Atomscale exists to build the bridge between raw experimental data and actionable insight to unlock the future of materials science. Check out our latest article on the data challenges in materials science: open.substack.com/pub/atomscal...

AI agents are poised to transform the commercialization of advanced materials. At Atomscale, we’re driving this transformation with our next-generation AI agents, purpose-built for atomic-scale engineering.

www.linkedin.com/pulse/ai-age...

Atomscale | Intelligent Atomic Scale Engineering Atomscale is building the future of atomic-scale engineering, applying AI to enable new material innovations.

Our mission to enable breakthroughs in advanced materials synthesis with state of the art AI is more focused than ever. Visit us at www.atomscale.ai!

Exciting news — Atomic Data Sciences is now Atomscale!

Our new name reflects our evolution from automating data analysis for materials science to building the comprehensive intelligence layer for atomic scale engineering.

The Power of Direct Real-Time Feedback for Atomic-Scale Engineering Atomic-scale engineering aims to create high-performance and high-value materials by controlling the assembly of matter with extreme precision.

Check out how real-time feedback unlocks this: open.substack.com/pub/atomicda...

Atomic Data Sciences is delivering the first end-to-end AI solution for advanced materials synthesis, leveraging the convergence of in-situ hardware, applied AI for materials science, and general foundation models to enable a new low-level programming language for the physical world.

Our featurization scheme generalizes across materials without customization. Reach out by DM or email info at atomicdatasciences dot com to learn more and try a demo!

We also predict film composition in-process with similar accuracy to expert practitioners. Even within a lab-scale synthesis campaign, applying these predictive models can save hundreds of hours of expert and equipment time.

We develop workflows to improve the efficiency of materials synthesis and characterization using the tools available in AtomCloud. With just ~10 conventionally labeled synthesis trials, we predict the defect rate of future trials with >80% accuracy.

Automated featurization of RHEED images, quantifying qualitative labels, and generating proxy models across techniques. https://pubs.acs.org/doi/10.1021/acs.nanolett.4c04500

We are excited to share that out paper, Predicting and Accelerating Nanomaterial Synthesis Using Machine Learning Featurization, a collaboration between Atomic DS and the Hinkle Lab at the University of Notre Dame, is published in ACS Nano Letters.
pubs.acs.org/doi/10.1021/...