Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes

• URL: http://arxiv.org/abs/2008.03226v3
• Date: Sun, 7 Aug 2022 17:44:25 GMT
• Title: Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes
• Authors: Ryan-Rhys Griffiths, Jake L. Greenfield, Aditya R. Thawani, Arian R. Jamasb, Henry B. Moss, Anthony Bourached, Penelope Jones, William McCorkindale, Alexander A. Aldrick, Matthew J. Fuchter Alpha A. Lee
• Abstract summary: Photoswitchable molecules display two or more isomeric forms that may be accessed using light. We present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules.
• Score: 51.17758371472664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states whilst overall red-shifting the absorption bands serves to limit material damage due to UV-exposure and increases penetration depth in photopharmacological applications. Engineering these properties into a system through synthetic design however, remains a challenge. Here, we present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning with Gaussian processes. In the prediction of electronic transition wavelengths, we demonstrate that a multioutput Gaussian process (MOGP) trained using labels from four photoswitch transition wavelengths yields the strongest predictive performance relative to single-task models as well as operationally outperforming time-dependent density functional theory (TD-DFT) in terms of the wall-clock time for prediction. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules. Through this screen, we identified several motifs that displayed separated electronic absorption bands of their isomers, exhibited red-shifted absorptions, and are suited for information transfer and photopharmacological applications. Our curated dataset, code, as well as all models are made available at https://github.com/Ryan-Rhys/The-Photoswitch-Dataset

Related papers

• Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks [0.0]
  We present an image segmentation neural network that automatically identifies and measures melt track dimensions from a cross-section image. We use a U-Net architecture to train on a data set of 62 pre-labelled images obtained from different labs, machines, and materials coupled with image augmentation.
  arXiv  Detail & Related papers  (2024-09-26T22:44:00Z)
• Data-Efficient Molecular Generation with Hierarchical Textual Inversion [48.816943690420224]
  We introduce Hierarchical textual Inversion for Molecular generation (HI-Mol), a novel data-efficient molecular generation method. HI-Mol is inspired by the importance of hierarchical information, e.g., both coarse- and fine-grained features, in understanding the molecule distribution. Compared to the conventional textual inversion method in the image domain using a single-level token embedding, our multi-level token embeddings allow the model to effectively learn the underlying low-shot molecule distribution.
  arXiv  Detail & Related papers  (2024-05-05T08:35:23Z)
• A multidimensional approach to quantum state tomography of photoelectron wavepackets [0.0]
  We propose a new and more efficient protocol that is able to reconstruct the continuous variable density matrix of a photoelectron in a single time delay scan. It is based on measuring the coherences of a photoelectron absorption of an XUV pulse using a broadband infrared (IR) probe that is scanned in time and a narrowband IR reference that is temporally fixed to the XUV pulse.
  arXiv  Detail & Related papers  (2024-05-02T03:15:02Z)
• Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders [0.0]
  Variational autoencoders (VAEs) are emerging as a powerful paradigm for the unsupervised data analysis. This article summarizes recent developments in VAEs, covering the basic principles and intuition behind the VAEs.
  arXiv  Detail & Related papers  (2023-03-30T03:16:27Z)
• Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential [0.0]
  A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives.
  arXiv  Detail & Related papers  (2022-07-23T20:07:04Z)
• Ultra-long photonic quantum walks via spin-orbit metasurfaces [52.77024349608834]
  We report ultra-long photonic quantum walks across several hundred optical modes, obtained by propagating a light beam through very few closely-stacked liquid-crystal metasurfaces. With this setup we engineer quantum walks up to 320 discrete steps, far beyond state-of-the-art experiments.
  arXiv  Detail & Related papers  (2022-03-28T19:37:08Z)
• Scaling and Acceleration of Three-dimensional Structure Determination for Single-Particle Imaging Experiments with SpiniFEL [0.0]
  We present SpiniFEL, an application used for structure determination of proteins from single-particle imaging (SPI) experiments. SpiniFEL is being developed to run on supercomputers in near real-time while an experiment is taking place, so that the feedback about the data can guide the data collection strategy.
  arXiv  Detail & Related papers  (2021-09-11T18:49:54Z)
• Near-ideal spontaneous photon sources in silicon quantum photonics [55.41644538483948]
  Integrated photonics is a robust platform for quantum information processing. Sources of single photons that are highly indistinguishable and pure, that are either near-deterministic or heralded with high efficiency, have been elusive. Here, we demonstrate on-chip photon sources that simultaneously meet each of these requirements.
  arXiv  Detail & Related papers  (2020-05-19T16:46:44Z)
• Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
  We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output. We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
  arXiv  Detail & Related papers  (2020-03-15T23:07:56Z)
• Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE) [50.65891535040752]
  We propose a diffusion encoding scheme, called Slice-Interleaved Diffusion. SIDE, that interleaves each diffusion-weighted (DW) image volume with slices encoded with different diffusion gradients. We also present a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data.
  arXiv  Detail & Related papers  (2020-02-25T14:48:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.