Sulfur species and gold transport in arc magmatic fluids.

The sulfur species present in magmatic fluids affect the global redox cycle, the Earth's climate and the formation of some of the largest and most economic ore deposits of critical metals. However, the speciation of sulfur under conditions that are relevant for upper crustal magma reservoirs is unclear. Here we combine a prototype pressure vessel apparatus and Raman spectroscopy to determine sulfur speciation in arc magmatic fluid analogues in situ over a range of geologically relevant pressure-temperature-redox conditions.

Sulfur and chlorine budgets control the ore fertility of arc magmas.

Continental arc magmas supply the ore-forming element budget of most globally important porphyry-type ore deposits. However, the processes enabling certain arc segments to preferentially generate giant porphyry deposits remain highly debated. Here we evaluate the large-scale covariation of key ore-forming constituents in this setting by studying silicate melt inclusions in volcanic rocks from a fertile-to-barren segment of the Andean Southern Volcanic Zone (33-40 °S).

The fall, recovery, classification, and initial characterization of the Hamburg, Michigan H4 chondrite.

The Hamburg meteorite fell on January 16, 2018, near Hamburg, Michigan, after a fireball event widely observed in the U.S. Midwest and in Ontario, Canada.

Multiple Self-Trapped Emissions in the Lead-Free Halide CsCuI.

Low-dimensional copper halides with high luminance have attracted increasing interest as heavy-metal-free light emitters. However, the optical mechanisms underpinning their excellent luminescence remain underexplored. Here, we report multiple self-trapped emissions in CsCuI.

Learning-in-Templates Enables Accelerated Discovery and Synthesis of New Stable Double Perovskites.

In the past three years, machine learning (ML) in combination with density functional theory (DFT) has enabled computational screening of compounds with the goal of accelerated materials discovery. Unfortunately, DFT+ML has, until now, either relied on knowledge of the atomic positions at DFT energy minima, which are a priori unknown, or been limited to chemical spaces of modest size. Here we report a strategy that we term learning-in-templates (LiT), wherein we first define a series of space group and stoichiometry templates corresponding to hypothesized compounds and, orthogonally, we allow any list of atoms to take on any template.