Professor of Chemistry
We use chemical informatics, computational chemistry, machine learning and AI to investigate molecules and to discover more about their structure, reactivity, analytical data and properties.
(Full list of publications)
Professor Goodman discusses his research
Publications
Quantitative In Silico Prediction of the Rate of Protodeboronation by a Mechanistic Density Functional Theory-Aided Algorithm.
The Journal of Physical Chemistry A
(2023)
127
2628
(doi: 10.1021/acs.jpca.2c08250)
Interpreting Vibrational Circular Dichroism Spectra: the Cai•Factor for Absolute Configuration with Confidence
(2023)
(doi: 10.21203/rs.3.rs-2567578/v1)
Reaction dynamics as the missing puzzle piece: the origin of selectivity in oxazaborolidinium ion-catalysed reactions
Chemical Science
(2023)
14
12355
(doi: 10.1039/d3sc03009a)
In silico prediction of protodeboronation by a mechanistic DFT-aided algorithm
Aiche Annual Meeting Conference Proceedings
(2023)
2023-November
MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations
Chemical science
(2022)
13
7204
(doi: 10.1039/d1sc06324c)
Analysing a billion reactions with the RInChI
Pure and Applied Chemistry
(2022)
94
643
(doi: 10.1515/pac-2021-2008)
Towards quantifying the uncertainty in in silico predictions using Bayesian learning
Computational Toxicology
(2022)
23
100228
(doi: 10.1016/j.comtox.2022.100228)
RSC CICAG Open Chemical Science Meeting: integrating chemical data from two symposia and a series of workshops
Pure and Applied Chemistry
(2022)
94
677
(doi: 10.1515/pac-2021-1003)
The DP5 probability, quantification and visualisation of structural uncertainty in single molecules.
Chemical Science
(2022)
13
3507
(doi: 10.1039/d1sc04406k)
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