Nanomaterials combine the complexities of molecules and materials. However, dealing with the challenges of low manufacturing yields and defects of individual samples have been secondary to searching for new properties and applications of nanomaterials. For science to become technology, machine learning will need to play a big role in navigating the large amount of data associated with these complexities. Applying data-driven methods will be crucial in the discovery and development of new nanomaterials that can improve the efficiency of many processes that span multiple industries. In this post, we’ll give a comprehensive overview of nanoinformatics and show how it’s used in industry.
What is Nanoinformatics?
Nanoinformatics is the use of informatics techniques to collect, process, and store information about the characteristics of nanoparticles, their environments, and applications. Traditional analysis looks at data sources individually, but informatics effectively brings multiple data streams together. This emerging field working at the intersection of informatics, nanotechnology, and traditional science develops tools to process nanoparticle data more efficiently.
Why is Nanoinformatics Important?
Automation has made it possible to synthesize and characterize materials faster, leading to the growth of both new materials and massive data sets. Nanomaterial data sets typically suffer from the same data challenges as other domains like variability, bias, and sparsity. However, these problems are further exacerbated by the greater complexity of the information collected. As a result, computational methods for analyzing these large, multidimensional data sets are rising in significance, sophistication, and utility.
Examples of Nanoinformatics in Action
Current informatics technology is typically focused on collecting, representing, and linking information together, but this isn’t enough to obtain greater insights into the complexities of environmental and multi-gene interactions in human diseases. Through using nanoinformatics, the growing amount of -omics data can be translated into personalized medical diagnoses and therapies for patients. For example, nanoinformatics can contribute to a new wave of nanodevices like nano-based screening tests and bio-sensing systems that are designed to diagnose and treat children with cancer and genetic diseases.
Researchers are also using nanoinformatics to develop models for classifying the ecotoxicity of nanomaterials. Through building a single model for multiple species and individual species-specific models, machine learning can generate across-species and species-specific models to predict toxicity with significant predictive power. The models can also provide insight into which nanoparticle compositions and descriptors are creating the most adverse biological effects on the environment.
More inventive uses of machine learning and statistical modeling will develop as nanoinformatics becomes more advanced. By effectively incorporating data science into nanotechnology, the discovery of nanomaterials will accelerate through more efficient data analysis and characterization. Therefore, nanoinformatics will be essential to solving a variety of problems across all industries.
Contact us today to see how AtomicAI can help your company optimize your nanomaterial production using data science.