Machine learning can predict material behaviour, allowing you to focus testing on the most promising candidates. In a nanomaterial thermal conductivity project, we implemented a machine learning model that analysed previous test data & predicted which materials were most likely to exhibit high thermal conductivity. This allowed us to focus physical testing only on the top 20% of materials predicted to perform well, reducing the overall testing burden by 50%. By explaining the technical workings of the model to the research team, I ensured everyone understood how machine learning could accelerate testing without compromising accuracy.

Boosting testing efficiency is crucial for any material innovation process. Here are some strategies that have proven effective: - **Automation**: Automate repetitive tasks in the testing process using advanced tools and technologies. This reduces manual effort, saves time, and improves accuracy. - **Parallel Testing**: Perform multiple tests simultaneously on different samples or materials to save time. - **Data-Driven Approach**: Leverage historical data and advanced analytics to predict outcomes and focus testing efforts on the most promising areas. - **Standardization**: Establish standard testing procedures to ensure consistency and reduce variability.