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The integration of machine learning (ML) and deep learning (DL) into geoscience has experienced a pronounced uptick in recent years, a trend propelled by the intricate nature of geosystems and the abundance of data they produce. These computational methods have been harnessed across a spectrum of geoscientific challenges, from climate modeling to seismic analysis, exhibiting notable efficacy in extracting valuable insights from intricate geological datasets for applications such as mineral prediction. A thorough analysis of the literature indicates a marked escalation in AI-centric geoscience research starting in 2018, characterized by a predictive research orientation and a persistent focus on key computational terms. The thematic network and evolution analyses underscore the enduring prominence of “deep learning” and “machine learning” as pivotal themes, alongside progressive developments in “transfer learning” and “big data”. Despite these advancements, other methodologies have garnered comparatively lesser focus. While ML and DL have registered successes in the realm of mineral prediction, their amalgamation with domain-specific knowledge and symbolic reasoning could further amplify their interpretability and operational efficiency. Neuro-Symbolic AI (NSAI) emerges as a cutting-edge approach that synergizes DL’s robust capabilities with the precision of symbolic reasoning, facilitating the creation of models that are both powerful and interpretable. NSAI distinguishes itself by surmounting traditional ML constraints through the incorporation of expert insights and delivering explanatory power behind its predictive prowess, rendering it particularly advantageous for mineral prediction tasks. This literature review delves into the promising potential of NSAI, alongside ML and DL, within the geoscientific domain, spotlighting mineral prediction as a key area of focus. Despite the hurdles associated with infusing domain expertise into symbolic formats and mitigating biases inherent in symbolic reasoning, the application of NSAI in the realm of critical mineral prediction stands to catalyze a paradigm shift in the field. By bolstering prediction accuracy, enhancing decision-making processes, and fostering sustainable resource exploitation, NSAI holds the potential to significantly reshape geoscience’s future trajectory. 

Biomass burning (BB) is a major source of trace gases and particles in the atmosphere, influencing air quality, radiative balance, and climate. Previous studies have mainly focused on the BB emissions of carbon and nitrogen species with less attention on chlorine. Reactive chlorine chemistry has significant effects on atmospheric chemistry and air quality. However, quantitative information on chlorine emissions from BB, particularly the long-term trend and associated atmospheric impacts, is limited both on regional and global scales. Here, we report a long-term (2001–2018) high-resolution BB emission inventory for the major chlorine-containing compounds (HCl, chloride, and CH3Cl) in Asia based on satellite observations. We estimate an average of 730 Gg yr−1 chlorine emitted from BB activity in Asia, with China contributing the largest share at 24.2% (177 Gg yr−1), followed by Myanmar at 18.7% and India at 18.3%. Distinct seasonal patterns and significant spatial and interannual variability are observed, mainly driven by human-mediated changes in agricultural activity. By incorporating the newly developed chlorine emission inventory into a global chemistry-climate model (CAM-Chem), we find that the BB-chlorine emissions lead to elevated levels of HCl and CH3Cl (monthly average up to 2062 and 1421 parts per trillion by volume (pptv), respectively), subsequently resulting in noticeable changes in oxidants (up to 3.1% in O3 and 17% in OH radicals). The results demonstrate that BB is not only a significant source of air pollutants but also of oxidants, suggesting a larger role of BB emissions in the atmospheric chemistry and oxidation process than previously appreciated. In light of the projected increase in BB activity toward the end of the century and the extensive control of anthropogenic emissions worldwide, the contribution of BB emissions may become fundamental to air quality composition in the future.

 

Real-time coupled cluster (CC) methods have several advantages over their frequency-domain counterparts, namely, response and equation of motion CC theories. Broadband spectra, strong fields, and pulse manipulation allow for the simulation of complex spectroscopies that are unreachable using frequency-domain approaches. Due to the high-order polynomial scaling, the required numerical time propagation of the CC residual expressions is a computationally demanding process. This scaling may be reduced by local correlation schemes, which aim to reduce the size of the (virtual) orbital space by truncation according to user-defined parameters. We present the first application of local correlation to real-time CC. As in previous studies of locally correlated frequency-domain CC, traditional local correlation schemes are of limited utility for field-dependent properties; however, a perturbation-aware scheme proves promising. A detailed analysis of the amplitude dynamics suggests that the main challenge is a strong time dependence of the wave function sparsity.