High performance computations using dynamical nucleation theory

Chemists continue to explore the use of very large computations to perform simulations that describe the molecular level physics of critical challenges in science. In this paper, we describe the Dynamical Nucleation Theory Monte Carlo (DNTMC) model - a model for determining molecular scale nucleation rate constants - and its parallel capabilities. The potential for bottlenecks and the challenges to running on future petascale or larger resources are delineated. A "master-slave" solution is proposed to scale to the petascale and will be developed in the NWChem software. In addition, mathematical and data analysis challenges are described.

[1]  M. Plesset,et al.  Note on an Approximation Treatment for Many-Electron Systems , 1934 .

[2]  Jarek Nieplocha,et al.  Advances, Applications and Performance of the Global Arrays Shared Memory Programming Toolkit , 2006, Int. J. High Perform. Comput. Appl..

[3]  T. H. Dunning.  Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen , 1989 .

[4]  B. C. Garrett,et al.  Current status of transition-state theory , 1983 .

[5]  T. Dunning,et al.  Electron affinities of the first‐row atoms revisited. Systematic basis sets and wave functions , 1992 .

[6]  Laxmikant V. Kale,et al.  Object-Based Adaptive Load Balancing for MPI Programs∗ , 2000 .

[7]  Theresa L. Windus,et al.  Implementation of dynamical nucleation theory with quantum potentials , 2009, J. Comput. Chem..

[8]  Alan M. Ferrenberg,et al.  New Monte Carlo technique for studying phase transitions. , 1988, Physical review letters.

[9]  James Arthur Kohl,et al.  A Component Architecture for High-Performance Scientific Computing , 2006, Int. J. High Perform. Comput. Appl..

[10]  Theresa L. Windus,et al.  Enabling new capabilities and insights from quantum chemistry by using component architectures , 2006 .

[11]  Jarek Nieplocha,et al.  Component‐based integration of chemistry and optimization software , 2004, Journal of computational chemistry.

[12]  Eugene P. Wigner,et al.  The transition state method , 1938 .

[13]  Mark S. Gordon,et al.  Parallel Implementation of the Electronic Structure Code GAMESS , 1995 .

[14]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[15]  B. C. Garrett,et al.  Variational transition state theory of vapor phase nucleation , 1999 .

[16]  Gregory K. Schenter,et al.  Dynamical Nucleation Theory: A New Molecular Approach to Vapor-Liquid Nucleation , 1999 .

[17]  James C. Keck,et al.  Variational Theory of Chemical Reaction Rates Applied to Three‐Body Recombinations , 1960 .

[18]  M. Fayer,et al.  Dynamics of water confined on a nanometer length scale in reverse micelles: ultrafast infrared vibrational echo spectroscopy. , 2005, Physical review letters.

[19]  Yoshinori Hayakawa,et al.  Theoretical and numerical approach to "magic angle" of stone skipping. , 2005, Physical review letters.

[20]  R. Harrison,et al.  AB Initio Molecular Electronic Structure on Parallel Computers , 1994 .

[21]  Paul Adrien Maurice Dirac,et al.  On the Theory of quantum mechanics , 1926 .

[22]  David E. Bernholdt,et al.  High performance computational chemistry: An overview of NWChem a distributed parallel application , 2000 .

[23]  Theresa L. Windus,et al.  Components for integral evaluation in quantum chemistry , 2008, J. Comput. Chem..

[24]  John H. Marburger,et al.  Our Changing Planet: The U.S. Climate Change Science Program for Fiscal Year 2006 , 2005 .

[25]  James Arthur Kohl,et al.  Component-based software for high-performance scientific computing , 2005 .