D3Square
AI-ready data from day one of experiment design. Pull scattered computational and experimental records into one place — and turn them into decision-making assets.
- Collect AI-ready data starting from experiment design
- A virtual lab that captures everyday experimental runs by design
- Local LLM analysis — your data never leaves the building
- Inverse design and active learning suggest the next experiment
Stop postponing the data problem
- One place
- Unified experiments, simulations, literature
- AI-ready
- Model-trainable from the start
- On-prem
- Data sovereignty preserved
- Reverse
- From data to the next experiment
Core capabilities
A toolset that lets research teams capture data through everyday work — and use that same data as the basis for the next R&D decision.
Virtual lab
Define your inputs, instruments, machine variables, and research templates once. Every subsequent experiment is recorded as structured data automatically.
AI-ready collection
Data is captured in a model-trainable format from the experiment-design step — no separate cleanup or post-processing.
Unified research data
Pull experiments, simulations, and literature data from across teammates and projects into a single workspace.
Local LLM analysis
Run analysis with an LLM hosted inside your organization. Sensitive R&D data does not need to leave the building.
Predictive models
Train property-prediction models on your accumulated data, compare them, and inspect uncertainties — all in the same view.
Inverse design & active learning
Work backwards from the property you want to candidate compositions and processes, with active learning ranking the next experiment.
Project flow
Set up the virtual lab once. From the next cycle on, data accumulates and models grow naturally.
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01
Set up the virtual lab
Register your inputs, instruments, machine variables, and research templates so your lab's reality maps onto D3Square.
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02
Run experiments, capture data
Log everyday experiments inside D3Square — results become AI-ready data the moment they are entered.
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03
Train models
Use the accumulated data to train and validate prediction models. The variables that drive your result reveal themselves.
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04
Inverse design & active learning
Work backwards from a target property to candidate compositions and processes, and let active learning rank what to try next.
Where it is used
Korean government research institute
Alloy composition design — using D3Square for experimental data integration and inverse-design workflows.
Korean enterprise R&D
A secondary-battery materials team accumulating composition and process data, and grounding decisions in it.
Universities (multiple)
Various labs using D3Square for lab-level data asset-building and smoother handover between students.
Data scattered everywhere becomes an asset when it comes together.
Tell us where your data lives today and what your R&D goal is. We will map out where to start collecting.