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.
Finding new materials usually requires a large number of coordinated experiments and time-consuming theoretical research. Of course, this search is necessary in the field of materials science because 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 an engine.
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.
Therefore this application will be valuable for this purpose and effective while reducing the cost of the laboratory which until now was increased. 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.