Research Data Platform

From Experiment to Optimal Design.

Unify scattered research data, train predictive models, and run
multi-objective optimization — all in one platform.

The Challenge

Research data is still
scattered everywhere.

Experimental data spread across spreadsheets, local files, and emails. Manual trial-and-error experiment design. Disconnected analysis workflows. D3Square solves this.

Fragmented Data

Experimental data scattered across tools, formats, and team members

Repetitive Experiments

Manually iterating experiments without data-driven guidance

Disconnected Analysis

Data collection, modeling, and optimization in separate environments

Platform

One connected pipeline
for your entire research.

Data Management Upload / Validate / Preprocess Model Training Regression / Classification Inverse Design Multi-objective Optimization Active Learning Optimal Sample Selection

Features

Precision tools
for every stage.

01

Data Buckets

Upload experimental datasets and validate them automatically. Handle missing values, categorical encoding, and outlier detection in a single preprocessing pipeline.

  • Automated data validation and error detection
  • Missing value and outlier preprocessing
  • Correlation analysis and visualization
  • Version control and history restoration
TemperaturePressureYieldDensity
1,2403.294.17.85
1,18091.77.82
1,3103.596.37.91
1,2753.395.07.88
1,1953.192.47.83
Validated — 1 missing value detected
02

Model Training

Train predictive models directly from your datasets. Compare multiple algorithms and kernels, then interpret model decisions with SHAP analysis.

  • GPR, XGBoost, linear regression and more
  • Cross-validation and performance comparison
  • SHAP-based feature importance analysis
  • Model publishing and version management
Predicted Actual R² = 0.97
03

Inverse Design

Combine trained models for multi-objective optimization. Maximize targets, set constraints, and explore the Pareto frontier to find optimal design parameters.

  • Genetic algorithm, grid, and random search
  • Multi-objective optimization with Pareto analysis
  • Constraint-based design variable exploration
  • Bayesian optimization support
Objective 1 (minimize) Objective 2 (maximize) Pareto Front
04

Active Learning

Recommend the optimal next samples to measure. Maximize information gain with minimal experiments, reducing research cost and time.

  • Expected Improvement / UCB strategies
  • Thompson Sampling-based recommendation
  • Iterative model refinement cycles
  • Minimized experimental cost
Train Model
Find Uncertainty
Suggest Samples
Run Experiment
Iterative Refinement

Workflow

From lab bench
to optimal design.

Step 1

Collect Data

Upload experimental datasets to buckets with automated validation and preprocessing.

Step 2

Explore & Analyze

Run correlation analysis, scatter plots, and distribution analysis to understand data structure.

Step 3

Train Models

Compare multiple algorithms and select the best predictive model for your targets.

Step 4

Deploy & Predict

Publish validated models and use them for predictions on new experimental conditions.

Step 5

Optimize

Run multi-objective optimization to find the optimal design parameter combinations.

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%

Fewer experimental iterations

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Faster data analysis

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Full experiment traceability

Use Cases

Built for diverse
research domains.

Materials Science

Optimize alloy compositions and heat treatment conditions to achieve target material properties.

Chemical Processes

Systematically explore reaction conditions and catalyst configurations to maximize yield.

Manufacturing QC

Build quality prediction models from process data and maintain optimal operating conditions.

Energy Research

Systematically manage experimental data for battery materials, catalysts, and energy systems.

Laboratory

Manage your lab,
not just your data.

Reagent inventory, equipment status, specimen tracking, and experimental protocols — everything your lab needs in one system, so you can focus on research.

Inventory
Equipment
Protocols
Collaboration
Active Experiments
12
Registered Reagents
847
Equipment Uptime
94%
Specimens This Month
156

Ready to transform
your research workflow?

We welcome inquiries about deployment, demo requests, and technical consultations.