Dr Linjiang Chen

Dr Linjiang Chen

School of Chemistry
Assistant Professor of Digital Chemistry

Contact details

School of Chemistry
University of Birmingham
B15 2TT

Linjiang is hugely passionate about the interface between Chemistry and Computer Science and has a strong devotion to the mission of fusing chemical knowledge with state-of-the-art computer science to develop and deploy best-in-class solutions to help address global environmental sustainability challenges, such as changing climate and plastic re/up-cycling. With a joint appointment between the Schools of Chemistry and Computer Science at Birmingham, Linjiang is developing the various research strands outlined in the Research section, through building strong collaborations with colleagues in both Schools and, more broadly, researchers in the chemical sciences and computational sciences communities. Linjiang firmly believes that the interfaces between different scientific disciplines often nucleate the most exciting and impactful research and that the development of technical capabilities for boundary-crossing research can often catalyse new thinking, new hypotheses, and new collaborations.

Linjiang has published over 40 research papers in scientific journals and co-authored two patent applications. Linjiang has (co-)supervised over 10 PhD students in Chemistry and Computer Science.

Linjiang is always keen to interact and collaborate with researchers who are passionate about digital chemistry, computational materials design, and applied AI for scientific discovery.


  • PhD in Molecular Modelling, University of Edinburgh, 2014
  • MEng in Materials Science and Engineering, Queen Mary University of London, 2009
  • BEng in Materials Science and Engineering, Beihang University, 2009


Linjiang was awarded his PhD in molecular modelling from the University of Edinburgh in 2014. His thesis was advised by Dr Tina Düren and concerned improving the modelling of gas adsorption in metal–organic frameworks (MOFs) with coordinatively unsaturated metal sites and flexible MOFs.

From 2013 to 2017, Linjiang was a postdoctoral research associate with Prof Andy Cooper at the University of Liverpool, followed by a research fellow/theme lead position in the Leverhulme Research Centre for Functional Materials Design, until February 2022. While at Liverpool, Linjiang’s research focused on computational studies of porous materials—molecular crystals, covalent organic frameworks (COFs), polymers, and MOFs—for gas adsorption and separation and computational studies of visible-light-driven organic catalysts—small molecules, COFs, and polymers—for water splitting, carbon dioxide reduction, and asymmetric chemical synthesis.

In March 2022, Linjiang joined the School of Chemistry. From November 2022, he is holding a joint appointment between the Schools of Chemistry and Computer Science, as Assistant Professor of Digital Chemistry.

Postgraduate supervision

Linjiang is seeking applications for a PhD studentship on deep learning; the student can be based in Chemistry or Computer Science.


  • Computational discovery and design of functional molecules and materials at the atomic scale
  • Automated and autonomous approaches to large-scale, high-throughput computational screening
  • Machine/deep learning augmented molecular modelling that tackles size and complexity challenges
  • Applied artificial intelligence (AI) for chemistry:
    • intelligent high-throughput screening with heuristic techniques
    • deconvolution of complex, multivariate relationships in (big) chemical data
    • predictive and explainable graph neural networks for molecules and materials
    • semantic AI ‘chemists’ extracting latent knowledge from literature and hypothesizing new research
    • automated analysis of lab instrument data, enabled by deep learning
    • generative learning for inverse design of functional molecules and materials
  • Data-driven, adaptive design of experiments, using Bayesian optimization, evolutionary algorithms, and recommender systems
  • Interactive data visualization improving human interpretability of high-dimensional structure–property–function correlations in big chemical data


A full publication list can be found on Google Scholar.

Selected publications

  • Zhao, C., Chen, L., Che, Y., Pang, Z., Wu, X., Lu, Y., Liu, H., Day, G.M., & Cooper, A.I. (2021), Digital navigation of energy–structure–function maps for hydrogen-bonded porous molecular crystals, Nature Commun., 12, 817.
  • Li, X., Maffettone, P.M., Che, Y., Liu, T., Chen, L., & Cooper, A.I. (2021), Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules, Chem. Sci., 12, 10742–10754.
  • Chen, L., Che, Y., Cooper, A.I., & Chong, S.Y. (2021), Exploring cooperative porosity in organic cage crystals using in situ diffraction and molecular simulations, Faraday Discuss., 225, 100–117.
  • Zhu, Q., Wang, X., Clowes, R., Cui, P., Chen, L., Little, M.A., & Cooper, A.I. (2020), 3D cage COFs: a dynamic three-dimensional covalent organic framework with high-connectivity organic cage nodes, J. Am. Chem. Soc., 142, 39, 16842–16848.
  • Fu, Z., Wang, X., Gardner, A.M., Wang, X., Chong, S.Y., Neri, G., Cowan, A.J., Liu, L., Li, X., Vogel, A., Clowes, R., Bilton, M., Chen, L., Sprick, R.S., & Cooper, A.I. (2020), A stable covalent organic framework for photocatalytic carbon dioxide reduction, Chem. Sci., 11, 543–550.
  • Liu, M., Zhang, L., Little, M.A., Kapil, V., Ceriotti, M., Yang, S., Ding, L., Holden, D.L., Balderas-Xicohténcatl, R., He, D., Clowes, R., Chong, S.Y., Schütz, G., Chen, L., Hirscher, M., & Cooper, A.I. (2019), Barely porous organic cages for hydrogen isotope separation, Science, 366, 613–620.
  • Wang, X., Chen, L., Chong, S.Y., Little, M.A., Wu, Y., Zhu, W., Clowes, R., Yan, Y., Zwijnenburg, M., Sprick, R.S., & Cooper, A.I. (2018), Sulfone-containing covalent organic frameworks for photocatalytic hydrogen evolution from water, Nature Chem., 10, 1180–1189.
  • Pulido, A., Chen, L., Kaczorowski, T., Holden, D., Little, M.A., Chong, S.Y., Slater, B.J., McMahon, D.P., Bonillo, B., Stackhouse, C.J., Stephenson, A., Kane, C.M., Clowes, R., Hasell, T., Cooper, A.I. & Day, G.M. (2017), Functional materials discovery using energy–structure–function maps, Nature, 543, 657–664.
  • Chen, L., Reiss, P.S., Chong, S.Y., Holden, D., Jelfs, K.E., Hasell, T., Little, M.A., Kewley, A., Briggs, M.E., Stephenson, A., Thomas, K.M., Armstrong, J.A., Bell, J., Busto, J., Noel, R., Liu, J., Strachan, D.M., Thallapally, P.K., & Cooper, A.I. (2014), Separation of rare gases and chiral molecules by selective binding in porous organic cages, Nature Mater., 13, 954–960.
  • Chen, L., Mowat, J.P.S., Fairen-Jimenez, D., Morrison, C.A., Thompson, S.P., Wright, P.A., & Düren, T. (2013), Elucidating the Breathing of the Metal–Organic Framework MIL-53(Sc) with ab initio Molecular Dynamics Simulations and in Situ X-ray Powder Diffraction Experiments, J. Am. Chem. Soc., 135, 42, 15763–15773.
  • Chen, L., Morrison, C.A., & Düren, T. (2012), Improving Predictions of Gas Adsorption in Metal–Organic Frameworks with Coordinatively Unsaturated Metal Sites: Model Potentials, ab initio Parameterization, and GCMC Simulations, J. Phys. Chem. C, 116, 35, 18899–18909.

View all publications in research portal