In addition, artificial intelligence can be used to discover and propose novel materials from scratch. For example, by filtering which chemical compositions are likely to form compounds, we can develop systems that recommend element combinations from a pool of elements or present ionic substitutions in existing materials for the discovery of new compounds.
We could also use these models to predict the crystal structure of a new material, so that we have a more guided exploration of which molecules are likely to have the crystal structure that we desire. An example
from novel OLED systems shows that machine learning has been crucial to develop more optimized structures.
AI can be also be used for process optimization. For example, to optimize the processing of material manufacturing, such as metal smelting. There are several key steps in this pipeline that could be accelerated by the use of AI, such as automating the analysis of the ore concentration, the processing of the ore, and the separation of the metals.
For example, by developing a model that can predict what mechanical properties an alloy will have given its processing parameters, we can optimize the processing parameters for the optimal mechanical properties in this alloy, and thereby improve the smelting method.
Additionally, AI methods have also gained a lot of popularity for the quantum mechanical simulation of these systems. When we're designing materials at the molecular level, quantum mechanical properties are very important and we need to have an accurate quantum mechanical description of the system. This is given by the density functional of the system, and often, these are very tedious and large calculations that have to be approximated numerically. And they may fail for very complex systems. However, by applying machine learning
to the estimation of density functions, we can more quickly and more robustly predict the density functionals with higher accuracy.