enMAT | AI-Assisted Materials R&D Decision Support

Methodology

How the enMAT Workflow Works

A systematic, loop-based framework to accelerate materials optimization.

01.Define the R&D Target

Translate business or device-level goals into measurable material targets such as conductivity, dielectric constant, thermal expansion, stability, lifetime, loss tangent, process window, or reliability metrics.

02.Build the AI-Ready Dataset

Integrate public materials databases, literature evidence, first-principles descriptors, customer-specific experimental data, and prior screening results where available.

03.Generate and Rank Candidates

Use physics-informed AI and active learning to rank candidate compositions, formulations, or process windows.

04.Design the Screening Strategy

Convert the ranked candidate list into a high-throughput screening plan with clear experiment matrices, sample preparation routes, characterization methods, decision criteria, and data-capture rules.

05.Run Focused Experiments

Execute a focused set of experiments designed to validate candidates, compare trade-offs, and generate high-quality learning data.

06.Learn from Results

Analyze measured properties, process sensitivity, failure modes, uncertainty, and data gaps to identify the most informative next step.

07.Update the Model

Feed new experimental results back into the model to improve the next recommendation cycle.

Outcomes

What We Deliver

Practical, actionable decision-support deliverables tailored to your materials R&D project.

1. Research Landscape Map

A structured view of relevant material families, known bottlenecks, competing approaches, and R&D opportunities.

2. Candidate Decision Map

A ranked and explainable map of candidate materials, trade-offs, evidence level, uncertainty, and experimental risk.

3. Search-Space Framing Analysis

A practical narrowing of composition, process, and validation directions based on physics, literature, prior data, and R&D objectives.

4. HTS Campaign Design

An experimental matrix, screening criteria, measurement plan, and iteration strategy for efficient validation.

5. Process / Recipe Recommendation Framework

A data-driven framework for recommending process conditions or recipe adjustments for new or modified processes.

6. AI-Ready Dataset Framework

A schema and data structure for composition, process, characterization, property, and reliability data.

7. Experimental Action Blueprint

A concrete next-step plan showing what to test, why to test it, how to record results, and how to update the next iteration.

Example Deliverable

What an enMAT Decision Map Looks Like

enMAT does not deliver a single “magic composition.” We deliver a decision map: ranked candidates, property ranges, evidence level, uncertainty, feasibility indicators, and the next recommended experiments.
* This is an illustrative example of the decision-support format. Actual candidate ranking and descriptors are customized to the material system, available data, target properties, and measurement conditions.

SOEC Anode Decision Map Example

Target specs: Oxygen electrode conductivity > 200 S/cm, TEC < 12.5 × 10⁻⁶/K

Candidate	Conductivity Range	TEC Range	Stability Indicator	Co Reduction Potential	Compatibility Risk	Experimental Risk	Next Action
Candidate 1	220–260 S/cm	12.0–12.4 ×10⁻⁶/K	Favorable	Low	Low	Medium	Secondary confirmation
Candidate 2	200–240 S/cm	12.2–12.6 ×10⁻⁶/K	Moderate	Medium	Low	Medium	Process sensitivity check
Candidate 3	180–225 S/cm	11.8–12.3 ×10⁻⁶/K	Favorable	Low	Medium	Low	Primary validation

Security Note: This output is an illustrative example of the decision-support format. Actual candidate ranking, compositions, and computational descriptors are fully customized and kept strictly confidential under client privacy.