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Machine learning for data-driven discovery in solid Earth geoscience

Thu, 03/21/2019 - 17:45

Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. It is made challenging by the complex, interacting, and multiscale processes needed to understand Earth’s behavior and by the inaccessibility of nearly all of Earth’s subsurface to direct observation. Substantial increases in data availability and in the increasingly realistic character of computer simulations hold promise for accelerating progress, but developing a deeper understanding based on these capabilities is itself challenging. Machine learning will play a key role in this effort. We review the state of the field and make recommendations for how progress might be broadened and accelerated.

Comment on "Insulator-metal transition in dense fluid deuterium"

Thu, 03/21/2019 - 17:45

Celliers et al. (Reports, 17 August 2018, p. 677), in an attempt to reconcile differences in inferred metallization pressures, provide an alternative temperature analysis of the Knudson et al. experiments (Reports, 26 June 2015, p. 1455). We show that this reanalysis implies an anomalously low specific heat for the metallic fluid that is clearly inconsistent with first-principles calculations.

The future of science in film

Thu, 03/21/2019 - 17:45

News at a glance

Thu, 03/21/2019 - 17:45

Humans may sense Earth's magnetic field

Thu, 03/21/2019 - 17:45

Tests identify HIV's final redoubt

Thu, 03/21/2019 - 17:45

The reef builders

Thu, 03/21/2019 - 17:45

Science at Sundance

Thu, 03/21/2019 - 17:45

Volcanic threats to global society

Thu, 03/21/2019 - 17:45

Evolving resistance to pathogens

Thu, 03/21/2019 - 17:45

Sodium channels caught in the act

Thu, 03/21/2019 - 17:45

Dampening oncogenic RAS signaling

Thu, 03/21/2019 - 17:45

Making more from methane

Thu, 03/21/2019 - 17:45

A treasure trove of Cambrian fossils

Thu, 03/21/2019 - 17:45

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