Dr Valentina Erastova
Senior Lecturer
Contact details
- Email: valentina.erastova@ed.ac.uk
- Web: Erastova Group Website
Address
- Street
-
Room B21
University of Edinburgh
Joseph Black Building
David Brewster Road
Edinburgh
EH9 3FJ - City
- Post code
Expertise: computational chemistry; molecular modelling; molecular dynamics; atomistic simulation; high-performance computing (HPC); data-driven modelling; interfacial chemistry; adsorption; porous materials; nanoporous materials; soft matter; formulations; polymeric systems; clay minerals; layered materials; biochar; carbon materials; organic-mineral interactions; confined fluids; pollutant capture; soil-contaminant interactions; hydrocarbon behaviour in shale nanopores; subsurface hydrogen storage; caprock integrity; oxide surfaces; catalyst surfaces; sustainable materials design; environmental remediation; nuclear waste management; open-source scientific software
Sectors: AI & Data Modelling, Advanced Materials, Catalysis, Synthesis & Manufacture, Environment & Sustainability, Energy, Healthcare, Analysis
I provide consultancy and collaborative R&D in molecular-level modelling to support materials design, formulation and process-relevant questions across energy, environment and advanced materials. Using atomistic simulation, HPC and data-driven workflows, I help partners understand how molecules adsorb, move and interact in complex porous materials and at interfaces. My main expertise is in clay minerals, biochar and other carbon materials, and organic-mineral interfaces.
I have experience of industry-funded and knowledge-transfer research through projects with Infineum (diesel crystallisation and cold-flow additives), Procter & Gamble (mixing and separation in polymer systems), and Siam Cement Group/Oxford (hydroxide surfaces for catalytic reactions). More recent applications include sustainable materials, contaminant capture, shale and subsurface systems, H2 storage, and modelling relevant to nuclear waste management. I also develop open-source tools such as ClayCode and DynDen, and can support partners with predictive modelling, interpretation of experimental data, bespoke computational workflows, and training in computational chemistry methods.