Oct 042019
 

Noah is researching development of better descriptors of molecules for use in machine learning to prediction of crystal properties. Despite being stuck in the basement, Noah’s favourite building on the Chemistry estate is the CRL. His favourite functional is B3LYP and when not in the lab he plays hockey and 5-a-side football.

Oct 042019
 

Aditya is developing machine learning methods for predicting the classification of various crystallographic properties. He mostly works with Python 3 and associated ML and DL libraries.

In his spare time he enjoys baking and hiking. His go to space group is Fmmm and his favourite intermolecular interaction is pi-pi stacking – classic.

Oct 032019
 

George is investigating the extent to which macroscopic material properties (e.g., habit) can be predicted from information about the constituent molecules of a material. The association is expected to be weak but significant and could be of use for in-silico screening of molecules for particular properties.

Oct 052017
 

George is following up his Part II year in the group with a DPhil project in collaboration with ANSTO neutron scattering facility in Sydney. He is spending most of 2019 at ANSTO collecting data on crystalline materials that need neutron diffraction to exctract crucial infomation about their structure.

In his spare time George may be found running.

Publications

A hexagonal planar transition-metal complex

Jul 062017
 

CrystEngComm, 2017, 19, 5336 – 5340 [ doi:10.1039/C7CE00587C ]

A data-driven approach to predicting co-crystal formation reduces the number of experiments required to successfully produce new co-crystals. A machine learning algorithm trained on an in-house set of co-crystallization experiments results in a 2.6-fold enrichment of successful co-crystal formation in a ranked list of co-formers, using an unseen set of paracetamol test experiments.

Jun 202017
 

CrystEngComm, 2017,19, 3737-3745 [ doi:10.1039/C7CE00738H ]

We present here the crystallisation outcomes for 319 publicly available compounds in up to 18 different solvents spread over 5710 individual single solvent evaporation trials. The recorded data is part of a much larger, corresponding in-house database and includes both positive as well as negative crystallisation outcomes. Such data can be used for statistical analyses of solvent performances, machine learning approaches or investigation of the crystallisation behaviour in structurally similar compound classes.

The presented data suggests that crystallisation behaviour in different solvents is not correlated with chemical similarity among clusters of highly similar compounds. Further, our machine learning models can be used to guide the solvent choice when crystallising a compound. In a retrospective evaluation, these models proved potent to reduce the workload to a third of our initial protocol, while still guaranteeing crystallisation success rates greater than 92%.

Publisher’s copy

Nov 242016
 

J. Chem. Inf. Model., 2016, 56 (12), 2347–2352 doi: 10.1021/acs.jcim.6b00565

A new molecular descriptor, nConf20, based on chemical connectivity, is presented which captures the accessible conformational space of a molecule. Currently the best available 2-dimensional descriptors for quantifying the flexibility of a particular molecule are the rotatable bond count (RBC) and the Kier flexibility index. We present a descriptor which captures this information by sampling the conformational space of a molecule using the RDKit conformer generator. Flexibility has previously been identified as a key feature in determining whether a molecule is likely to crystallize or not. For this application, nConf20 significantly outperforms previously reported single-variable classifiers and also assists rule-based analysis of black-box machine learning classification algorithms.

Publisher’s Copy