Overview
From semiconductors in computer chips to plastics in everyday objects, materials are everywhere. Knowing how to synthesize and process them, as well as understanding their structure and properties, has helped to shape the world around us. Materials science contributes to the development of stronger, lighter materials that improve everything from battery electrodes to medical implants and from automobiles to spacecraft.
Broadly speaking, materials science and engineering research focuses on four major activities. The first is the study of the structure of materials to understand how they are composed and organized from atomic to macroscopic scales. The second involves verifying the properties of materials, such as their conductivity, strength, and elasticity. The third area covers analysis and benchmarking of how materials perform in specific situations. The final one involves assessing how materials can be fabricated and manufactured.
KEY DEVELOPMENTS
- Flexible electronics involves the creation of electrical devices that can bend, stretch, and deform without compromising their performance. These can be used as wearable, skin-like devices. For instance, a “smart bandage” with integrated sensors to monitor wound conditions and provide electrical stimulation can cut the time needed to heal chronic wounds by 25 percent.
- Additive manufacturing, colloquially known as 3-D printing, is one of the most promising advances in materials processing over the past fifteen years. The technology comes in different forms. For instance, a method known as continuous liquid interface production (CLIP) uses directed ultraviolet light to form structures from a polymer resin.
- Nanotechnology exploits the properties of nanoscale materials (i.e., with one or more dimensions of 1–100 nanometers) that differ from the same materials in bulk—including electronic, optical, magnetic, thermal, and mechanical properties.
- Quantum dots are spherical nanocrystals that emit light and are used in television displays. They are a model example of a material whose properties vary because of its scale—in dot form, their optoelectronic properties differ from those of the same material when found in bulk. They can be used in areas such as medical imaging, solar cells, chemical and biological detection sensors, and anticounterfeiting measures.
The fundamental challenge of materials science as a discipline is the vast number of possible materials and material combinations that are possible and the associated time and cost involved in their synthesis and characterization (which is the general process by which materials’ structure and properties are ascertained through spectroscopic, microscopic, and several other complementary methods).
Artificial intelligence (AI)—and, in particular, machine learning (ML)—offers promising solutions by leveraging experimental and computational data on the properties of materials. ML algorithms can recognize patterns in existing data and make generalized predictions about new materials. Their results provide a starting point for further exploration, but additional laboratory-derived data is needed to make ML-informed solutions more accurate, especially in the case of complex materials.
Another application of ML in materials science involves examining scientific literature for hidden relationships that could reveal latent knowledge about materials and point to new research directions. This approach has also been used to improve the design of electrolytes used in batteries and has been deployed in automated labs that can rapidly synthesize and characterize materials at scale.
