SUBJECT: Ph.D. Proposal Presentation
BY: Lijuan He
TIME: Tuesday, November 26, 2013, 10:00 a.m.
PLACE: Love Building, 210
TITLE: Multiple Phase Transition Paths and Saddle Points Search in Computer-Aided Nano-Design
COMMITTEE: Dr. Yan Wang, Chair (ME)
Dr. Graeme Henkelman (CHBC, University of Texas at Austin)
Dr. Seung Soon Jang (MSE)
Dr. David L. McDowell (ME)
Dr. Ting Zhu (ME)


In engineering domains, the functions of phase-change materials (PCMs) have been widely used such as in information storage (e.g. hard-disk, CD-ROM, memory) and in energy storage (e.g. battery, shape memory alloy). One of the important issues to design PCMs is to realize the desirable phase transition processes, in which atomistic simulation can be used for the prediction of materials properties. The accuracy of the prediction is largely dependent on searching the true value of the transition rate, which is determined by the minimum energy barrier between stable states, i.e. the saddle point on a potential energy surface (PES). Although a number of methods that search for saddle points on a PES have been developed, they intend to locate only one saddle point with the maximum energy along the transition path at a time. Thus they are unable to provide us an overview of the landscape of the PES. In addition, they do not take uncertainties associated with the fitted PES into consideration. The accuracy of the estimated activation energy value is unknown, which thus affects the accuracy of simulation prediction. To overcome the two major limitations in existing methods, we will develop three new saddle point search methods to provide a global view of energy landscape efficiently and robust estimation of activation energy. First, a concurrent search algorithm for multiple phase transition pathways will be developed. The algorithm is able to search multiple local minima and saddle points simultaneously without prior knowledge of initial and final stable configurations. A new representation of transition paths based on parametric Bézier curves is proposed. Second, a curve swarm search algorithm is proposed to exhaustively locate the local minima and saddle points within a region concurrently. The algorithm is based on the flocking of multiple curves. Third, a verifiable saddle-point search method is proposed to incorporate the PES model error on the fly during the searching process, which is to improve the robustness of activation energy estimation.