Projects
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| Reference Number | EP/Z002982/1 | |
| Title | ANDANTE: Accelerated nonadiabatic dynamics in photochromic molecular crystals | |
| Status | Funded | |
| Energy Categories | Other Cross-Cutting Technologies or Research 5%; Not Energy Related 95%; |
|
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Chemistry) 50%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 50%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Dr R Crespo-Otero Chemistry University College London |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 May 2025 | |
| End Date | 30 April 2027 | |
| Duration | 24 months | |
| Total Grant Value | £192,297 | |
| Industrial Sectors | ||
| Region | London | |
| Programme | UKRI MSCA | |
| Investigators | Principal Investigator | Dr R Crespo-Otero , Chemistry, University College London (100.000%) |
| Web Site | ||
| Objectives | ||
| Abstract | Light is a versatile stimulus, offering multiple adjustable parameters that allow for precise manipulation without physical contact or as an energy source. Recently, there has been a growing interest in light-controlled photochromic materials (PCMs). These materials have garnered attention due to their significance in fundamental research and diverse applications, including optical switches, optical data storage devices, energy-efficient coatings, energy storage, and eyewear. Photochromism, defined as the reversible transformation of a chemical species between two different isomers with distinct absorption spectra, plays a pivotal role in PCMs. This transformation is initiated by exposure to light and can occur in both forward and reverse directions. In this context, excited state modelling is essential to understand mechanisms in PCMs, aiding the design and optimization of new materials. However, computational limitations, particularly the high computational costs associated with non-adiabatic dynamics (NAMD) in crystalline environments, present significant challenges in studying these reactions. An alternative approach to address these computational challenges is to employ data-driven models alongside traditional quantum chemistry (QC) calculations. Machine learning (ML) models, trained using quantum chemical data, have shown great promise in predicting ground state energies and forces with remarkable precision. This data-driven approach has the potential to substantially accelerate simulations. This research project aims to integrate Graph Neural Networks (GNNs) with existing QC codes to enhance and expedite NAMD simulations within solid environments, extending the simulation timescales. Our research focuses around investigating the cyclization reaction of photochromic diarylethene derivatives. This will serve as a crucial case study to evaluate the efficacy of the suggested approaches in understanding the behaviour of known materials and aiding in the discovery | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 03/07/24 | |
