Publications 2023 | SynTech CDT

SynTech CDT Publications 2023

A Brief Introduction to Chemical Reaction Optimization

CJ. Taylor, A. Pomberger, K. Felton, R. Grainger, M. Barecka, TW. Chamberlain, RA. Bourne, CN. Johnson, A. Lapkin

Chem. Rev. 2023, 123, 6, 3089-3126 (10.1021/acs.chemrev.2c00798)

Quantitative In Silico Prediction of the Rate of Protodeboronation by a Mechanistic Density Functional Theory-Aided Algorithm

D. Wigh, M. Tissot, P. Pasau, J. Goodman, A. Lapkin

Phys. Chem. A, 2023, 127, 11, 2628-2636 (10.1021/acs.jpca.2c08250)

Micro-kinetics analysis based on partial reaction networks to compare catalysts performances for methane dry reforming reaction

S. Shambhawi, JM. Weber, A. Lapkin

CEJ., 2023, 466, 143212 (10.1016/j.cej.2023.143212)

ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction

K. Felton, L. Rasßpe-Lange, J. Rittig, K. Leonhard, A. Mitsos, J. Meyer-Kirschner, C. Knösche, A. Lapkin

ChemRxiv, 2023, 1 (10.26434/chemrxiv-2023-j1z06)

Disulfide re-bridging reagents for single-payload antibody-drug conjugates

T. A. King, S. J. Walsh, M. Kapun, T. Wharton, S. Krajčovičová, M. S. Glossop, D. R. Spring

Chem. Commun., 2023, 59, 9868-9871, Advance Article (10.1039/D3CC02980H)

Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields

L. Schaaf, E. Fako, S. De, A Schafer, G. Csanyi

NPJ Computer Materials 9, 2023, 180 (10.1038/s41524-023-01124-2)

Red-light modulated ortho-chloro azobenzene photoswitch for peptide stapling via aromatic substitution

M. Kapun, F. Javier Perez-Areales, N. Ashman, P. Rowling, T. Schober, E. Fowler, L. Itzhaki, D.R. Spring

RSC Chemical Biology, 2023, (10.1039/D3CB00176H)

wfl Python Toolkit for Creating Machine Learning Interatomic Potentials and Related Atomistic Simulation Workflows

E. Gelžinyte, S. Wengert, T. K. Stenczel, H. H. Heenen, K. Reuter, G. Csányi, N. Bernstein

arXiv, 2023, 2306.11421, (10.48550/arXiv.2306.11421)

Accelerated Chemical Reaction Optimization Using Multi-Task Learning

C.J. Taylor, K. Felton, D. Wigh, M.I. Jeraal, R. Grainger, G. Chessari, C.N. Johnson, A. Lapkin

ACS, 2023, 9, 5, 957–968, (10.1021/acscentsci.3c00050)

10.

Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecule

E. Gelžinyte, M. Öeren, M.D. Segall, G. Csányi

ChemRxiv, 2023, Working Paper V1, (10.26434/chemrxiv-2023-l85nf)

11.

ACEpotentials.jl: A Julia implementation of the atomic cluster expansion

W.C. Witt, C.V.D. Oord, E. Gelžinytė, T. Järvinen, A. Ross, J.P. Darby, C.H. Ho, W.J. Baldwin, M. Sachs, J. Kermode, N. Bernstein, G. Csányi, C. Ortner

J. Chem. Phys. 2023, 159, 164101, (10.1063/5.0158783)

12.

ORDerly: Datasets and benchmarks for chemical reaction data

D. Wigh, J. Arrowsmith, A. Pomberger, K. Felton, A. Lapkin

ChemRxiv, 2023, Working Paper V1, (10.26434/chemrxiv-2023-qkjtb-v2)

13.

An atomic surface site interaction point description of non-covalent interactions†

M.C. Storer, K.J. Zator, D.P. Reynoldsa, C.A. Hunter

Chem. Sci., 2024 Advanced Article, (10.1039/D3SC05690B)