Atomic Tessellator
AI-powered platform for ab-initio materials discovery simulations.
| Date | Investors | Amount | Round |
|---|---|---|---|
N/A | Seed | ||
| Total Funding | 000k | ||
Founded in 2022 by Alain Richardt and headquartered in Auckland, New Zealand, Atomic Tessellator is developing an ab-initio, in-silico material discovery platform. The company aims to accelerate the research and development loop for material scientists and computational chemists by leveraging AI and cloud computing to automate and speed up complex simulations.
Founder Alain Richardt, a programmer with a rekindled passion for chemistry, started the company after recognizing that existing tooling for chemical simulations was inadequate. He began Atomic Tessellator as a side project to build better, more user-friendly tools. The company's platform is designed to make high-performance computing accessible to material scientists who may not be experts in distributed cloud engineering. It manages complex computational tasks, allowing researchers to focus on science rather than infrastructure. The technology combines generative AI and data streaming to analyze massive molecular datasets, enabling simulations that are significantly faster than traditional methods. For example, work that previously took a global team of scientists a year to complete can now be done in hours.
Atomic Tessellator serves both academic and industrial researchers, with enterprise solutions offered via hosted cloud, dedicated cloud, or on-premise deployments. The platform addresses key industry challenges, such as the need for new materials for electric vehicle batteries, carbon capture, and fusion reactors. It also helps companies stay ahead of regulatory compliance and supply chain disruptions by facilitating the discovery of new materials to replace those facing scrutiny, like PFAS and microplastics. The business has received seed funding from investors including Salus Ventures and Side Stage Ventures.
Keywords: material discovery, computational chemistry, ab-initio simulation, in-silico, materials science, high-performance computing, generative AI, molecular simulation, quantum chemistry, catalyst discovery, high-entropy alloy design, automated workflows, property prediction, research automation, drug discovery, battery materials, carbon capture, sustainable manufacturing, scientific software, physics-based simulation