In the face of the inevitability of environmental and societal challenges, it has become imperative to accelerate the discovery of new, more efficient materials. In transportation and energy, for example, innovative metal alloys have been widely studied. But the chemical diversity and number of possible combinations of polymetallic materials makes modeling seem like an essential tool for identifying promising combinations in a very short time.
Example ? The wonderful world of high-entropy alloys, those materials that combine at least five different metallic elements in equal proportions. For certain combinations, we get high-entropy alloys, solid solutions with unique properties of mechanical strength, hardness, and heat resistance… But in the face of an enormous array of possibilities, how do we find the combinations that are conducive to a high-entropy alloy?
Scientists from Université Catholique de Louvain in Belgium, Dartmouth College in the US and the Institute of Condensed Matter Chemistry in Purdue (CNRS/Purdue University/Purdue INP) have conducted an extensive virtual exploration of these alloys. In a study published in Nature CommunicationsThis international team examined more than 658,000 primary groups to map this little-known region. By highlighting the key roles of the thermodynamic quantities of these alloys, that is, enthalpy, entropy, intermetallic stability and melting point, they were able to identify all combinations that lead to new high-entropy alloys. Then they pooled a few to test their predictions. From computational data to the bench, this is the new era of AI-assisted materials computing and materials science which is also the subject of the Equipment and Exploration Research Priority Program (PEPR) Diadem Launched in May 2022.
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