一合 · AEQUO / RESEARCH TECHNOLOGY

Tools for research.
Computation for discovery.

Scientific AI products and advanced computational research for chemistry, materials, and environmental science.

BUILT AROUND
Scientific AIFirst-principlesMachine learning
Original scientific visualisation of an atomistic interface, molecular coordination network, and computational data
ATOMISTIC MODELLING / 01
STRUCTURE · DYNAMICS · ENERGY
AEQ—001
RESEARCH
IN MOTION
INDEPENDENT RESEARCH TECHNOLOGYSCIENTIFIC SOFTWARE · COMPUTATIONAL MODELLING · AI WORKFLOWS

A more connected way to do scientific work.

Aequo brings together purpose-built research tools, structured scientific workflows, and atomistic modelling to help researchers move from complex data to clearer scientific insight.

01SCIENTIFIC AI PRODUCTS

Research tools, engineered for real work.

Agent Skills, scientific software, and integrated workflows designed around the practical demands of research.

02Preview
Research data intelligence

Scientific Result Extraction

An evidence-centred agent workflow for finding, extracting, reconciling, and documenting quantitative results reported in papers and supplementary information.

Agent SkillsOpenCodePython
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04In development
Research automation

Manuscript Project Workflow

A governed, iterative research workspace for evidence-linked manuscript development, scoped revisions, data provenance, and journal compliance.

AntigravityOpenCodePython
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02COMPUTATIONAL RESEARCH SERVICES

From atomic models to research insight.

Method-driven computational support for research questions that benefit from atomistic simulation, data-driven modelling, and mechanistic analysis.

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01
DFT & electronic structure

First-Principles Calculations

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

DFT structure optimisationElectronic structure and density of statesCharge analysis
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02
AIMD & enhanced sampling

Ab Initio Molecular Dynamics

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

AIMD model construction and equilibrationSolvation and coordination analysisPLUMED collective variables
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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.

Training-set design and structure samplingMACE and related MLIP workflowsModel fitting and validation
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04
Data-driven materials research

Materials Machine Learning

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

Literature-derived dataset constructionFeature engineering and data auditingGNN and descriptor-based modelling
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Custom scientific illustration combining an interface structure, energy profile, and data analysis
COMPUTATIONAL WORKFLOWAEQUO / 02
03METHODS & TECHNOLOGY

Built on scientific methods.
Designed for reproducibility.

Useful research automation is more than a prompt. It requires well-defined inputs, deterministic processing, traceable evidence, explicit review points, and outputs that researchers can inspect and reuse.

01 Evidence-aware data workflows
02 First-principles and molecular simulation
03 Machine-learning research pipelines
Explore our approach →
04RESEARCH DOMAINS

Focused on complex
chemical systems.

01Electrochemical interfacesIon solvation · Interphases · Aqueous batteries
02Reaction and surface chemistryCatalysis · Adsorption · Energy landscapes
03Environmental chemistryTransformation pathways · Interface processes
04Materials informaticsData extraction · ML models · Screening
LET'S WORK ON WHAT'S NEXT

Have a research challenge
worth solving?

Explore a research tool, discuss a computational project, or enquire about a workflow for your group.

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