Publications

What is a Publication?
69 Publications visible to you, out of a total of 69

Abstract

Not specified

Author: Ganna Gryn’ova

Date Published: 11th Oct 2023

Publication Type: Journal

Abstract

Not specified

Authors: Christopher Ehlert, Anna Piras, Ganna Gryn’ova

Date Published: 3rd Oct 2023

Publication Type: Journal

Abstract (Expand)

Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules.

Authors: Rostislav Fedorov, Ganna Gryn’ova

Date Published: 8th Aug 2023

Publication Type: Journal

Abstract

Not specified

Editor:

Date Published: 1st Aug 2023

Publication Type: Journal

Abstract (Expand)

Chemical (molecular, quantum) machine learning relies on representing molecules in unique and informative ways. Here, we present the matrix of orthogonalized atomic orbital coefficients (MAOC) as a quantum-inspired molecular and atomic representation containing both structural (composition and geometry) and electronic (charge and spin multiplicity) information. MAOC is based on a cost-effective localization scheme that represents localized orbitals via a predefined set of atomic orbitals. The latter can be constructed from such small atom-centered basis sets as pcseg-0 and STO-3G in conjunction with guess (non-optimized) electronic configuration of the molecule. Importantly, MAOC is suitable for representing monatomic, molecular, and periodic systems and can distinguish compounds with identical compositions and geometries but distinct charges and spin multiplicities. Using principal component analysis, we constructed a more compact but equally powerful version of MAOC—PCX-MAOC. To test the performance of full and reduced MAOC and several other representations (CM, SOAP, SLATM, and SPAHM), we used a kernel ridge regression machine learning model to predict frontier molecular orbital energy levels and ground state single-point energies for chemically diverse neutral and charged, closed- and open-shell molecules from an extended QM7b dataset, as well as two new datasets, N-HPC-1 (N-heteropolycycles) and REDOX (nitroxyl and phenoxyl radicals, carbonyl, and cyano compounds). MAOC affords accuracy that is either similar or superior to other representations for a range of chemical properties and systems.

Authors: Stiv Llenga, Ganna Gryn’ova

Date Published: 7th Jun 2023

Publication Type: Journal

Abstract (Expand)

Metal-organic frameworks (MOF) and covalent organic frameworks (COFs) are promising nanocarriers for targeted drug delivery. Noncovalent interactions between frameworks and drugs play a fundamental role in the therapeutic uptake and release of the latter. However, the scope of framework functionalizations and deliverable drugs remains underexplored. Using a multilevel approach combining molecular docking and density functional theory, we show for a range of drugs and frameworks that experimentally reported release metrics are in good agreement with the in silico computed host–guest interaction energies. Functional groups within the framework significantly impact the strength of these host–guest interactions, while a given framework can serve as an efficient delivery agent for drugs beyond the prototypical few. Our findings identify the interaction energy as a reliable and relatively easy to compute descriptor of organic framework materials for drug delivery, able to facilitate their high-throughput screening and targeted design towards extended-release times.

Authors: Michelle Ernst, Ganna Gryn'ova

Date Published: 26th May 2023

Publication Type: Journal

Abstract (Expand)

Collagen is a force-bearing, hierarchical structural protein important to all connective tissue. In tendon collagen, high load even below macroscopic failure level creates mechanoradicals by homolytic bond scission, similar to polymers. The location and type of initial rupture sites critically decide on both the mechanical and chemical impact of these micro-ruptures on the tissue, but are yet to be explored. We here use scale-bridging simulations supported by gel electrophoresis and mass spectrometry to determine breakage points in collagen. We find collagen crosslinks, as opposed to the backbone, to harbor the weakest bonds, with one particular bond in trivalent crosslinks as the most dominant rupture site. We identify this bond as sacrificial, rupturing prior to other bonds while maintaining the material’s integrity. Also, collagen’s weak bonds funnel ruptures such that the potentially harmful mechanoradicals are readily stabilized. Our results suggest this unique failure mode of collagen to be tailored towards combatting an early onset of macroscopic failure and material ageing.

Authors: Benedikt Rennekamp, Christoph Karfusehr, Markus Kurth, Aysecan Ünal, Kai Riedmiller, Ganna Gryn’ova, David M. Hudson, Frauke Gräter

Date Published: 12th Apr 2023

Publication Type: Journal

Powered by
(v.1.15.2)
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH