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Accurate prediction of the solidified microstructure in laser powder bed fusion (LPBF)-processed components is critical, because, heterogeneity and spatial anisotropy in the solidified microstructure lead to variation in functional properties. This paper presents a physics-aware and real-time data-integrated machine learning approach for predicting the solidified microstructure of LPBF-processed Inconel 718 parts. Currently, the prediction of solidified microstructure in LPBF is realized primarily through three approaches: (i) empirical modeling; (ii) blackbox modeling using in-situ sensor data; and (iii) whitebox modeling leveraging physics-based process simulations. Each of these approaches has inherent limitations. Empirical and blackbox models lack generalizability because they do not account for geometry-dependent causal thermal-fluid phenomena governing microstructural evolution. Physics-based whitebox models are computationally demanding and overlook the stochasticity inherent to the process. To overcome the foregoing limitations, this work establishes a graybox modeling approach. This approach combines temperature fields predicted by a physics-based thermal model with real-time data acquired from in situ infrared thermal imaging and optical tomography sensors. The graybox model is trained to predict the following microstructural aspects of LPBF-processed Inconel 718 parts: melt pool depth; primary dendritic arm spacing; crystallographic texture, orientation, and grain aspect ratio; and microhardness. The graybox model predicted the solidified microstructure with an accuracy approaching 95% (R²). By contrast, the prediction accuracy of blackbox and physics-based whitebox models ranged between R² ∼ 60% and 85%. Thus, this work takes a critical step towards a rapid, accurate, in-situ, and non-destructive Born Qualified assessment of LPBF part quality. 

Modeling and control of the spatiotemporal temperature distribution (thermal history) in laser powder bed fusion (LPBF) is critical because the thermal history governs defects, such as porosity, poor surface finish, cracking, and deformation. This article presents a coupled physics-based computational modeling and feedforward process control framework for regulating the thermal history in LPBF-processed parts. Existing LPBF process optimization relies on empirical parameter tuning by manufacturing and testing of simple, standardized coupon geometries. Empirical coupon-based optimization inherently disregards the geometry-dependent effect of thermal history on defect formation. Consequently, process parameters optimized based on coupon studies, when used for manufacturing real-world components, often result in build failures and defects. To address this limitation, a rapid graph theory-based computational model was coupled to a feedforward control (FFC) algorithm. The approach is implemented for manufacturing a topology-optimized Inconel 718 aerospace component (GE bracket). The model-guided FFC approach maintains a constant end-of-cycle (interpass or interlayer) temperature across layers by adjusting the laser power and velocity. The processing parameters are adjusted in silico—offline and prior to manufacturing—within the thermal model. Compared to its empirically optimized counterpart, the FFC-processed GE bracket exhibited three characteristics favorable to functional integrity and production: (i) meltpool instability-induced porosity was not observed; (ii) thermal-induced deformation, dross formation, and recoater contact damage were significantly mitigated; and (iii) FFC-induced improvements enabled the part to be manufactured with 45% less support mass, resulting in a 20% reduction in the as-built part weight (with the part design unchanged). This work thus underscores the potential of physics-based control, as opposed to empirical optimization, to mitigate defects in LPBF parts and accelerate their practical deployment. 

We address the problem of predicting high-detail RNA structure geometry from the information available in low-detail experimental maps. Here, low-detail refers to resolutions ≈ 2.5-3.5Å, where the location of the phosphate groups and the glycosidic bonds can be determined from experimental maps but all other backbone atom positions cannot. In contrast, higher-resolution maps allow high-detail determinations of all backbone atomic positions. To this end, we first create a gold standard dataset of highly curated, experimentally supported RNA suites. Second, we develop and employ a modified version of the previously devised algorithm MINT-AGE to learn clusters that are in high correspondence with the gold standard’s conformational classes of suites based on 3D RNA structure. Since some of the gold standard classes are of very small size, a new modified version of MINT-AGE is able to also identify very small clusters. Third, we create a new conformer prediction algorithm, RNAprecis, which assigns low-detail structures to newly designed 3D shape coordinates. Our improvements include: (i) learned classes augmented to cover also very low sample sizes and (ii) replacing distances from clusters by Bayesian posterior probabilities. On test data containing suites modeled as conformational outliers, RNAprecis shows good results suggesting that our learning method generalizes well. In particular, we show that the modified MINT-AGE clustering can more finely delineate between previously unseen suite conformer separations. For example, the0aconformer has been separated into two clusters seen in different structural contexts. Such new distinctions can have implications for biochemical interpretation of RNA structure. 

Abstract No-till management is often recognized for its environmental and economic benefits, but its potential to reduce climate warming is still uncertain. Beyond ongoing debate over its effects on soil carbon storage, no-till also leaves plant residue on the surface, which can reflect more sunlight. This increase in surface reflectivity, called albedo, may help mitigate climate change by reducing the energy absorbed by the land. Here, we assessed this climate benefit of no-till across the U.S. Corn Belt using conservation survey records, county-level tillage data, and satellite observations. We found that no-till increased land surface brightness during the dormant season, reducing absorbed solar energy by an estimated 50 grams of CO₂equivalent per square meter per year. Regionally, this could add up to 24 teragrams of CO₂equivalent per year in potential climate benefits. Areas with low adoption, especially those with dark, carbon-rich soils, offer the greatest opportunity for further mitigation. 

Abstract Reforestation is a prominent climate change mitigation strategy, but available global maps of reforestation potential are widely criticized and highly variable, which limits their ability to provide robust estimates of both the locations and total area of opportunity. Here we develop global maps that address common critiques, build on a review of 89 reforestation maps created at multiple scales, and present eight reforestation scenarios with varying objectives, including providing ecosystem services, minimizing social conflicts, and delivering government policies. Across scenarios, we find up to 195 Mha (million hectares) are available (2225 TgCO₂e (teragrams of carbon dioxide equivalent) per year total net mitigation potential), which is 71–92% smaller than previous estimates because of conservative modeling choices, incorporation of safeguards, and use of recent, high-resolution datasets. This area drops as low as 6 Mha (53 TgCO₂e per year total net mitigation potential) if only statutorily protected areas are targeted. Few locations simultaneously achieve multiple objectives, suggesting that a mix of lands and restoration motivations will be needed to capitalize on the many potential benefits of reforestation. 

Abstract Additive manufacturing (AM) of aerogels increases the achievable geometric complexity, and affords fabrication of hierarchically porous structures. In this work, a custom heated material extrusion (MEX) device prints aerogels of poly(phenylene sulfide) (PPS), an engineering thermoplastic, via in situ thermally induced phase separation (TIPS). First, pre‐prepared solid gel inks are dissolved at high temperatures in the heated extruder barrel to form a homogeneous polymer solution. Solutions are then extruded onto a room‐temperature substrate, where printed roads maintain their bead shape and rapidly solidify via TIPS, thus enabling layer‐wise MEX AM. Printed gels are converted to aerogels via postprocessing solvent exchange and freeze‐drying. This work explores the effect of ink composition on printed aerogel morphology and thermomechanical properties. Scanning electron microscopy micrographs reveal complex hierarchical microstructures that are compositionally dependent. Printed aerogels demonstrate tailorable porosities (50.0–74.8%) and densities (0.345–0.684 g cm⁻³), which align well with cast aerogel analogs. Differential scanning calorimetry thermograms indicate printed aerogels are highly crystalline (≈43%), suggesting that printing does not inhibit the solidification process occurring during TIPS (polymer crystallization). Uniaxial compression testing reveals that compositionally dependent microstructure governs aerogel mechanical behavior, with compressive moduli ranging from 33.0 to 106.5 MPa.