In materials science, data is not the problem—context is. Modern synthesis techniques operating at atomic precision now generate exponentially more raw data than they did even a decade ago. Yet, despite this explosion of data, the field continues to face a critical bottleneck: a scarcity of labeled data. Without it, the application of AI and Machine Learning (ML)—technologies with huge potential to improve empirical design and process optimization—remains largely untapped in materials development and scale-up.

Labeled data is the cornerstone of effective AI and ML models. It enables algorithms to find patterns, predict outcomes, and optimize processes. In sectors like natural language processing or computer vision, large labeled readily available datasets have catalyzed decades' worth of progress in just a few years. But in materials science, the situation is very different. While experimental tools and simulation engines are vastly more powerful today, the raw data they generate still depends heavily on manual interpretation by domain experts to be transformed into structured, labeled insights and applied in synthesizing the actual materials.

This human-in-the-loop approach—tedious, expensive, and error-prone—is incompatible with the scale of challenges facing several critical industries. Advanced semiconductors, solid-state batteries, next-generation photonics, and quantum computing all rely on successful integration of new materials and devices. Yet every step toward innovation introduces complexity at the atomic level: defects, interfaces, novel phases, and unpredictable interactions. Without high-quality, contextualized data at scale, humans and even the best AI tools are flying blind.

At Atomscale, we believe this bottleneck must—and can—be broken. Our platform is purpose-built to automate the extraction, integration, and labeling of data across the entire materials lifecycle. Whether it's microscopy images, spectroscopic data, synthesis logs, or modeling output, our system transforms heterogeneous data into a unified, labeled foundation that can be readily compared across samples and materials systems. Since each of these raw data sources captures just a small slice of the state of the material, we build task-specific models to distill as much signal as possible from each measurement to build this unified foundation. Using models instead of manual effort to achieve this reduces bias and provides the scale needed to input data to our AI agents, which can monitor and deliver feedback on complex processes to enable commercialization of new transformative materials and accelerate production scaling.

This is speeding up scaling of materials platforms by applying computation to model the physical world in real-time in a way that's never been done before, enabling a new era of materials engineering. By automating the creation of labeled datasets at scale, we give our customers the power to identify trends, predict performance, and optimize process variables faster and more reliably than ever before. For companies racing to commercialize new materials, Atomscale delivers a critical capability: data-driven decision-making at the atomic scale.

In the years ahead, the pace of innovation will increasingly depend not just on how much data we can gather, but how intelligently we can use it. Labeled data is the bridge between raw experimental output and actionable insight. Atomscale exists to build that bridge—for every lab, every fab, and every breakthrough yet to come.