Resources

Atinary, ABB, Mettler-Toledo, and Agilent are building the first Self-Learning and Self-Driving Lab of its kind

Atinary collaborates with ABB Robotics, Mettler-Toledo, and Agilent to launch the first Self-Driving Lab of its kind in accelerating molecular discovery.

Atinary is now listed on AWS Marketplace

Atinary announces availability of its SDLabs platform in AWS Marketplace.

Atinary’s SDLabs Platform is SOC 2 Compliant

Atinary’s SDLabs Platform is now SOC 2 compliant, ensuring top security, availability, and trust for scientists and R&D teams worldwide.

Atinary Joins the AWS Partner Network

Atinary and Amazon Web Services (AWS) partner to accelerate AI-driven R&D and Scientific Discovery.

Dr. Stephen Buchwald Joins Atinary’s Advisory Board

Atinary welcomes Dr. Stephen Buchwald, pioneering chemist behind Buchwald–Hartwig reaction, to advance AI-driven molecular discovery and innovation.

Atinary and Chemspeed teaming up for Self-Driving Labs

Atinary and Chemspeed are teaming up to accelerate R&D with AI and automation. Their no-code AI and automation platforms are now fully integrated, enabling self-driving labs with closed-loop optimization. Live demos launch in Basel (Q3 2025) and Boston (Q4 2025), showcasing faster, smarter experimentation.

Atinary’s Multi-Fidelity ML Algorithm Now Published

Atinary's latest MFBO study in Nature Computational Science shows how mixing high- and low-fidelity experiments boosts R&D and cuts costs.

New machine learning research article from Atinary

Atinary announces their latest research on Multi-fidelity Optimization (MFBO), a machine learning technique that optimizes several information sources with different fidelities.

New publication for Atinary and IBM Research

🔬🚀 Our latest article, a collaboration between Atinary & IBM Research, was published in the May 2024 issue of Chemical Science from the Royal Society of Chemistry!

New publication for Atinary in Chimia

🔬🚀 Our latest article describes a framework and lay out the key technologies to accelerate R&D and optimize experiment planning.