Researchers have developed an artificial intelligence (AI) algorithm called Closed-Loop Autonomous System for Materials Exploration and Optimization (CAMEO) that discovered a potentially useful new material without requiring additional training from scientists. The AI system could help reduce the amount of trial-and-error time scientists spend in the lab, while maximizing productivity and efficiency in their research.

In the field of materials science, scientists seek to discover new materials that can be used in specific applications such as metals that are light but also strong or one that can withstand high stresses and temperatures for a jet engine. Finding such new materials usually takes a large number of coordinated experiments and time-consuming theoretical searches. If a researcher is interested in how a material’s properties vary with different temperatures, then the researcher may need to run 10 experiments at 10 different temperatures. But temperature is just one parameter. If there are five parameters, each with 10 values, then that researcher must run the experiment 10 × 10 × 10 × 10 × 10 times — a total of 100,000 experiments. CAMEO can ensure that each experiment maximizes the scientist’s knowledge and understanding, skipping over experiments that would give redundant information.

Machine learning is a process in which computer programs can access data and process it themselves, automatically improving on their own instead of relying on repeated training. This is the basis for CAMEO, a self-learning AI that uses prediction and uncertainty to determine which experiment to try next.

CAMEO looks for a useful new material by operating in a closed loop: It determines which experiment to run on a material, does the experiment, and collects the data. It can also ask for more information, such as the crystal structure of the desired material, from the scientist before running the next experiment, which is informed by all past experiments performed in the loop.

The AI is also designed to contain knowledge of key principles including knowledge of past simulations and lab experiments, how the equipment works, and physical concepts. CAMEO is armed with the knowledge of phase mapping, which describes how the arrangement of atoms in a material changes with chemical composition and temperature. Understanding how atoms are arranged in a material is important in determining its properties such as how hard or how electrically insulating it is and how well it is suited for a specific application.

CAMEO discovered the material Ge 4Sb 6Te 7, which the group shortened to GST467. CAMEO was given 177 potential materials to investigate, covering a large range of compositional recipes. To arrive at this material, CAMEO performed 19 different experimental cycles, which took 10 hours, compared with the estimated 90 hours it would have taken a scientist with the full set of 177 materials.

The material is composed of three different elements (germanium, antimony and tellurium, Ge-Sb-Te) and is a phase-change memory material that changes its atomic structure from crystalline (solid material with atoms in designated, regular positions) to amorphous (solid material with atoms in random positions) when quickly melted by applying heat. This type of material is used in electronic memory applications such as data storage.

GST467 also has applications for photonic switching devices that control the direction of light in a circuit. They can also be applied in neuromorphic computing, a field of study focused on developing devices that emulate the structure and function of neurons in the brain, opening possibilities for new kinds of computers as well as other applications such as extracting useful data from complex images.

CAMEO can be used for many other materials applications. The code for CAMEO is open source and will be freely available for use by scientists and researchers.

For more information, contact Alex Boss at This email address is being protected from spambots. You need JavaScript enabled to view it.; 301-975-3611.

Tech Briefs Magazine

This article first appeared in the June, 2021 issue of Tech Briefs Magazine.

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