GA Optoelectronics

Design solution for novel molecular materials in optoelectronic applications based on genetic algorithm

GA Optoelectronics

Overview

GA Optoelectronics evolves novel molecular analogs with desired properties using a genetic algorithm, generating structurally-new candidates from your seed compounds. By pairing this evolutionary search with quantum mechanics (QM) or ML-based property evaluation, it efficiently narrows a vast design space to the most promising candidates – accelerating experimental development, elucidating structure-property relationships, and informing your future synthetic targets.

Key Capabilities

Simultaneously target several optoelectronic properties in one run, each with its own target and weight

Score molecules with rigorous DFT calculations or swap in custom and pre-trained ML models to optimize properties for which DFT is slow or inaccurate (e.g. solubility and PLQY)

Drive structural novelty using advanced crossover (bond-based recombination) and mutations (elemental, isoelectronic, and fragment-library swaps), while strictly enforcing physical constraints such as atom count and molecular weight

Use the interactive viewer and pre-generation output files to monitor the population converge toward your design target in real time

Broad applications across materials science research areas

Get more from your ideas by harnessing the power of large-scale chemical exploration and accurate in silico molecular prediction.

Polymeric Materials
Pharmaceutical Formulations & Delivery
Energy Capture & Storage
Organic Electronics
Consumer Packaged Goods
Catalysis & Reactivity

Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Materials Science Documentation

GA Optoelectronics

A design solution for novel molecular materials in optoelectronic applications based on a generative algorithm.

Materials Science Tutorial

Genetic Optimization

Generate new structures for which a chosen set of optoelectronic properties is optimized by mutating the structures with a genetic algorithm.

Materials Science Tutorial

Optoelectronics Active Learning

Learn to predict optoelectronic properties using active learning models for a series of iridium complexes.

Related Products

Learn more about the related computational technologies available to progress your research projects.

MS Maestro

Complete modeling environment for your materials discovery

MS Informatics

Automated machine learning tools for materials science applications

DeepAutoQSAR

Automated, scalable solution for the training and application of predictive machine learning models

Jaguar

Quantum mechanics solution for rapid and accurate prediction of molecular structures and properties

Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Materials Science Publication

n-Type naphthalimide-indole derivative for electronic applications

Materials Science Publication

Design of organic electronic materials with a goal-directed generative model powered by deep neural networks and high-throughput molecular simulations

Materials Science Publication

Achieving High Efficiency and Pure Blue Color in Hyperfluorescence Organic Light Emitting Diodes using Organo-Boron Based Emitters

Materials Science Publication

Rapid Multiscale Computational Screening for OLED Host Materials

Materials Science Publication

Atomic-scale Simulation for the Analysis, Optimization and Accelerated Development of Organic Optoelectronic Materials

Materials Science Publication

Virtual Screening for OLED Materials

Training & Resources

Online certification courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Tutorials

Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.