Physics-informed neural networks (PINNs) based machine learning is an emerging
framework for solving nonlinear differential equations. However, due to the implicit regularity …

In this work, we propose an efficient nullspace-preserving saddle search (NPSS) method for
a class of phase transitions involving translational invariance, where the critical states are …

Nucleation of various ordered phases in block copolymers is studied by examining the free-
energy landscape within the self-consistent field theory. The minimum energy path (MEP) …

Confined nematic liquid crystals can admit multiple stable equilibria. These equilibrium
states usually exhibit peculiar defect patterns in order to satisfy the topological constraints …

We examine the dynamical evolution of a stable lamellar phase nucleating from a
metastable cylinder phase in a diblock copolymer melt, through large-scale simulations of …

In this paper, we study the constrained shrinking dimer dynamics (CSDD) which leads to
numerical procedures for locating saddle points (transition states) associated with an energy …

The fundamental understanding of the effects of rare earth (RE) on the phase transformation
of Fe-based alloys is essential in the development of advanced RE-containing high strength …

The string method is a useful numerical technique for resolving minimum energy paths in
rare-event barrier-crossing problems. However, when applied to systems with relatively …

The transition between two basic structures, a disk and an enclosed vesicle, of a finite
membrane is studied by examining the minimum energy path (MEP) connecting these two …

A cubic to tetragonal transformation leads to simultaneous nucleation and growth of several
orientation variants. The size and morphology of a critical nucleus, comprising of one or …