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COMPUTATIONAL RESEARCH

From a scientific question
to a computational answer.

Research-oriented computational support spanning first-principles calculations, atomistic dynamics, machine-learning potentials, and data-driven materials analysis.

METHODS & CAPABILITIES

A connected set of computational methods.

The appropriate method depends on the scientific question, system size, timescale, available evidence, and required level of theory. Project scope and feasibility are assessed before work begins.

01
DFT & electronic structure

First-Principles Calculations

Atomistic modelling to examine structures, energetics, charge distribution, adsorption, and reaction pathways.

Methods
  • DFT structure optimisation
  • Electronic structure and density of states
  • Charge analysis
  • Adsorption and reaction energetics
  • Transition-state workflows where appropriate
Potential deliverables
  • Calculation models and input files
  • Convergence and method documentation
  • Analysed energies and electronic descriptors
  • Publication-ready figures and technical report
02
AIMD & enhanced sampling

Ab Initio Molecular Dynamics

Finite-temperature atomistic simulations for dynamic interfaces, solvation environments, and activated processes.

Methods
  • AIMD model construction and equilibration
  • Solvation and coordination analysis
  • PLUMED collective variables
  • Metadynamics and free-energy reconstruction
  • Trajectory analysis
Potential deliverables
  • Validated simulation setup
  • Trajectory-derived structural descriptors
  • Free-energy and coordination analyses
  • Reproducible scripts and methods report
03
MLIP & atomistic machine learning

Machine-Learning Interatomic Potentials

Machine-learning potentials and data workflows for extending atomistic simulation towards larger systems and longer timescales.

Methods
  • Training-set design and structure sampling
  • MACE and related MLIP workflows
  • Model fitting and validation
  • Error and extrapolation diagnostics
  • Molecular-dynamics deployment
Potential deliverables
  • Curated training and validation datasets
  • Model configuration and checkpoints as agreed
  • Validation metrics and diagnostics
  • Simulation-ready model package and report
04
Data-driven materials research

Materials Machine Learning

Structured scientific data and machine-learning workflows for screening, property modelling, and hypothesis generation.

Methods
  • Literature-derived dataset construction
  • Feature engineering and data auditing
  • GNN and descriptor-based modelling
  • Study-aware validation
  • Interpretation and candidate prioritisation
Potential deliverables
  • Traceable dataset and data dictionary
  • Model and validation report
  • Candidate rankings with uncertainty notes
  • Reproducible analysis scripts
05
Interfacial and reaction mechanisms

Mechanistic Research Support

Integrated computational analysis to connect atomistic observations with experimentally relevant chemical mechanisms.

Methods
  • Electrochemical interfaces and ion solvation
  • Catalytic reaction pathways
  • Environmental transformation chemistry
  • Surface adsorption and interfacial interactions
  • Cross-analysis of computational and experimental evidence
Potential deliverables
  • Mechanism-focused analysis plan
  • Evidence-linked computational findings
  • Integrated figures and interpretation
  • Methods and limitations documentation
HOW COLLABORATION WORKS

A clear path from scope to deliverables.

Every project starts with scientific scoping. The final methods, computational settings, deliverables, and timeline are agreed for the specific research question.

01

Scope

Discuss the scientific question, system, existing data, and intended research outcome.

02

Design

Assess feasibility and propose models, methods, validation checks, and a project plan.

03

Compute

Build and run the agreed calculations or machine-learning workflow with documented settings.

04

Interpret

Analyse results, document limitations, and prepare figures and technical deliverables.

START WITH THE SCIENCE

Let's scope your
research question.

Share the system, the question, and what you need to understand.

Discuss a project ↗