SIMPloy – Systematic Identification of Material Properties from Literature to Accelerate ML-enabled High-Entropy Alloys Discovery Workflows

Research question
Can material properties be explored and evaluated from a large corpus of unstructured literature in a systematic manner, using machine learning?

Sustainability aspects
The quest for novel high temperature structural materials is driven by the need to improve the energy efficiency of aerospace gas-turbine engines, by operating at higher temperatures. Apart from the aerospace industry, the novel high temperature alloys developed in this project have the potential to be used in marine vessels, automotive vehicles, trains or other engineering applications where the requirements for high-temperature mechanical properties are critical. Higher fuel efficiency is economically beneficial because of the fuel saving, and it contributes greatly to the reduction of CO2 emission from fossil fuel combustion in the short term, and is important for the longer term solutions of reducing CO2 emission by carbon capture and sequestration (CCS), in mitigating the energy penalty of the CCS technology. The proposed research has a huge application potential to improve energy efficiency (more sustainable energy system) of the combustion system and reduce CO2 emission (cleaner environment) for transportation and energy industries in Sweden.