Imperial College London


Faculty of EngineeringDepartment of Materials

Visiting Professor



+44 (0)20 7594 6801p.d.lee




102Royal School of MinesSouth Kensington Campus





Professor Peter D. Lee joined Imperial College in 1994 after completing his D.Phil. in Materials Science from the University of Oxford. He remained in the department, as the Professor in the area of Metals Processing, until 2011, when he moved to become Director of the Diamond-Manchester Collaboration (DMC) and co-Director of the Manchester X-ray Imaging Facility (MXIF, In Jan 2015, he became Assistant Director (Physical Sciences) of the Research Complex at Harwell (RCaH,, the UK’s Large Facility dedicated to bridging the Physical and Life Sciences disciplines.

Prof. Lee has retained strong links with Imperial with collaborative projects and students in the Depts. of Materials, Earth Sciences and Mechanical Engineering, including the BP-ICAM centre (with Materials & Chemical Engineering), and the EPSRC funded MAPP Advanced Manufacturing Hub (

Prof. Lee has worked in the area of modelling and advanced characterisation of light metals processing  for over 25 years, both in academia and industry. Working in Industry, as a Research Scientist at Alcan International''s Kingston R&D Laboratory,he helped establish their Modelling of Shape Castings Programme, both developing analysis software and applying it to the design of many automotive components. He is Fellow of the Institute of Materials, Minerals and Mining and the Institute of Cast Metals Engineers. He holds a B.A.Sc. in Engineering Science and M.A.Sc. in Materials from the University of Toronto, with his undergraduate and master''s thesis focussing on the simulation of ferrous metallurgical processes.

Prof. Lee is now based at the Research Complex in Harwell and Diamond Light Source, leading a group of 20 researchers and support staff, together with a half-dozen PhD students at Manchester, Imperial, UCL, Warwick and Oxford. The group is developing new imaging and computational techniques to better understanding microstructure evolution during the forming and in service behaviour of metals and other materials. Over the past 20 years he has supervised over 45 PhD students and 50 PDRA’s. Many of these researchers are now leading academics (at universities including Carnegie Mellon University, Lille, Leicester, NTU-Singapore, Warwick, Shanghai and IIT Bombay) and industrialists (at companies including Alcoa, Rolls-Royce, BP, Shell, GE, Ford and Johnson & Matthey).

Prof. Lee has published over 250 journal articles and has been the Plenary, Keynote or Invited Speaker at over 70 international conferences, including the Science of Metals Processing Symposium, Delft, 2010; the Gordon Conf. on ICME 2009 and the Modelling of Casting, Welding and Advanced Solidification Processing, 2009, and he has been Chair of the Technical Committee for many International Conferences. The open source software Prof. Lee and group developed to predict defects in castings, μMatIC, is used by many companies worldwide to help develop new components for more energy efficient transport and gas turbine design.  For example, Ford use this software are part of their “Atoms to Engines” project to produce better castings.



Sun T, Wang H, Gao Z, et al., 2022, The role of in-situ nano-TiB2 particles in improving the printability of noncastable 2024Al alloy, Materials Research Letters, Vol:10, ISSN:2166-3831, Pages:656-665

Massimi L, Clark SJ, Marussi S, et al., 2022, Time resolved in-situ multi-contrast x-ray imaging of melting in metals, Scientific Reports, Vol:12, ISSN:2045-2322

Jonigk D, Werlein C, Lee PD, et al., 2022, Pulmonary and systemic pathology in COVID-19—holistic pathological analyses, Deutsches ärzteblatt International, Vol:119, ISSN:1866-0452, Pages:429-435

Arzilli F, Polacci M, La Spina G, et al., 2022, Dendritic crystallization in hydrous basaltic magmas controls magma mobility within the Earth's crust, Nature Communications, Vol:13

Xian RP, Walsh CL, Verleden SE, et al., 2022, A multiscale X-ray phase-contrast tomography dataset of a whole human left lung, Scientific Data, Vol:9

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