# ML & AI applications *These pages are under active development as we improve the quality of our documentation on ML applications and ML-dataset accessibility.* ### Motivation Predicting the properties of materials, such [as those computed by the Materials Project](https://docs.materialsproject.org/methodology/total-energies/) and [its collaborators](https://next-gen.materialsproject.org/contribs), can be an expensive and time-consuming undertaking. Synthesizing, characterizing, and experimentally validating novel materials in the physical world is vastly more expensive. New techniques based on ML are a promising avenue for reducing the time and cost of materials development by multiple orders of magnitude. These include direct prediction of physical properties from structural or chemical composition information, and direct prediction of the materials potential energy surface by "foundation potentials" (FPs), AKA universal machine learning interatomic potentials (MLIPs). Using information such as crystal structure, chemical composition, and electronic bandstructures as inputs, ML models can be trained to predict otherwise expensive or computationally prohibitive properties. More, ML can help scientists generate novel ideas for highly complex tasks like solid-state synthesis and crystal structure determination with heretofore unobtainable physical insights. In practice, this means scientists can screen enormous chemical and configurational search spaces in the pursuit of stable, cheap, and highly performant materials. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.materialsproject.org/services/ml-and-ai-applications.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.