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The high-pressure structure and stability of the calcic amphibole tremolite (Ca2Mg5Si8O22(OH)2) was investigated to ~40 GPa at 300 K by single-crystal X-ray diffraction using synchrotron radiation. C2/m symmetry tremolite displays a broader metastability range than previously studied clinoamphiboles, exhibiting no first-order phase transition up to 40 GPa. Axial parameter ratios a/b and a/c, in conjunction with finite strain versus normalized pressure trends, indicate that changes in compressional behavior occur at pressures of ~5 and ~20 GPa. An analysis of the finite strain trends, using third-order Birch-Murnaghan equations of state, resulted in bulk moduli (𝐾) of 72(7), 77(2), and 61(1) GPa for the compressional regimes from 0-5 GPa (regime I), 5-20 GPa (II), and above 20 GPa (III), respectively, and accompanying pressure-derivatives of the bulk moduli (𝐾′) of 8.6(42), 6.0(3), and 10.0(2). The results are consistent with first-principle theoretical calculations of tremolite elasticity. The axial compressibility ratios of tremolite, determined as 𝛽a : 𝛽b : 𝛽c = 2.22:1.0:0.78 (regime I), 2.12:1.0:0.96 (II), and 1.03:1.0:0.75 (III), demonstrate a substantial reduction of the compressional anisotropy of tremolite at high pressures, which is a notable contrast with the increasingly anisotropic compressibility observed in the high-pressure polymorphs of the clinoamphibole grunerite. The shift in compression-regime at 5 GPa (I-II) transition is ascribed to stiffening along the crystallographic a-axis corresponding to closure of the vacant A-site in the structure, and a shift in the topology of the a-oriented surfaces of the structural I-beam from concave to convex. The II-III regime shift at 20 GPa corresponds to an increasing rate of compaction of the Ca-polyhedra and increased distortion of the Mg-octahedral sites, processes which dictate compaction in both high-pressure compression-regimes. Bond-valence analyses of the tremolite structure under pressure show dramatic overbonding of the Ca-cations (75% at 30 GPa), with significant Mg-cation overbonding as well (40%). These imply that tremolite’s notable metastability range hinges on the calcium cation’s bonding environment. The 8-fold coordinated Ca-polyhedron accommodates significant compaction under pressure, while the geometry of the Ca-O polyhedron becomes increasingly regular and inhibits the reorientation of the tetrahedral chains that generate phase transitions observed in other clinoamphiboles. Peak/background ratio of diffraction data collected above 40 GPa and our equation of state determination of bulk moduli and compressibilities of tremolite in regime III, in concert with the results of our previous Raman study, suggest that C2/m tremolite may be approaching the limit of its metastability above 40 GPa. Our results have relevance for both the metastable compaction of tremolite during impact events, and for possible metastable persistence of tremolite within cold subduction zones within the Earth. 

NASA's Mars 2020 and ESA's ExoMars will collect Raman measurements in dusty field conditions obscuring underlying rocks. This presents a challenge for remote Raman measurements at distances where mechanical or ablative sample cleaning is not straightforward. Historically, probing broad lithostratigraphic suites has been thwarted by the need for pristine targets and high‐quality spectra. We provide a means of identifying Raman spectra of common rock‐forming silicate, carbonate, and sulfate minerals under low signal‐to‐noise‐ratios, Mars‐like conditions using a convolutional neural network (CNN). The CNN was trained on the Machine Learning Raman Open Data set data set with 500,000+ Raman spectra of hand samples/powder mixtures (5,000+ spectra/mineral class). Diversity in sample microtopography, orientation, and crystallinity simulated varying laser focuses and spectral quality, and no traditional spectral preprocessing such as cosmic ray or baseline removal was employed. The CNN identified low‐intensity Raman scatterers (micas and amphiboles), mixed minerals, and distinguished between mineral endmembers with +99% success. We present among the first known implementations of “big data” machine learning using varied, high‐volume Raman spectral datasets. The pattern recognition abilities of CNNs can facilitate scientist Raman spectral interpretation on Earth and autonomous rover decision‐making on planets like Mars; increasing scientific yield, correcting human classification errors, reducing the need for thorough target dust removal during evaluative measurements, and streamlining the data communications pipeline—saving time and resources. This study examines an end‐to‐end development process for creating a deep learning algorithm sensitive to varieties of Raman spectra and provides guidelines for CNN model development at the interface of Raman spectroscopy, deep learning, and planetary science.