V. KalininUniversity of TennesseeApplications of machine learning and artificial intelligence in nanotechnology, atomic fabrication, and materials discovery via scanning transmission electron microscopy, as well as mesoscopic studies of electrochemical, ferroelectric, and transport phenomena via scanning probe microscopy.[49][50]
2023James J. CollinsMassachusetts Institute of TechnologyFor pioneering work on synthetic gene circuits that launched the field of synthetic biology and has enabled the development of programmable biomolecular tools for the life sciences, medicine and nanobiotechnology.[51]

Theory category

YearLaureateInstitutionScope of work
1997Charles BauschlicherNASA Ames Research CenterComputational nanotechnology[15][52]
Stephen Barnard
Creon Levit
Glenn Deardorff
Al Globus
Jie Han
Richard Jaffe
Alessandra Ricca
Marzio Rosi
Deepak Srivastava
H. Thuemmel
1998Ralph C. MerkleZyvexMolecular tools for atomically precise chemical reactions[16][17]
Stephen WalchELORET Corporation/NASA Ames Research Center
1999William A. Goddard IIICalifornia Institute of TechnologyModeling of molecular machines[18]
Tahir Cagin
Yue Qi
2000Uzi LandmanGeorgia Institute of TechnologyComputational materials science for nanostructures[21]
2001Mark A. RatnerNorthwestern UniversityMolecular scale electronics[22]
2002Don BrennerNorth Carolina State UniversityMolecular machines for molecular manufacturing[23][24]
2003Marvin L. CohenUniversity of California, BerkeleyModeling of new materials[25]
Steven G. Louie
2004David BakerUniversity of WashingtonDevelopment of RosettaDesign[26]
Brian KuhlmanUniversity of North Carolina, Chapel Hill
2005Christian JoachimFrench National Centre for Scientific ResearchTheoretical tools and design principles for molecular machines[6]: 56 [27]
2006Erik WinfreeCalifornia Institute of TechnologyDNA computing[29]
Paul W. K. Rothemund
2007David A. LeighUniversity of EdinburghDesign and synthesis of molecular machines[30]
2008George C. SchatzNorthwestern UniversityModeling of dip-pen nanolithography, and of plasmon effects in metallic nanoparticles[31]
2009Robert A. Freitas Jr.Institute for Molecular ManufacturingMechanosynthesis and systems design of molecular machines[32]
2010Gustavo E. ScuseriaRice UniversityTools for modeling of carbon nanostructures[34]
2011Raymond AstumianUniversity of MaineMolecular machines powered by Brownian motion[35][36]
2012David SoloveichikUniversity of California, San FranciscoDNA computing using strand displacement cascades[37]
2013David BeratanDuke UniversityFunctional supramolecular assemblies[38]
2014Amanda BarnardAustralian Commonwealth Scientific and Industrial Research OrganisationCarbon nanostructure structure-function relationships[39][53]
2015Markus J. BuehlerMassachusetts Institute of TechnologyMechanical simulations of materials[40]
2016Bartosz GrzybowskiUlsan National Institute of Science and TechnologyModeling of the outcomes of organic reactions[42]
2017Giovanni ZocchiUniversity of California, Los AngelesStress–strain analysis of soft nanoparticles[43]
2018O. Anatole von LilienfeldUniversity of Basel, now University of ViennaMethods for fast quantum mechanical modelling[44]
2019Giulia GalliUniversity of ChicagoThe development of theoretical and computational methods to predict and design, from first principles, the properties of nanostructured materials.[45]
2020Massimiliano Di VentraUniversity of California, San DiegoQuantum transport in nanoscale and atomic systems; prediction of nanoscale phenomena which were later verified experimentally, studied memory effects in materials and devices.[54]
2021Kendall N. HoukUCLAQuantum mechanical and molecular dynamics simulations which have elucidated structural and dynamical features of synthetic nanomachines.[47][55]
2022James R. ChelikowskyUniversity of TexasPioneered the use of computational approaches to understand and predict the properties of materials at the nanoscale.[49][56]
2023Alexandre TkatchenkoUniversity of LuxembourgFor pioneering the development of methods that seamlessly merge quantum mechanics, statistical mechanics, and machine learning to unravel the intricacies of complex molecules and materials.[57]

See also

References