AutoBot: Machine Learning and Robotics Accelerate the Future of Materials Discovery

AutoBot platform for materials discovery

The search for new materials has always been a time-consuming process, often relying on trial-and-error experimentation guided by human intuition. Now, scientists at the Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a revolutionary platform called AutoBot, which merges machine learning, robotics, and automated synthesis to accelerate discovery and optimization of advanced materials. Their recent study, published in Advanced Energy Materials, highlights how AutoBot dramatically reduces the time needed to develop high-performance thin films and other functional materials.

A New Paradigm in Materials Research

AutoBot integrates robotic synthesis, high-throughput characterization, and machine learning into a single closed-loop system. Unlike traditional methods, where researchers manually test one set of parameters at a time, AutoBot evaluates thousands of possible fabrication conditions and automatically selects the most promising ones. In one demonstration, AutoBot explored over 5,000 combinations of synthesis parameters for metal halide perovskites, a class of materials with potential applications in LEDs, lasers, and photodetectors.

Incredibly, AutoBot only needed to test about 1% of the total combinations to identify the optimal synthesis conditions. What would typically take a year of manual experimentation was achieved in just weeks. This represents a major step toward creating autonomous optimization laboratories capable of accelerating materials innovation across a wide range of technologies.

How the AutoBot Works

The platform’s operation follows an iterative learning loop:

  1. AutoBot synthesizes thin films by varying parameters such as temperature, treatment timing, and humidity.
  2. It characterizes the films using UV-Vis spectroscopy, photoluminescence spectroscopy, and imaging.
  3. The system processes the data into a single score that represents film quality.
  4. Machine learning algorithms analyze the results, predict outcomes for unexplored combinations, and guide the next experiments.

This multimodal data fusion—converting diverse datasets into unified quality metrics—was key to AutoBot’s success. The platform’s learning rate declined rapidly after sampling fewer than 1% of the possibilities, showing how efficiently it optimized its predictions.

Overcoming a Key Challenge for Perovskites

Metal halide perovskites are highly promising materials, but they are also extremely sensitive to humidity, which complicates scaling production for industrial use. AutoBot identified a humidity range (5%–25%) where high-quality films could be synthesized by fine-tuning the other parameters. This finding may significantly reduce the cost and complexity of manufacturing perovskite-based devices, bringing them closer to commercialization.

Toward Autonomous Materials Laboratories

According to Berkeley Lab scientist Carolin Sutter-Fella, AutoBot represents a paradigm shift in how materials are explored and optimized. By coupling robotics with AI-driven decision-making, AutoBot is paving the way for fully autonomous laboratories where AI “co-researchers” can design, test, and refine materials with minimal human intervention. This approach could accelerate advances in clean energy, electronics, quantum devices, and more.

The work was conducted by an international team including researchers from the University of Washington, University of Nevada, UC Berkeley, UC Davis, and Friedrich-Alexander-Universität Erlangen–Nürnberg. Their collaboration underscores the global effort to bring artificial intelligence into the heart of materials science.

Read the full article here: AutoBot platform uses machine learning to rapidly find best ways to make advanced materials (TechXplore).

This article for Quantum Server Networks was prepared with the help of AI technologies to enhance clarity, readability, and SEO optimization.

Sponsored by PWmat (Lonxun Quantum) – a leading developer of GPU-accelerated materials simulation software for cutting-edge quantum, energy, and semiconductor research. Learn more at: www.pwmat.com

📘 Download their company brochure to explore software features, case studies, and success stories.

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#AutoBot #MachineLearning #Robotics #MaterialsDiscovery #Perovskites #ThinFilms #AdvancedMaterials #AIinScience #BerkeleyLab #QuantumServerNetworks

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