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Catalyst R&D

Molecular Intelligence for
Advanced Optimization

We harmonize lab-scale discovery with industrial engineering reality. By deploying high-fidelity Digital Twins, we collapse the development timeline for next-generation catalysts—transforming experimental uncertainty into margin certainty.

Industrial R&D

Accelerating process science and molecular intelligence for the energy sector.
THE R&D MOAT

Traditional R&D is siloed and resource-heavy. We make it autonomous

By integrating historical lab data, real-time pilot plant streams, and proprietary molecular simulations, we provide an end-to-end environment for rapid catalyst screening and de-risking.

Program-Level KPIs

30–50%

Reduction in experimental cycles

≥10%

Catalyst cost optimization potential identified

Targeted

Measurable improvement in selectivity & activity

Continuous

Learning loop from experimental feedback

Key Features

DIGITAL TWINNING

Process Twins
Create high-fidelity digital replicas of R&D units to simulate various catalyst formulations under diverse operating conditions without physical risk.

KNOWLEDGE UNIFICATION

Semantic Data Lake
Break data silos by unifying structured lab results with unstructured research papers and pilot plant sensor data for comprehensive, cross-domain analysis.

PERFORMANCE SCREENING

AI-Driven Selection
Leverage machine learning to identify promising catalyst candidates and predict life-cycle performance before moving to expensive pilot-stage testing.
The Scientific Workflow

From Lab to Commercial

This workflow showcases a modern, semi-autonomous, human-in-the-loop AI ecosystem engineered to accelerate the discovery and scaling of hydrocracking catalysts for petrochemical plants. The process moves seamlessly from initial digital target configuration to parallel multi-agent virtual screening, followed by a strict human gatekeeping matrix, and concludes with a closed-loop lab feedback mechanism. By dynamically balancing selectivity, activity, lifecycle, and cost metrics, this architecture compresses industrial R&D timelines from months to hours while keeping the scientist in absolute control.
Molecular Design

Capture critical molecular properties and synthesis parameters directly into a secure digital thread, ensuring full traceability and regulatory compliance across your entire chemical asset lifecycle.

Begin the development process directly on the platform configuration interface by pinpointing the specific hydrocracking or petrochemical product line you intend to maximize to anchor the engine’s core goals.

Establish explicit operational guardrails by setting selectivity boundaries and desired active lifecycles, with the ability to assign custom percentage splits (e.g., 25% Cost, 25% Selectivity, 50% Activity) to explicitly mandate top R&D project priorities.

Clicking “Generate Hypothesis” engages the backend engine to narrow down massive chemical possibilities, instantly outputting highly curated, predictive catalyst formulations designed to meet tight performance and affordability targets simultaneously.

Run exhaustive scenarios across the digital twin infrastructure to optimize specific selectivity equations and critical chemical conversion rates at an immense scale before committing to asset engineering.

Leverage parallel processing loops where a digital brain agent isolates molecular structures via virtual screening, a semantic agent mines historical context from past trial sheets and publications, and a predictive agent evaluates regressions to forecast future behavior patterns.

Engineers remain in full gatekeeping authority. Scientists can draft brand-new formulation ideas manually on-screen to challenge the AI’s results, utilizing a side-by-side interactive matrix that scores cost, activity life, and selectivity before hitting “Send to Lab.”

When data returns from physical assays, actual metrics populate directly into the user interface. Researchers compare lab metrics against digital models, manually accepting or rejecting findings to continuously retrain and reinforce core algorithmic models.

Move past simple metric sheets into deep chemistry reasoning. Engineers can use a conversational workspace to query anomalies or reactions, instantly generating a concise summary or a highly detailed scientific analysis on demand.
Accurately map real industrial performance parameters and fluid kinetics, turning months of manual exploratory data compilation into rapid insights to guarantee a seamless transition from bench lab units to commercial crackers.

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