Multi-fidelity prediction of molecular optical peaks with deep learning

Abstract

A multi-fidelity deep learning approach that utilizes data from both experiments and physics-based calculations predicts molecular absorption peaks with higher accuracy and generalizability than existing methods.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2022
Source ID
10.1039/d1sc05677h

Entities

People

  • Kevin P. Greenman
  • Rafael Gómez-Bombarelli
  • William H. Green

Organizations

  • Defense Advanced Research Projects Agency
  • Division of Graduate Education
  • Massachusetts Institute of Technology

Tags

Fields of Study

  • Physics

Readers

  • Computational Modeling and Simulation
  • Materials Science and Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks