Updates

We're excited to share our latest work, Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization, which uses Atomscale's AI/ML powered automation to accelerate key steps in the materials synthesis feedback loop, deliver insights faster and earlier, and save time while helping avoid doomed trials. Thanks to our collaborators Yansong Li, Guanyu Zhou, Rehan Younas, and Chris Hinkle on materials growth and interaction + feedback with Atomscale.

We're thrilled to announce that Jason Munro, PhD has officially joined Atomscale as CTO. Jason brings a wealth of expertise from his prior role as a staff researcher at Lawrence Berkeley National Lab, where he helped lead the development of the Materials Project, the most popular public database and web interface for materials science data in the world. Jason is a computational materials scientist who completed his PhD at Penn State University and Postdoc at Berkeley Lab. Prior to joining the Materials Project, he worked on research problems involving quantum mechanical materials simulation and applied machine learning for materials science. Jason's transition from part-time cofounder to full-time CTO is an exciting and transformative moment for Atomscale as we continue to build and scale our AI-enabled platform for advanced materials synthesis. Jason will accelerate and drive our product development towards real-time feedback for materials synthesis and expansion to widespread techniques in the semiconductor industry, including CVD, SEM, AFM, and ellipsometry. Please join us in welcoming Jason and stay tuned for more exciting updates on the journey ahead!

We're making significant steps towards our vision of Atomscale as a unified platform for materials science at the nanoscale. Highlights from the latest improvements include: Near-real-time RHEED processing using streaming screen capture that creates datasets on the fly. A data fusion structure that enables easy organization and annotation of any data type. Flexible API client for programmatic interaction with data and annotations both within and between samples.