# Abstract This paper presents a machine learning system aimed at predicting material properties of a wide range of diversified materials, such as steels, non-ferrous alloys and polymers. By using copious training sets provided from a very large database and proprietary methodology for taxonomy, data curation and normalization, the developed system is able to predict physical and mechanical properties for hundreds of thousands of materials, at various temperatures and various heat treatments and delivery conditions. The accuracy achieved in terms of relative error is in most cases above 90%, and frequently above 95%, thus being higher than readily available, statistically derived data sources such as MMPDS B-Basis values, which are routinely used in aerospace industry. --- # 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.totalmateria.com/predictor/predictor-2-whitepaper/abstract.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.