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BLASTNet: A call for community-involved big data in combustion machine learning
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Mapping mineral prospectivity through big data analytics and a deep learning algorithm
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Discovering unusual structures from exception using big data and machine learning techniques
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Pycroscopy - An Open Source Approach to Microscopy and Microanalysis in the Age of Big Data and Open Science
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Big Data Analysis and Technical Review of Regeneration for Carbon Capture Processes
Carbon capture remains an integral technology to mitigate pollution from one of the most prevalent greenhouse gases. CO 2 desorption/absorbent regeneration for both solid- and liquid-based systems is widely recognized as an energy-intensive and costly process operation. Consequently, tremendous work was devoted towards developing new absorbents and regeneration processes to promote their economic feasibility for extensive implementation. In this review, we broadly and deeply review more than 10,000 papers and extract the hidden trends of carbon capture and absorbents regeneration in the past few decades, using a novel data-mining analysis technique. We comprehensively analyzed an array of recent absorbent regeneration methods utilized in post-combustion, pre-combustion, carbon capture from industrial point sources, and direct air carbon capture, with an emphasis on sorbent and solvent-based techniques. In conclusion, advanced regeneration methods in these techniques were illustrated and discussed, followed by recommendations for further research efforts.
Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis
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From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
Abstract The behaviors and skills of models in many geosciences (e.g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration. A well-calibrated model can reasonably propagate information from observations to unobserved variables via model physics, but traditional calibration is highly inefficient and results in non-unique solutions. Here we propose a novel differentiable parameter learning (dPL) framework that efficiently learns a global mapping between inputs (and optionally responses) and parameters. Crucially, dPL exhibits beneficial scaling curves not previously demonstrated to geoscientists: as training data increases, dPL achieves better performance, more physical coherence, and better generalizability (across space and uncalibrated variables), all with orders-of-magnitude lower computational cost. We demonstrate examples that learned from soil moisture and streamflow, where dPL drastically outperformed existing evolutionary and regionalization methods, or required only ~12.5% of the training data to achieve similar performance. The generic scheme promotes the integration of deep learning and process-based models, without mandating reimplementation.
Enabling pan-repository reanalysis for big data science of public metabolomics data
Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.
Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures
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Flat-histogram extrapolation as a useful tool in the age of big data
In this work, we review recent work by the authors to revisit the concept of extrapolating thermodynamic properties of classical systems using statistical mechanical principles. Specifically, we discuss how the combination of these principles with biased sampling techniques enables the prediction of free energy landscapes and other detailed information, such as structural properties, of the system in question. Remarkably accurate estimates of physical properties across a broad range of conditions have been achieved using this approach, greatly reducing the number of simulations needed to explore a given system's behaviour. While approximate, these extrapolations significantly amplify the amount of reasonably accurate information that can be extracted from simulations enabling a small set of them to feed data-intensive regression algorithms such as neural networks. Thus, this extrapolation methodology represents a useful tool for performing tasks such as high-throughput screening of physical properties, optimising force field parameters, exploring equilibrium phase behaviour, and enabling theory-guided data science for these systems.
Toward High-Performance Computing and Big Data Analytics Convergence: The Case of Spark-DIY
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