Matlantis has announced a new AI agent integration for its universal atomistic simulator that lets researchers build and run simulations using natural-language instructions.
The release includes a public Skills library on GitHub, available immediately, and an upcoming installer that will run Anthropic’s Claude Code directly inside the Matlantis terminal environment.
Atomistic simulation has long required a combination of computational chemistry knowledge, programming fluency, and environment expertise, a barrier that has kept the technology in the hands of specialists even as the rest of materials R&D has become more cross-functional. Matlantis already removed much of the infrastructure burden by delivering its high-accuracy AI model, PFP (Preferred Potential), as a cloud service. This release targets the last remaining barrier: the scripting layer that sits between a researcher’s question and a working simulation.
The integration arrives as AI agents move from experimentation to production use in technical workflows. By embedding Claude Code inside Matlantis and giving it access to a domain-specific Skills library, Matlantis is connecting general-purpose agent capability to the specialized procedures and APIs that simulation work actually requires.
“Simulation has always had three barriers: the infrastructure, the science, and the scripting. We’ve spent five years removing the first one,” said Daisuke Okanohara, President & CEO, Matlantis. “This release is about removing the third, and it changes who can credibly do atomistic simulation inside an R&D organization.”
A public Skills library
The Skills library packages Matlantis-specific knowledge – functions, APIs, and representative workflows – into a format that general-purpose AI agents can load and reference. This gives agents access to expertise that isn’t present in their underlying training data, allowing them to generate more accurate, context-aware simulation scripts on the first attempt. Initial workflows include structure relaxation, molecular dynamics, reaction pathway exploration, crystal structure prediction, visualization, and retrieving structures from external databases. The library will expand as customer use cases evolve.
Claude Code, embedded in the simulation environment
An installer scheduled for release in a forthcoming update will let users launch Anthropic’s Claude Code directly from the Matlantis terminal. Running the agent in-context means researchers can describe what they want to simulate in plain language, generate or edit the underlying scripts, run the calculation, and interpret outputs, all without leaving the workflow. For experimental researchers who lack programming fluency, this opens up simulation as a practical tool. For computational specialists, it removes the repetitive scripting work that typically sits between hypothesis and result.
“Matlantis already enables non-specialists to predict material behavior with high accuracy and speed,” said Okanohara. “With AI agents now a practical interface for complex tools, we can close the remaining gap between a researcher’s question and a running simulation. The goal is to make undiscovered knowledge in the physical world as accessible through natural language as knowledge in the textual world has become.”
For more information, visit matlantis.com
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