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Computational Molecular Materials Discovery

Total Cost €


EC-Contrib. €






Project "CoMMaD" data sheet

The following table provides information about the project.


Organization address
city: LONDON
postcode: SW7 2AZ

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 1˙499˙390 €
 EC max contribution 1˙499˙390 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-04-01   to  2023-03-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

The objective of the project is to develop a computational approach to accelerate the discovery of molecular materials. These materials will include porous molecules, small organic molecules and macromolecular polymers, which have application as a result of either their porosity or optoelectronic properties. The applications that will be targeted include in molecular separations, sensing, (photo)catalysis and photovoltaics. To achieve my aims, I will screen libraries of building blocks through a combination of techniques including evolutionary algorithms and machine learning. Through the application of cheminformatics algorithms, I will target the most promising libraries, assess synthetic diversity and accessibility and analyse structure-property relationships. I will develop software that will predict the (macro)molecular structures and properties; the molecular property screening calculations will include void characterisation, binding energies, diffusion barriers, local assembly, charge transport and energy level assessment. A consideration of synthetic accessibility at every stage will be central to my approach, which will ensure the realisation of our predicted targets. I have several synthetic collaborators who can provide pathways to synthetic realisation. Improved materials in this field have the potential to either reduce our energy needs or provide renewable energy, helping the EU meet the targets of the 2030 Energy Strategy.


year authors and title journal last update
List of publications.
2018 Marcin Miklitz, Kim E. Jelfs
pywindow : Automated Structural Analysis of Molecular Pores
published pages: 2387-2391, ISSN: 1549-9596, DOI: 10.1021/acs.jcim.8b00490
Journal of Chemical Information and Modeling 58/12 2019-11-11
2019 Francesco Salerno, Beth Rice, Julia A. Schmidt, Matthew J. Fuchter, Jenny Nelson, Kim E. Jelfs
The influence of nitrogen position on charge carrier mobility in enantiopure aza[6]helicene crystals
published pages: 5059-5067, ISSN: 1463-9076, DOI: 10.1039/c8cp07603k
Physical Chemistry Chemical Physics 21/9 2019-11-11
2018 Enrico Berardo, Rebecca L. Greenaway, Lukas Turcani, Ben M. Alston, Michael J. Bennison, Marcin Miklitz, Rob Clowes, Michael E. Briggs, Andrew I. Cooper, Kim E. Jelfs
Computationally-inspired discovery of an unsymmetrical porous organic cage
published pages: 22381-22388, ISSN: 2040-3364, DOI: 10.1039/c8nr06868b
Nanoscale 10/47 2019-11-11
2019 Liam Wilbraham, Reiner Sebastian Sprick, Kim E. Jelfs, Martijn A. Zwijnenburg
Mapping binary copolymer property space with neural networks
published pages: 4973-4984, ISSN: 2041-6520, DOI: 10.1039/c8sc05710a
Chemical Science 10/19 2019-11-11
2018 Enrico Berardo, Lukas Turcani, Marcin Miklitz, Kim E. Jelfs
An evolutionary algorithm for the discovery of porous organic cages
published pages: 8513-8527, ISSN: 2041-6520, DOI: 10.1039/c8sc03560a
Chemical Science 9/45 2019-11-11
2019 Enrico Berardo, Rebecca L. Greenaway, Marcin Miklitz, Andrew I. Cooper, Kim E. Jelfs
Computational screening for nested organic cage complexes
published pages: , ISSN: 2058-9689, DOI: 10.1039/c9me00085b
Molecular Systems Design & Engineering 2019-11-11
2018 Liam Wilbraham, Enrico Berardo, Lukas Turcani, Kim E. Jelfs, Martijn A. Zwijnenburg
High-Throughput Screening Approach for the Optoelectronic Properties of Conjugated Polymers
published pages: 2450-2459, ISSN: 1549-9596, DOI: 10.1021/acs.jcim.8b00256
Journal of Chemical Information and Modeling 58/12 2019-11-11
2019 Rebecca L. Greenaway, Valentina Santolini, Angeles Pulido, Marc A. Little, Ben M. Alston, Michael E. Briggs, Graeme M. Day, Andrew I. Cooper, Kim E. Jelfs
From Concept to Crystals via Prediction: Multi‐Component Organic Cage Pots by Social Self‐Sorting
published pages: 16421-16427, ISSN: 0044-8249, DOI: 10.1002/ange.201909237
Angewandte Chemie 131/45 2019-11-11
2019 Lukas Turcani, Rebecca L. Greenaway, Kim E. Jelfs
Machine Learning for Organic Cage Property Prediction
published pages: 714-727, ISSN: 0897-4756, DOI: 10.1021/acs.chemmater.8b03572
Chemistry of Materials 31/3 2019-11-11
2019 Edward Jackson, Marcin Miklitz, Qilei Song, Gareth A. Tribello, Kim E. Jelfs
Computational Evaluation of the Diffusion Mechanisms for C8 Aromatics in Porous Organic Cages
published pages: 21011-21021, ISSN: 1932-7447, DOI: 10.1021/acs.jpcc.9b05953
The Journal of Physical Chemistry C 123/34 2019-11-11

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