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490 results on '"Jelfs, Kim E."'

1. Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations

Author

Judge, Edmund, Azzouzi, Mohammed, Mroz, Austin M., Chanona, Antonio del Rio, and Jelfs, Kim E.

Subjects
Computer Science - Machine Learning, Condensed Matter - Materials Science, Physics - Chemical Physics
Abstract

Multi fidelity Bayesian optimization (MFBO) leverages experimental and or computational data of varying quality and resource cost to optimize towards desired maxima cost effectively. This approach is particularly attractive for chemical discovery due to MFBO's ability to integrate diverse data sources. Here, we investigate the application of MFBO to accelerate the identification of promising molecules or materials. We specifically analyze the conditions under which lower fidelity data can enhance performance compared to single-fidelity problem formulations. We address two key challenges, selecting the optimal acquisition function, understanding the impact of cost, and data fidelity correlation. We then discuss how to assess the effectiveness of MFBO for chemical discovery.

Published
2024

2. PolyCL: Contrastive Learning for Polymer Representation Learning via Explicit and Implicit Augmentations

Author

Zhou, Jiajun, Yang, Yijie, Mroz, Austin M., and Jelfs, Kim E.

Subjects
Computer Science - Machine Learning
Abstract

Polymers play a crucial role in a wide array of applications due to their diverse and tunable properties. Establishing the relationship between polymer representations and their properties is crucial to the computational design and screening of potential polymers via machine learning. The quality of the representation significantly influences the effectiveness of these computational methods. Here, we present a self-supervised contrastive learning paradigm, PolyCL, for learning high-quality polymer representation without the need for labels. Our model combines explicit and implicit augmentation strategies for improved learning performance. The results demonstrate that our model achieves either better, or highly competitive, performances on transfer learning tasks as a feature extractor without an overcomplicated training strategy or hyperparameter optimisation. Further enhancing the efficacy of our model, we conducted extensive analyses on various augmentation combinations used in contrastive learning. This led to identifying the most effective combination to maximise PolyCL's performance.

Published
2024

8. Promoting transparency and reproducibility in enhanced molecular simulations

Author

Bonomi, Massimiliano, Bussi, Giovanni, Camilloni, Carlo, Tribello, Gareth A, Banas, Pavel, Barducci, Alessandro, Bernetti, Mattia, Bolhuis, Peter G, Bottaro, Sandro, Branduardi, Davide, Capelli, Riccardo, Carloni, Paolo, Ceriotti, Michele, Cesari, Andrea, Chen, Haochuan, Chen, Wei, Colizzi, Francesco, De, Sandip, De La Pierre, Marco, Donadio, Davide, Drobot, Viktor, Ensing, Bernd, Ferguson, Andrew L, Filizola, Marta, Fraser, James S, Fu, Haohao, Gasparotto, Piero, Gervasio, Francesco Luigi, Giberti, Federico, Gil-Ley, Alejandro, Giorgino, Toni, Heller, Gabriella T, Hocky, Glen M, Iannuzzi, Marcella, Invernizzi, Michele, Jelfs, Kim E, Jussupow, Alexander, Kirilin, Evgeny, Laio, Alessandro, Limongelli, Vittorio, Lindorff-Larsen, Kresten, Lohr, Thomas, Marinelli, Fabrizio, Martin-Samos, Layla, Masetti, Matteo, Meyer, Ralf, Michaelides, Angelos, Molteni, Carla, Morishita, Tetsuya, Nava, Marco, Paissoni, Cristina, Papaleo, Elena, Parrinello, Michele, Pfaendtner, Jim, Piaggi, Pablo, Piccini, GiovanniMaria, Pietropaolo, Adriana, Pietrucci, Fabio, Pipolo, Silvio, Provasi, Davide, Quigley, David, Raiteri, Paolo, Raniolo, Stefano, Rydzewski, Jakub, Salvalaglio, Matteo, Sosso, Gabriele Cesare, Spiwok, Vojtech, Sponer, Jiri, Swenson, David WH, Tiwary, Pratyush, Valsson, Omar, Vendruscolo, Michele, Voth, Gregory A, and White, Andrew

Subjects
Biological Sciences, Clinical Research, Humans, Models, Molecular, Molecular Conformation, Molecular Dynamics Simulation, Reproducibility of Results, Software, PLUMED consortium, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

The PLUMED consortium unifies developers and contributors to PLUMED, an open-source library for enhanced-sampling, free-energy calculations and the analysis of molecular dynamics simulations. Here, we outline our efforts to promote transparency and reproducibility by disseminating protocols for enhanced-sampling molecular simulations.

Published
2019

24. Diastereoselective Self‐Assembly of Low‐Symmetry PdnL2n Nanocages through Coordination‐Sphere Engineering.

Author

Molinska, Paulina, Tarzia, Andrew, Male, Louise, Jelfs, Kim E., and Lewis, James E. M.

Subjects
ENGINEERING, METAL ions, ISOMERS, DIASTEREOISOMERS, POLYMERSOMES
Abstract

Metal‐organic cages (MOCs) are popular host architectures assembled from ligands and metal ions/nodes. Assembling structurally complex, low‐symmetry MOCs with anisotropic cavities can be limited by the formation of statistical isomer libraries. We set out to investigate the use of primary coordination‐sphere engineering (CSE) to bias isomer selectivity within homo‐ and heteroleptic PdnL2n cages. Unexpected differences in selectivities between alternative donor groups led us to recognise the significant impact of the second coordination sphere on isomer stabilities. From this, molecular‐level insight into the origins of selectivity between cis and trans diastereoisomers was gained, highlighting the importance of both host–guest and host‐solvent interactions, in addition to ligand design. This detailed understanding allows precision engineering of low‐symmetry MOC assemblies without wholesale redesign of the ligand framework, and fundamentally provides a theoretical scaffold for the development of stimuli‐responsive, shape‐shifting MOCs. [ABSTRACT FROM AUTHOR]

Published
2023
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25. Tetramine Aspect Ratio and Flexibility Determine Framework Symmetry for Zn8 L6 Self-Assembled Structures

Author

Davies, Jack A, Tarzia, Andrew, Ronson, Tanya K, Auras, Florian, Jelfs, Kim E, Nitschke, Jonathan R, Davies, Jack A [0000-0003-4625-7701], Tarzia, Andrew [0000-0001-8797-8666], Ronson, Tanya K [0000-0002-6917-3685], Auras, Florian [0000-0003-1709-4384], Jelfs, Kim E [0000-0001-7683-7630], Nitschke, Jonathan R [0000-0002-4060-5122], and Apollo - University of Cambridge Repository

Subjects
Self-Assembly, Stereochemistry, Metal-Organic Cages, Supramolecular Chemistry
Abstract

We derive design principles for the assembly of rectangular tetramines into Zn8 L6 pseudo-cubic coordination cages. Because of the rectangular, as opposed to square, geometry of the ligand panels, and the possibility of either Δ or Λ handedness of each metal center at the eight corners of the pseudo-cube, many different cage diastereomers are possible. Each of the six tetra-aniline subcomponents investigated in this work assembled with zinc(II) and 2-formylpyridine in acetonitrile into a single Zn8 L6 pseudo-cube diastereomer, however. Each product corresponded to one of four diastereomeric configurations, with T, Th , S6 or D3 symmetry. The preferred diastereomer for a given tetra-aniline subcomponent was shown to be dependent on its aspect ratio and conformational flexibility. Analysis of computationally modeled individual faces or whole pseudo-cubes provided insight as to why the observed diastereomers were favored.

Published
2023
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26. Chemical design by artificial intelligence.

Author

Ess, Daniel H., Jelfs, Kim E., and Kulik, Heather J.

Subjects
*ELECTRON configuration, *ARTIFICIAL intelligence, *PESTICIDE residues in food, *SOLAR thermal energy, *MACHINE learning
Abstract

For machine learning models to be predictive, especially in the low data regime, accurate feature selection is key, and an understanding of the most essential features also informs why models work the way they do. It is almost certainly true that as developments continue at ever-increasing speeds over the next few years, the face of chemical physics will continue to transform as machine learning and artificial approaches mature into useful tools alongside more traditional modeling to enable chemical design by artificial intelligence. Empirical principles and structure-property relations derived from chemical intuition have for centuries driven the design and synthesis of materials and molecules. [Extracted from the article]

Published
2022
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27. Thin Film Composite Membranes with Regulated Crossover and Water Migration for Long‐Life Aqueous Redox Flow Batteries

Author

Tan, Rui, primary, Wang, Anqi, additional, Ye, Chunchun, additional, Li, Jiaxi, additional, Liu, Dezhi, additional, Darwich, Barbara Primera, additional, Petit, Luke, additional, Fan, Zhiyu, additional, Wong, Toby, additional, Alvarez‐Fernandez, Alberto, additional, Furedi, Mate, additional, Guldin, Stefan, additional, Breakwell, Charlotte E., additional, Klusener, Peter A. A., additional, Kucernak, Anthony R., additional, Jelfs, Kim E., additional, McKeown, Neil B., additional, and Song, Qilei, additional

Published
2023
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28. Machine learning for materials science: Community requirements for building a UK capacity

Author

Butler, Keith T., Elena, Alin-Marin, Jelfs, Kim E., and Maurer, Reinhard

Abstract

We convened a one-day workshop to canvas a wide range of practitioners from materials’ science who have been early adopters of the use of machine learning (full list in Appendix A). The cohort consisted of individuals from academia, industry and the public sector, working in experimental and computational materials science, and spanning career stages. We set out to discuss the challenges faced in applying ML for materials science under five broad categories – Education/Training, Infrastructure, Ethics, Open Science and FAIR data, Community building and events. The structure of the workshop (discussed in Methods) allowed us to identify some clear requirements as well as draw out and explore tensions between requirements of different sectors of the materials science community.

Published
2023
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29. Modeling the Effect of Defects and Disorder in Amorphous Metal-Organic Frameworks

Author

Bechis, Irene, Sapnik, Adam F, Tarzia, Andrew, Wolpert, Emma H, Addicoat, Matthew A, Keen, David A, Bennett, Thomas D, Jelfs, Kim E, Bechis, Irene [0000-0001-7322-2446], Sapnik, Adam F [0000-0001-6200-4208], Tarzia, Andrew [0000-0001-8797-8666], Wolpert, Emma H [0000-0002-7975-7916], Addicoat, Matthew A [0000-0002-5406-7927], Keen, David A [0000-0003-0376-2767], Bennett, Thomas D [0000-0003-3717-3119], Jelfs, Kim E [0000-0001-7683-7630], and Apollo - University of Cambridge Repository

Subjects
3403 Macromolecular and Materials Chemistry, 34 Chemical Sciences
Abstract

Funder: FP7 Ideas: European Research Council; doi: 10.13039/100011199; Grant(s): 758370, Amorphous metal-organic frameworks (aMOFs) are a class of disordered framework materials with a defined local order given by the connectivity between inorganic nodes and organic linkers, but absent long-range order. The rational development of function for aMOFs is hindered by our limited understanding of the underlying structure-property relationships in these systems, a consequence of the absence of long-range order, which makes experimental characterization particularly challenging. Here, we use a versatile modeling approach to generate in silico structural models for an aMOF based on Fe trimers and 1,3,5-benzenetricarboxylate (BTC) linkers, Fe-BTC. We build a phase space for this material that includes nine amorphous phases with different degrees of defects and local order. These models are analyzed through a combination of structural analysis, pore analysis, and pair distribution functions. Therefore, we are able to systematically explore the effects of the variation of each of these features, both in isolation and combined, for a disordered MOF system, something that would not be possible through experiment alone. We find that the degree of local order has a greater impact on structure and properties than the degree of defects. The approach presented here is versatile and allows for the study of different structural features and MOF chemistries, enabling the derivation of design rules for the rational development of aMOFs.

Published
2022

30. stk: An extendable Python framework for automated molecular and supramolecular structure assembly and discovery.

Author

Turcani, Lukas, Tarzia, Andrew, Szczypiński, Filip T., and Jelfs, Kim E.

Subjects
MOLECULAR structure, PYTHON programming language, EVOLUTIONARY algorithms, STRUCTURAL models, STRUCTURAL frames
Abstract

Computational software workflows are emerging as all-in-one solutions to speed up the discovery of new materials. Many computational approaches require the generation of realistic structural models for property prediction and candidate screening. However, molecular and supramolecular materials represent classes of materials with many potential applications for which there is no go-to database of existing structures or general protocol for generating structures. Here, we report a new version of the supramolecular toolkit, stk, an open-source, extendable, and modular Python framework for general structure generation of (supra)molecular structures. Our construction approach works on arbitrary building blocks and topologies and minimizes the input required from the user, making stk user-friendly and applicable to many material classes. This version of stk includes metal-containing structures and rotaxanes as well as general implementation and interface improvements. Additionally, this version includes built-in tools for exploring chemical space with an evolutionary algorithm and tools for database generation and visualization. The latest version of stk is freely available at github.com/lukasturcani/stk. [ABSTRACT FROM AUTHOR]

Published
2021
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31. Multivariate analysis of disorder in metal-organic frameworks

Author

Sapnik, Adam F, Bechis, Irene, Bumstead, Alice M, Johnson, Timothy, Chater, Philip A, Keen, David A, Jelfs, Kim E, Bennett, Thomas, Sapnik, Adam F [0000-0001-6200-4208], Bechis, Irene [0000-0001-7322-2446], Bumstead, Alice M [0000-0001-9269-1731], Johnson, Timothy [0000-0003-4651-7374], Chater, Philip A [0000-0002-5513-9400], Keen, David A [0000-0003-0376-2767], Jelfs, Kim E [0000-0001-7683-7630], Bennett, Thomas [0000-0003-3717-3119], and Apollo - University of Cambridge Repository

Abstract

The rational design of disordered frameworks is an appealing route to target functional materials. However, intentional realisation of such materials relies on our ability to readily characterise and quantify structural disorder. Here, we use multivariate analysis of pair distribution functions to fingerprint and quantify the disorder within a series of compositionally identical metal-organic frameworks, possessing different crystalline, disordered, and amorphous structures. We find this approach can provide powerful insight into the kinetics and mechanism of structural collapse that links these materials. Our methodology is also extended to a very different system, namely the melting of a zeolitic imidazolate framework, to demonstrate the potential generality of this approach across many areas of disordered structural chemistry., JM11106 EPSRC iCASE Funding

Published
2022
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33. Ion‐Selective Microporous Polymer Membranes with Hydrogen‐Bond and Salt‐Bridge Networks for Aqueous Organic Redox Flow Batteries

Author

Wang, Anqi, primary, Tan, Rui, additional, Liu, Dezhi, additional, Lu, Jiaxin, additional, Wei, Xiaochu, additional, Alvarez‐Fernandez, Alberto, additional, Ye, Chunchun, additional, Breakwell, Charlotte, additional, Guldin, Stefan, additional, Kucernak, Anthony R., additional, Jelfs, Kim E., additional, Brandon, Nigel P., additional, McKeown, Neil B., additional, and Song, Qilei, additional

Published
2023
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42. Author Correction: Hydrophilic microporous membranes for selective ion separation and flow-battery energy storage

Author

Tan, Rui, Wang, Anqi, Malpass-Evans, Richard, Williams, Rhodri, Zhao, Evan Wenbo, Liu, Tao, Ye, Chunchun, Zhou, Xiaoqun, Darwich, Barbara Primera, Fan, Zhiyu, Turcani, Lukas, Jackson, Edward, Chen, Linjiang, Chong, Samantha Y., Li, Tao, Jelfs, Kim E., Cooper, Andrew I., Brandon, Nigel P., Grey, Clare P., McKeown, Neil B., and Song, Qilei

Published
2020
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47. Solution-Processable Redox-Active Polymers of Intrinsic Microporosity for Electrochemical Energy Storage

Author

Wang, Anqi, primary, Tan, Rui, additional, Breakwell, Charlotte, additional, Wei, Xiaochu, additional, Fan, Zhiyu, additional, Ye, Chunchun, additional, Malpass-Evans, Richard, additional, Liu, Tao, additional, Zwijnenburg, Martijn A., additional, Jelfs, Kim E., additional, McKeown, Neil B., additional, Chen, Jun, additional, and Song, Qilei, additional

Published
2022
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48. Long‐Life Aqueous Organic Redox Flow Batteries Enabled by Amidoxime‐Functionalized Ion‐Selective Polymer Membranes

Author

Ye, Chunchun, primary, Tan, Rui, additional, Wang, Anqi, additional, Chen, Jie, additional, Comesaña Gándara, Bibiana, additional, Breakwell, Charlotte, additional, Alvarez‐Fernandez, Alberto, additional, Fan, Zhiyu, additional, Weng, Jiaqi, additional, Bezzu, C. Grazia, additional, Guldin, Stefan, additional, Brandon, Nigel P., additional, Kucernak, Anthony R., additional, Jelfs, Kim E., additional, McKeown, Neil B., additional, and Song, Qilei, additional

Published
2022
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