Mission Statement
Note: the ParamChem project as outlined below has ended, and the direction in which it will evolve has been realigned to the new funding situation. For our current research efforts and goals, see the News & Events page. To access the transformative technologies that were developed as part of the project, e.g. the CGenFF program, the lsfitpar parameter fitting software etc., please visit our technology page. All text below this point pertains to the original project that is now defunct, and only serves historical purposes.
  • Development of a comprehensive cyber environment that automates the parametrization of molecular force fields and provides researchers with the reference test data enabling them to focus on developing novel hamiltonians.
  • Project Summary

  • Cyber environments will be created to automate the process of parametrization for classical molecular mechanics (MM) and semi-empirical (SE) Hamiltonians and allow for wide dessimination of the developed parameters. Project goals include an extensible cyber environment for
    1. rapid and systematic parametrization of novel hamiltonians
    2. systematic extension of currently available models, with the resulting paramter sets from both (i) and (ii) to be made available via the cyber environment
    The integrated environment will include a database of experimental and quantum mechanical reference data to be used in parametrization process along with computational resources for data acquisition, automatization of QM reference data generation and automatization of parameter optimization processes. GridChem Computational Chemistry Grid will serve as the computational cyber infrastructure for Quantum Chemical and Molecular Mechanics services and will include workflows from which specific parametrization schemes will be created and executed. Workflow management tools specific to generating QM reference data, monitoring parameter optimization and analysis will be implemented and will include interfaces allowing for expert intervention in the parametrization process.

    Many existing popular MM and SE Hamiltonians will be integrated from which a wide range of parameters encompassing biological, organic and inorganic species will be accessible for direct use or further optimization. The infrastructure will be extensible in terms of data sources and energy functions allowing for its applicability in any parametrization scheme.
  • Intellectual Merit

  • The development of the scientific gateways in the recent years has provided access to cyber infrastructures through web portals and desktop applications. Combining such science gateways with data archives would be a powerful combination, providing a significant infrastructure for addressing simulation needs in molecular sciences.

    The proposed infrastructure will provide reference data organizers and generators as well as workflows for the automation of the parametrization. It will help the field by making parameter optimization and testing significantly less cumbersome, thereby leading to improved Hamiltonians, including a proposed novel hybrid MM/SE Hamiltonian. Such capabilities will allow for high accuracy molecular simulations to understand the static and dynamic behaviour of novel chemical systems including their behaviour under different environments.

    A major bottleneck for performing reliable simulations for novel systems is the availability, accuracy and validation of consistent parameter sets. The proposed extensible architecture will be especially useful to add new reference data types from which improved models will be obtained. It will lead to greater accuracy in the predictions of molecular properties from molecular simulations. The proposed workflow management tools will reduce the data and computational organization burden on the scientist so he/she can focus on the scientific phenomenology associated with developing novel Hamiltonians and/or extending available Hamiltonians to novel systems rather than logistics. The workflow will enable generation of better models to advance our understanding of the physical world.
  • Broader Impact

  • More accurate descriptions of the static and dynamic properties of a wide range of material, pharmacological and biological systems using theoretical methods will be attained by simplifying the task of parameter optimization. This will allow for the generation of high quality parameters for a wide variety of molecular systems. Improvements in the accuracy of modelling as well as the range of accessible chemical systems will benefit such fields as nanotechnology, medicine and biology, among others. In addition, analytical models used in engineering fields such as structural mechanics and fluid dynamics will become accessible to molecular level treatments allowing for the rational design of the better world.