article

Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ) on Tetrathiafulvalene (TTF)

Saeed Moayedpour, Imanuel Bier, Wen Wen, Derek Dardzinski, Olexandr Isayev, Noa Marom

J. Phys. Chem. C Vol. 127 (21) pp. 10398–10410 2023

Highlight

Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ., materials science

Keywords

Cite This Paper

@article{Moayedpour2023,
  author = {Moayedpour, Saeed and Bier, Imanuel and Wen, Wen and Dardzinski, Derek and Isayev, Olexandr and Marom, Noa},
  title = {Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ) on Tetrathiafulvalene (TTF)},
  year = {2023},
  journal = {J. Phys. Chem. C},
  volume = {127},
  number = {21},
  pages = {10398--10410},
  doi = {10.1021/acs.jpcc.3c02384},
  keywords = {structure prediction, organic interfaces},
  highlight = {Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ., materials science}
}

Related Publications

2024
cited85

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Zhang S., Makoś M. Z., Jadrich R. B., Kraka E., Barros K., Nebgen B. T., Tretiak S., Isayev O., Lubbers N., Messerly R. A., Smith J. S.

Nature Chemistry, 16, 727–734 (2024)

Ml Potentials
Experiment Automation

Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery.

DOI
2023
cited201

Generative Models as an Emerging Paradigm in the Chemical Sciences

Anstine D. M., Isayev O.

J. Am. Chem. Soc., 145, 8736–8750 (2023)

Generative Models as an Emerging Paradigm in the Chemical Sciences.

DOI
2023
cited143

Machine Learning Interatomic Potentials and Long-Range Physics

Anstine D. M., Isayev O.

J. Phys. Chem. A, 127, 2417–2431 (2023)

Ml Potentials

Machine Learning Interatomic Potentials and Long-Range Physics.

DOI
2022
cited74

Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds

Korshunova M., Huang N., Capuzzi S., Radchenko D. S., Savych O., Moroz Y. S., Wells C. I., Willson T. M., Tropsha A., Isayev O.

Communications Chemistry, 5 (2022)

Generative Ai
Drug Discovery
Experiment Automation

AbstractDeep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties.

DOI
2022
cited203

The transformational role of GPU computing and deep learning in drug discovery

Pandey M., Fernandez M., Gentile F., Isayev O., Tropsha A., Stern A. C., Cherkasov A.

Nature Machine Intelligence, 4, 211–221 (2022)

The transformational role of GPU computing and deep learning in drug discovery.

DOI