Machine learning applications on lunar meteorite minerals:From classification to mechanical properties prediction

Amid the scarcity of lunar meteorites and the imperative to preserve their scientific value,nondestructive testing methods are essential.This translates into the application of microscale rock mechanics experiments and scanning electron microscopy for surface composition analysis.This study explores the application of Machine Learning algorithms in predicting the mineralogical and mechanical properties of DHOFAR 1084,JAH 838,and NWA 11444 lunar meteorites based solely on their atomic percentage compositions.Leveraging a prior-data fitted network model,we achieved near-perfect classification scores for meteorites,mineral groups,and individual minerals.The regressor models,notably the KNeighbor model,provided an outstanding estimate of the mechanical properties—previously measured by nanoindentation tests—such as hardness,reduced Young’s modulus,and elastic recovery.Further considerations on the nature and physical properties of the minerals forming these meteorites,including porosity,crystal orientation,or shock degree,are essential for refining predictions.Our findings underscore the potential of Machine Learning in enhancing mineral identification and mechanical property estimation in lunar exploration,which pave the way for new advancements and quick assessments in extraterrestrial mineral mining,processing,and research.

Optimizing magneto-ionic performance in structure/composition-engineered ternary nitrides

Magneto-ionics,an emerging approach to manipulate magnetism that relies on voltage-driven ion motion,holds the promise to boost energy efficiency in information technologies such as spintronic devices or future non-von Neumann computing architectures.For this purpose,stability,reversibility,endurance,and ion motion rates need to be synergistically optimized.Among various ions,nitrogen has demonstrated superior magneto-ionic performance compared to classical species such as oxygen or lithium.Here,we show that ternary Co_(1−x)Fe_(x)N compound exhibits an unprecedented nitrogen magneto-ionic response.Partial substitution of Co by Fe in binary CoN is shown to be favorable in terms of generated magnetization,cyclability and ion motion rates.Specifically,the Co_(0.3)5Fe_(0.65)N films exhibit an induced saturation magnetization of 1,500 emu/cm^(3),a magneto-ionic rate of 35.5 emu/(cm^(3)·s)and endurance exceeding 10^(3) cycles.These values significantly surpass those of other existing nitride and oxide systems.This improvement can be attributed to the larger saturation magnetization of Co_(0.35)Fe_(0.65) compared to individual Co and Fe,the nature and size of structural defects in as-grown films of different composition,and the dissimilar formation energies of Fe and Co with N in the various developed crystallographic structures.