Jason M. Munro
Boston, MAHi, I’m Jason. I’m the cofounder and CTO of
Atomscale
Cofounder & CTO · 2024–present
From atom to scale.
AI for materials synthesis: connecting process data, understanding material state, and helping engineers control fabrication in real time.
Explore Atomscale
, where we’re building the intelligence layer for atomic-scale engineering.
I work at the intersection of materials science, AI/ML, and scientific software. I’m interested in closing the loop between what we can predict about a material and what we can reliably make—from the first experiment to production.
Previously, I co-led technical development of the
Materials Project
Berkeley Lab · 2019–2024
Making materials data useful.
An open database and software ecosystem for discovering and understanding materials, making computed properties and scientific tools available to researchers worldwide.
Visit the Materials Project
at Berkeley Lab, helping grow the world’s most-cited materials database and software stack to over half a million users.
I have a PhD in materials science and engineering and a bachelor’s in engineering physics. My doctoral and postdoctoral research spanned materials physics, AI/ML, and data-driven approaches to materials discovery Research themes Physics, learning, and data. My work connects materials physics with AI/ML and large-scale datasets to understand how materials behave, predict their properties, and guide discovery. Google scholar .
Open source
GitHubI’ve created, maintained, and contributed to software used by over 2,700 projects and downloaded 80M+ times.
Selected research
All publications on Google Scholar- Accelerated data-driven materials science with the Materials Project
- Predicting and Accelerating Nanomaterial Synthesis Using Machine Learning Featurization
- An equivariant graph neural network for the elasticity tensors of all seven crystal systems
- An improved symmetry-based approach to reciprocal space path selection in band structure calculations
- High-throughput search for magnetic and topological order in transition metal oxides
- Discovering minimum energy pathways via distortion symmetry groups
Elsewhere
Working on something in materials, AI, or scientific software?
Let’s connect.