Maithreyi Ramakrishnan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Maithreyi Ramakrishnan has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Alexander van Teijlingen
Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow, G1 1XL, UK
2 shared publications - Rein V. Ulijn
Nanoscience Initiative at Advanced Science Research Center (ASRC) of the Graduate Center of the City University of New York New York, NY USA
2 shared publications - Tell Tuttle
Department of Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow, G1 1XL, UK
2 shared publications Asheesh Kumar
1 shared publicationDimple P. Dutta
1 shared publicationMainak Roy
1 shared publication
Latest Publications
- Open Access
- 14 February 2023
- Published by Wiley in Angewandte Chemie
- Vol. 135 (18)
Integrating Computation, Experiment, and Machine Learning in the Design of Peptide‐Based Supramolecular Materials and Systems
Interest in peptide‐based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interactions and inform the design of new supramolecular systems. They are also used to virtually screen and navigate these very large design spaces. Increasingly, the use of artificial intelligence is employed to screen far more candidates than traditional methods. Based on a brief history of computational and experimentally integrated investigations of peptide structures, we explore recent impactful examples of computationally driven investigation into peptide self‐assembly, focusing on recent advances in methodology development. It is clear that the integration between experiment and computation to understand and design new systems is becoming near seamless in this growing field.
- 14 February 2023
- Published by Wiley in Angewandte Chemie
- Vol. 135 (18)
Open AccessIntegrating Computation, Experiment, and Machine Learning in the Design of Peptide‐Based Supramolecular Materials and Systems
Interest in peptide‐based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interactions and inform the design of new supramolecular systems. They are also used to virtually screen and navigate these very large design spaces. Increasingly, the use of artificial intelligence is employed to screen far more candidates than traditional methods. Based on a brief history of computational and experimentally integrated investigations of peptide structures, we explore recent impactful examples of computationally driven investigation into peptide self‐assembly, focusing on recent advances in methodology development. It is clear that the integration between experiment and computation to understand and design new systems is becoming near seamless in this growing field.
- Open Access
- 14 February 2023
- Published by Wiley in Angewandte Chemie International Edition
- Vol. 62 (18) , e202218067
Integrating Computation, Experiment, and Machine Learning in the Design of Peptide‐Based Supramolecular Materials and Systems
Interest in peptide‐based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interactions and inform the design of new supramolecular systems. They are also used to virtually screen and navigate these very large design spaces. Increasingly, the use of artificial intelligence is employed to screen far more candidates than traditional methods. Based on a brief history of computational and experimentally integrated investigations of peptide structures, we explore recent impactful examples of computationally driven investigation into peptide self‐assembly, focusing on recent advances in methodology development. It is clear that the integration between experiment and computation to understand and design new systems is becoming near seamless in this growing field.
- 14 February 2023
- Published by Wiley in Angewandte Chemie International Edition
- Vol. 62 (18) , e202218067
Open AccessIntegrating Computation, Experiment, and Machine Learning in the Design of Peptide‐Based Supramolecular Materials and Systems
Interest in peptide‐based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interactions and inform the design of new supramolecular systems. They are also used to virtually screen and navigate these very large design spaces. Increasingly, the use of artificial intelligence is employed to screen far more candidates than traditional methods. Based on a brief history of computational and experimentally integrated investigations of peptide structures, we explore recent impactful examples of computationally driven investigation into peptide self‐assembly, focusing on recent advances in methodology development. It is clear that the integration between experiment and computation to understand and design new systems is becoming near seamless in this growing field.
- 1 February 2017
- Published by Elsevier in Journal of Photochemistry and Photobiology A: Chemistry
- Vol. 335, 102-111
Effect of transition metal doping on the photocatalytic properties of FeVO4 nanoparticles
- 1 February 2017
- Published by Elsevier in Journal of Photochemistry and Photobiology A: Chemistry
- Vol. 335, 102-111
Effect of transition metal doping on the photocatalytic properties of FeVO4 nanoparticles