Structure Prediction and Design of Molecular Interfaces from First Principles

Abstract

Molecular interfaces are ubiquitous and perform critical functions in organic electronic and photovoltaic devices. In typical organic solar cells charge separation is achieved at donor-acceptor interfaces. In any organic device, charge transport to external circuits depends on interfaces between active molecular layers and electrode materials. The properties and functionality of these critical interfaces cannot be deduced directly from those of their isolated constituents. Rather, they emerge from quantum mechanical interactions at the atomistic scale. Predicting the properties of molecular interfaces thus requires a fully quantum mechanical first principles approach. The configuration space of molecular interfaces is infinitely vast, owing to the endless possibilities of combining layers of one or more molecular species with different substrates. Layers of the same molecular species may adopt different structures on different substrates and therefore exhibit different electronic and optical properties. Furthermore, epitaxial templating may enable stabilizing meta-stable crystal structures with desirable properties in thin film form. Efficient algorithms may significantly accelerate the discovery and design of molecular interfaces with enhanced properties. The goal of the proposed research is to develop a first-principles framework for (i) predicting the structure of molecular interfaces and (ii) designing molecular interfaces with enhanced properties. To this end, we will integrate first principles quantum mechanical simulations with data driven strategies of genetic algorithms and machine learning.

Document Details

Document Type
DoD Grant Award
Publication Date
Feb 14, 2019
Source ID
W911NF1810148

Entities

People

  • Noa Marom

Organizations

  • Army Contracting Command
  • Massachusetts Institute of Technology
  • United States Army

Tags

Readers

  • Distributed Systems and Data Platform Development
  • Nanocomposite Materials Science
  • Quantum Chemistry

Technology Areas

  • AI & ML
  • Biotechnology
  • Microelectronics
  • Quantum Computing
  • Space