Optimizing Interacting Potentials to Form Targeted Materials Structures
- Princeton Univ., NJ (United States)
Conventional applications of the principles of statistical mechanics (the "forward" problems), start with particle interaction potentials, and proceed to deduce local structure and macroscopic properties. Other applications (that may be classified as "inverse" problems), begin with targeted configurational information, such as low-order correlation functions that characterize local particle order, and attempt to back out full-system configurations and/or interaction potentials. To supplement these successful experimental and numerical "forward" approaches, we have focused on inverse approaches that make use of analytical and computational tools to optimize interactions for targeted self-assembly of nanosystems. The most original aspect of our work is its inherently inverse approach: instead of predicting structures that result from given interaction potentials among particles, we determine the optimal potential that most robustly stabilizes a given target structure subject to certain constraints. Our inverse approach could revolutionize the manner in which materials are designed and fabricated. There are a number of very tangible properties (e.g. zero thermal expansion behavior), elastic constants, optical properties for photonic applications, and transport properties.
- Research Organization:
- Princeton Univ., NJ (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- DOE Contract Number:
- FG02-04ER46108
- OSTI ID:
- 1222771
- Report Number(s):
- DOE-PRINCETON-46108
- Country of Publication:
- United States
- Language:
- English
Similar Records
A genetic algorithm based inverse band structure method for semiconductor alloys
Numerical homogenization on approach for stokesian suspensions.