Dr Devis Di Tommaso

Profile

Dr Devis Di Tommaso is a Reader in Computational Chemistry. His research lies at the interface of computational chemistry, inorganic chemistry, and materials science, with a strong focus on developing atomistic and data‑driven approaches to understand and design materials for sustainable energy and environmental applications.

He has extensive expertise in density functional theory (DFT), ab initio and classical molecular dynamics, and machine‑learning methods for materials discovery. His work combines fundamental mechanistic insight with predictive modelling to guide the design of catalysts and functional materials for CO₂ conversion, electrocatalysis, photocatalysis, and mineral carbonation.

Undergraduate Teaching

• Chemistry Research Project and Chemistry Investigative Project (CHE600 and CHE601)
• Chemical Research Project (CHE700)
• Computational Chemistry (CHE305)
• Machine Learning in Materials Discovery (SPC723P)

Research

Research Interests:

Dr Di Tommaso’s research activity focuses on the atomistic‑level understanding and rational design of functional materials, combining first‑principles simulations, molecular dynamics, and machine‑learning techniques.

A major research theme is the electrochemical and photocatalytic conversion of small molecules, in particular CO₂ reduction and nitrogen reduction. His work has addressed single‑atom, binuclear, and nanostructured catalysts, including amorphous alloys and metal nanowires, providing mechanistic insight into activity and selectivity trends and identifying structure–property relationships that guide experiments. These studies frequently integrate DFT with machine‑learning models to accelerate catalyst screening and optimisation.

A second core area concerns the chemistry of carbon‑based materials, including graphene, nanoporous graphene, and carbon coatings grown by chemical vapour deposition or methane activation. Through combined computational and experimental studies, his research has clarified growth mechanisms, defect formation, and the role of surfaces and radicals in controlling structure and functionality.

Dr Di Tommaso also has a long‑standing interest in interfacial aqueous chemistry and mineralization processes, with applications to CO₂ mineral carbonation, ion separation, and water purification. His work has provided fundamental insight into ion hydration and dehydration, nucleation and crystal growth in solution, and electric‑field‑assisted nanofiltration, linking atomistic processes to macroscopic behaviour.

More recently, he has contributed to the development of new machine‑learning architectures for chemical and materials modelling, including graph‑based and transformer models capable of capturing long‑range interactions in complex systems.

Overall, his research integrates theory, simulation, and data‑driven approaches to address challenges in sustainable energy, carbon utilisation, and advanced materials design.

See Devis Di Tammaso’s research profile pages including details of research interests, publications, and live grants.

Examples of research funding:

Dr Di Tommaso has secured and contributed to a broad portfolio of competitive research funding as Principal Investigator and Co‑Investigator, supporting projects in computational chemistry, catalysis, CO2 utilisation, and materials modelling. Recent and current funding includes:

• Leverhulme Trust — Optimizing CO₂ Mineralization: From Atomistic Detail to Reactor Design (PI)
• Daphne Jackson Trust —Catalysts to Promote the Water Splitting Reaction (PI)
• The Royal Society — Tungsten‑free Superhard Materials: Earth‑abundant Metal Ternary Borides Solid Solutions (PI)
• Royal Society of Chemistry — First‑Principles Design of Porous Nanographene Formation on Metal Oxides (PI)
• QMUL Impact Fund — Next Generation Reactors for Multi‑kg Scale CO₂ Mineralization (Co‑I)
• H2020 Accelerating CCS Technologies — Fundamental Studies of Mineral Carbonation with Applications to CO₂ Utilisation (PI)

These grants have supported interdisciplinary collaborations with experimental groups and international partners, and have funded PhD students, postdoctoral researchers, and the development of advanced computational methodologies.

Publications

• Anselmi, M.; Slabaugh, G.; Crespo-Otero, R.; Di Tommaso, D. “Accelerating discovery through integration: a DFT validated machine learning framework for screening MOF photocatalysts”, 2026, DOI: https://doi.org/10.1039/D5TA08107F
• Lin, W.; Di Tommaso, D.*. “Screening Nitrogen-Coordinated Single Atom Catalysts on Armchair Carbon Nanotubes for Enhanced Electrochemical CO2 Reduction to C1 Products”. ACS CATALYSIS 2025, 15, 16463-16475. DOI: https://doi.org/10.1021/acscatal.5c03565
• Muthuperiyanayagam, A.; Pedretti, E.; Righi, M. C.*; Di Tommaso, D.*. Optimized selectivity in CO2 electrochemical reduction using amorphous CuNi catalysts: insights from density functional theory and machine learning simulations. JOURNAL OF ENERGY CHEMISTRY 2026, 112, 1014-1025. DOI: https://doi.org/10.1016/j.jechem.2025.08.089
• Pirabul, K.; Zhao, Q.; Pan, Z.; Liu, H.; Itoh, M.; Izawa, K.; Kawai, M.; Crespo-Otero, R.; Di Tommaso, D.*; Nishihara, H.*. Silicon radical-induced CH4 dissociation for uniform graphene coating on silica surface. SMALL 2024, 20, 2306325. DOI: https://doi.org/10.1002/smll.202306325
• Anselmi, M.; Slabaugh, G.; Crespo-Otero, R.*; Di Tommaso, D.*. Molecular graph transformer: stepping beyond ALIGNN into long-range interactions. DIGITAL DISCOVERY 2024, 3, 1048-1057. DOI: https://doi.org/10.1039/D4DD00014E  

Supervision

PhD Supervision

• Akshayini Muthuperiyanayagam (first supervisor)
• Marco Anselmi (first supervisor)
• Wuyang Lin (first supervisor)
• Elisavet Tsakelidou (first Supervisor)
• Houlin Yu (second supervisor)
• Alina Zakrjevsky (second supervisor)