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From Lab to Launch:
AI for Defence and Space

Our integration of AI with advanced materials and chemistry enables
strategic defence systems and transformative space technologies.

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About Us

Our Mission

We are committed to advancing national defence and space exploration through the power of artificial intelligence. By combining intelligent chemistry, novel materials, and AI-driven workflows, we transform discoveries in the lab into strategic capabilities for the nation. From safeguarding security to enabling space frontiers, we pioneer AI innovations that strengthen self-reliance and shape the future.

Our Vision

To establish a future where AI-driven systems empower national security, accelerate space exploration, and ensure technological sovereignty. We envision breakthroughs in research seamlessly deployed into real-world applications that protect, explore, and inspire.

Our Values

  • Integrity — Ethical, responsible, and secure AI systems.
  • Security — Resilient and reliable mission-critical AI.
  • Innovation — Pioneering defence and space technologies.
  • Collaboration — Bridging academia, industry, and defence.

What We Do

  • ⚙️ AI Systems Engineering
  • 🧪 Intelligent Chemistry & Materials
  • 🤖 Autonomous Workflows
  • 🚀 Defence & Space Deployment
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Products

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Identify ideal formulation for solid rocket propellant in a fraction of the time

Expert-curated high quality and diverse dataset of propellant formulations

  • A unified ML platform for data collection, processing, annotation, and visualization.
  • Human-in-the-loop feedback to ensure data quality, reliability, and trustworthiness.
  • Continuous improvement through new data and refined annotations.
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Publications

Selected peer-reviewed journal publications

A Meta-learning Approach for Selectivity Prediction in Asymmetric Catalysis
A Meta-learning Approach for Selectivity Prediction in Asymmetric Catalysis
Nature Communications · 2025
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Bayesian Meta-Learning for Few-Shot Reaction Outcome Prediction of Asymmetric Hydrogenation of Olefins
Bayesian Meta-Learning for Few-Shot Reaction Outcome Prediction of Asymmetric Hydrogenation of Olefins
Angewandte Chemie · 2025
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Deep Kernel Learning for Reaction Outcome Prediction and Optimization
Deep Kernel Learning for Reaction Outcome Prediction and Optimization
Communications Chemistry · 2024
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Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges
Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges
Accounts of Chemical Research · 2022
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A transfer learning approach for reaction discovery in small data situations using generative model
A transfer learning approach for reaction discovery in small data situations using generative model
iScience · 2022
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A transfer learning protocol for chemical catalysis using a recurrent neural network adapted from natural language processing
A transfer learning protocol for chemical catalysis using a recurrent neural network adapted from natural language processing
Digital Discovery · 2021
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A Unified Machine Learning Protocol for Asymmetric Catalysis: A Proof of Concept Demonstration using Asymmetric Hydrogenation
A Unified Machine Learning Protocol for Asymmetric Catalysis: A Proof of Concept Demonstration using Asymmetric Hydrogenation
PNAS · 2020
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Get in Touch

We’d love to hear from you! Fill out the form below or email us directly at info@cisolene.com