RESUMEN
Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific, technical, and social opportunities that the materials community must prioritize to consistently develop and leverage Scientific AI (SciAI) to provide a credible path towards the advancement of current materials-limited technologies. Here we highlight the intersections of these opportunities with a series of proposed paths forward. The opportunities are roughly sorted from scientific/technical (e.g. development of robust, physically meaningful multiscale material representations) to social (e.g. promoting an AI-ready workforce). The proposed paths forward range from developing new infrastructure and capabilities to deploying them in industry and academia. We provide a brief introduction to AI in materials science and engineering, followed by detailed discussions of each of the opportunities and paths forward.
RESUMEN
A gradient annealing cell has been developed for the high-throughput study of thermal annealing effects on thin-film libraries in different environments. The inexpensive gradient annealing unit permits temperature gradients as large as 28 °C∕mm and can accommodate samples ranging in length from 13 mm to 51 mm. The system was validated by investigating the effects of annealing temperature on the crystallinity, resistivity, and transparency of tin-doped indium oxide deposited on a glass substrate by magnetron sputtering. The unit developed in this work will permit the rapid optimization of materials properties such as crystallinity, homogeneity, and conductivity across a variety of applications.
RESUMEN
We have developed an infrared imaging setup enabling in situ infrared images to be acquired, and expanded on capabilities of an infrared imaging as a high-throughput screening technique, determination of a critical thickness of a Pd capping layer which significantly blocks infrared emission from below, enhancement of sensitivity to hydrogenation and dehydrogenation by normalizing raw infrared intensity of a Mg thin film to an inert reference, rapid and systematic screening of hydrogenation and dehydrogenation properties of a Mg-Ni composition spread covered by a thickness gradient Pd capping layer, and detection of formation of a Mg2Si phase in a Mg thin film on a thermally oxidized Si substrate during annealing.
RESUMEN
We are developing a procedure for the quick identification of structural phases in thin film composition spread experiments which map large fractions of compositional phase diagrams of ternary metallic alloy systems. An in-house scanning x-ray microdiffractometer is used to obtain x-ray spectra from 273 different compositions on a single composition spread library. A cluster analysis software is then used to sort the spectra into groups in order to rapidly discover the distribution of phases on the ternary diagram. The most representative pattern of each group is then compared to a database of known structures to identify known phases. Using this method, the arduous analysis and classification of hundreds of spectra is reduced to a much shorter analysis of only a few spectra.