Integrated Tools for Computational Chemical Dynamics


·        Overview

·        Background

·        Features of the Program





The goal of this research program is to develop more powerful simulation methods and incorporate them into a user-friendly high-throughput integrated software suite for chemical dynamics. It is supported by the Office of Naval Research (ONR) for 2005-2009.

This is a joint grant between the Chemistry Department and the Supercomputing Institute of the University of Minnesota (PI: Donald. G. Truhlar) and Pacific Northwest National Laboratory (co-PI: Bruce C. Garrett). 

·        The University of Minnesota faculty investigators: Donald. G. Truhlar, J. Ilja Siepmann, Christopher Cramer, Jiali Gao, and Darrin York

·        The Pacific Northwest National Laboratory investigators: Bruce C. Garrett, Theresa Windus, Michel Dupuis, Shawn Kathmann, Greg Schenter, and Marat Valiev

This research program consists of three subprojects:

·        Computational Electrochemistry

·        Heterogeneous Catalysis

·        Computational Photochemistry

Currently, the project manager is Alek Marenich.




Photochemical creation of excited states offers a means to control chemical transformations because different wavelengths of light create different vibronic states, thereby directing chemical reactions along different pathways. It is crucial to understand how energy deposited into the system is used; this is particularly complicated in condensed-phase systems where many channels lead to dissipation of excess energy. Similar opportunities and challenges present themselves in the areas of electrochemistry and catalysis.

A deeper insight for these critical problems relies on our accurate knowledge about chemical equilibria and kinetics in the complex systems. Thanks to the recent advances in computer power and algorithms, we can now calculate accurately a large variety of chemical properties for many systems. Nonetheless, applications to complex chemical systems, such as reactive processes in the condensed phase, remain problematic. The problem is due to the lack of a seamless integration of computational methods that allow modern quantum electronic structure calculations to be performed with state-of-the-art methods for electronic structure, chemical thermodynamics, and reactive dynamics. The problems is often exacerbated by invalidated methods, non-modular and non-portable codes, and inadequate documentation that drastically limit software reliability, throughput, and ease of use.

This research program takes advantage of new computer hardware and algorithms and develops computational chemistry software for scientific discovery by modeling and simulation. This includes tools for calculating reaction rate and transport properties, and for investigating energetic materials, catalysis, and ultra fast dynamics. A notable area of emphasis is multi-scale modeling. The research includes methods and algorithm development as well as software integration. An important program goal is to develop practical solutions and standards for interfacing computer programs developed in separate laboratories. We will extend software capability, hardware compatibility and parallel efficiency, automation and documentation. Computational chemistry codes are becoming increasingly complex, not only in the types of computation available, but also in the software architectures used in code development. Developing appropriate interfaces will enable chemists to leverage current research in mathematics and computer science without duplicating the efforts of experts in the field.


Features of the Program


Combining Quantum Mechanics (QM) and Molecular Mechanics (MM)

Conventional force fields are typically not appropriate for treating bond making and breaking processes, which involve electronic structure changes and must be treated by quantum mechanics. A promising strategy to circumvent the computational restrictions of large-scale quantum electronic structure calculations is to apply combined QM and MM methods. In these methods, reactive molecules or reactive site are treated explicitly by QM, while the surrounding subsystem is modeled by MM. A key feature of the hybrid QM/MM approach is that it combines the accuracy of quantum mechanics for the region of primary chemical interest and the computational efficiency of molecular mechanics for the large surrounding subsystem. The QM/MM approach is a practical strategy in treating the complexity in the condensed phase.


Combining Highly Accurate Potentials with Robust and Reliable Dynamical Algorithms

Prediction of highly accurate potential energy surfaces is the first step in understanding reactions and mechanisms. Robust and reliable dynamical algorithms are the second step. We will integrate accurate and scalable quantum chemistry methods with state-of-the-art dynamics codes to study the dynamics of chemical reactions. An integrated kinetics and electronic structure software will be especially convenient for multi-step kinetics. 


Component-Based Software Engineering

To date, typical scientific software designs make rigid assumptions regarding programming languages and data structures, frustrating software interoperability and scientific collaboration. However, component-based software engineering is an emerging approach to managing the increasing complexity of scientific software. Component technology facilitates code interoperability and reuse, and proves extremely beneficial in facilitating the interfacing of the several complex simulation modules. We will adopt this methodology embodied in the Common Component Architecture (CCA) Forum, and we will take advantage of existing software packages as a starting point, especially, but not exclusively, NWChem and software developed at the University of Minnesota. NWChem is a new generation of high-performance molecular modeling software for parallel computing systems, and it is described at http://www.emsl.pnl.gov/docs/nwchem/nwchem.html. Software developed at the University of Minnesota is very diverse, and much of it is described at http://comp.chem.umn.edu.


Updated: March 23, 2008