GLOBAL OPTIMIZATION IN COMPUTATIONAL CHEMISTRY USING INTERVAL ARITHMETIC

GLOBAL OPTIMIZATION PLAYS A KEY ROLE IN MOLECULAR COMPUTATIONS IN COMPUTATIONAL CHEMISTRY. DUE TO THE LARGE SIZE OF EVEN MODEST MOLECULES, THESE METHODS MUST CONTEND WITH COUNTLESS LOCAL MINIMA. CLEVER ALGORITHMS HAVE BEEN DEVELOPED THAT SAMPLE PARAMETER SPACE TO REDUCE TRAPPING IN LOCAL MINIMA, BUT THESE FAIL BY PROVIDING STATISTICAL RATHERTHAN GUARANTEED SOLUTIONS. THERE IS A CLASS OF ALGORITHMS THAT FOR CERTAIN FUNCTIONS GUARANTEE THAT A GLOBAL MINIMUM HAS BEEN FOUND. THESE METHODS ACHIEVE THIS REMARKABLE RESULT USING INTERVAL ARITHMETIC. INTERVAL GLOBAL OPTIMIZATION HAS BEEN KNOWN FORSOME TIME, BUT HAS NEVER BEEN APPLIED TO SYSTEMS AS LARGE ASTHOSE ENCOUNTERED IN COMPUTATIONAL CHEMISTRY. THE PROPOSED RESEARCH WILL IDENTIFY HOW TO MODIFY THE FUNCTIONS ENCOUNTERED IN COMPUTATIONAL CHEMISTRY SUCH THAT INTERVAL GLOBAL ANALYSIS APPLIES. WE WILL ALSO EXPLORE THE PARALLEL PROGRAMMING TECHNIQUES NECESSARY TO SCALE THE CALCULATIONS UP TO THE HUGE SIZES DEMANDED BY MOLECULAR MECHANICS CALCULATIONS.