NIMO: A Software Platform for Closed-Loop Materials Exploration with Diverse AI Algorithms

2026-06-13

AI Summary

This work addresses the challenge of seamlessly integrating artificial intelligence algorithms with heterogeneous robotic hardware—a key bottleneck hindering the deployment of self-driving laboratories in materials discovery. To overcome this, we present an open-source software platform that decouples AI and robotic systems through a modular architecture, employs a CSV-based communication protocol to ensure flexible interoperability, and introduces a discrete candidate pool framework capable of incorporating domain knowledge. The platform provides a unified Python API integrating twelve AI algorithms and includes a no-code desktop application to enable participation by non-programmers. It has been successfully validated across six experimental domains, including electrolyte discovery, organic synthesis, and thin-film exploration, significantly advancing the accessibility and democratization of autonomous scientific research.

Abstract

Self-driving laboratories (SDLs), where artificial intelligence proposes subsequent experiments and robotic systems execute them, are rapidly becoming the vanguard of materials discovery. A critical bottleneck, however, lies in seamlessly bridging diverse AI algorithms tailored for specific exploration goals with the heterogeneous robotic hardware found across different laboratories. Here, we present NIMO, an open-source software platform designed to dissolve this barrier through three core paradigms: a modular AI-robot decoupling mediated via simple CSV file exchange, a discrete candidate-pool architecture that seamlessly absorbs domain knowledge, and a unified Python interface pre-loaded with twelve distinct AI algorithms. In this Perspective, we review the operational principles of each algorithm alongside six diverse SDL implementations driven by NIMO, covering electrolyte discovery, organic synthesis, thin-film exploration, fuel-cell process informatics, coffee-ring phase exploration, and legacy liquid-handling automation. One of these also demonstrates NIMO's seamless interoperability with the IvoryOS orchestration framework. To democratize autonomous science, we also introduce a no-code desktop application that enables intuitive, human-in-the-loop exploration for non-programmers. NIMO is freely available at https://github.com/NIMS-DA/nimo, offering a versatile, plug-and-play foundation to accelerate autonomous materials exploration across diverse experimental landscapes.

Problem

• self-driving laboratories
• AI-robot integration
• materials discovery
• heterogeneous hardware
• autonomous science

Innovation

• modular AI-robot decoupling
• discrete candidate-pool architecture
• unified Python interface
• no-code desktop application
• self-driving laboratories