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DOE OSTI2024

Enhancing predictive understanding and accuracy in geological carbon dioxide storage monitoring: Simulation and history matching of tracer transport dynamics

Co-injection of conservative tracers with carbon dioxide (CO 2 ) is a viable tool for monitoring subsurface processes during geological CO 2 storage (GCS). This research investigates the simulation and history-matching of a gas tracer (sulfur hexafluoride, SF 6 ) during CO 2 flooding, employing a core flooding result in Berea sandstone. Four extensively used saturation functions are assessed for their efficacy in history matching of CO 2 /SF 6 injection at the core scale. The history-matching process incorporates particle swarm optimization (PSO) to fine-tune constitutive relationships parameters. Next, employing transport models at the aquifer scale, we interrogate the impact on tracer transport and mixing of saturation function uncertainties, arising from the non-uniqueness of constitutive relationships parameters and saturation function types. To assess the effects of geological heterogeneity on behavior of tracer breakthrough curves (BTCs), we employ two normalized parameters assessing the degree of mixing and SF 6 breakthrough time. The aquifer-scale investigation encompasses both homogeneous and heterogeneous systems with and without capillary heterogeneity effects. Our findings underscore the critical importance of addressing saturation function uncertainties, emphasizing the significance of auxiliary experiments and innovative methodologies to enhance predictive accuracy. The findings highlight significant disparities in arrival times, BTC peaks, tails, and mixing levels, even under optimal conditions. Heterogeneity, with or without capillary heterogeneity, plays a crucial role in shaping BTC variations, resulting in accelerated SF 6 breakthrough times and reduced BTC peaks. Evaluation of monitoring points distant from the injector reveals a dampening effect on the SF6 BTC peak, particularly in heterogeneous systems with capillary heterogeneity, where the peak is halved. These insights underscore the challenges associated with tracer monitoring and the necessity for enhanced methodologies to improve predictive accuracy in subsurface processes during GCS.

58 GEOSCIENCES↗

DOE OSTI2023

Effect of water droplet growth dynamics on electrode current in fuel-cell catalyst layers

Fuel cells are a promising next-generation energy-conversion technology designed to replace internal combustion engines in transportation applications. However, much work remains to optimize them. Operation at high humidities causes liquid water droplet formation on Pt catalyst particles during oxygen reduction, potentially impeding reactant arrival to the reactive electrode. In this work, four different cases of water droplet growth in fuel-cell catalyst layers are considered: pinned or advancing droplets on a bare Pt surface, advancing droplets on a Nafion film, and water-layer growth in carbon nanopores. Transient drop growth is captured with a combination of mass, species mass, and momentum balances, and the subsequent limiting current is determined via oxygen diffusion and Tafel kinetics. Further, water droplets are found not to be mass-transfer limiting due to the relatively large liquid-gas area compared to the Pt nanoparticle. Mass-transfer-limited behavior is calculated in carbon nanopores.

25 ENERGY STORAGE↗

DOE OSTI2022

In silico design of microporous polymers for chemical separations and storage

Polymers of intrinsic microporosity (PIMs) are a family of materials with potential to be effective and scalable solutions for challenging adsorbent and membrane applications. The broad range of repeat unit chemistry, microporous structural features, and polymer processing makes exploration of the expansive PIM design space inefficient via chemical and materials intuition alone. Computational techniques such as molecular simulations and machine learning can provide a leap in capabilities to address this polymer design challenge and will be central to the future development of PIMs. In this work, we highlight recent microporous material studies that arrived at key results by employing computational techniques and provide our perspective on the prospects for in silico design and development of PIMs.

adsorption↗

DOE OSTI2023

Effect of a micro-scale dislocation pileup on the atomic-scale multi-variant phase transformation and twinning

In this paper, we perform concurrent atomistic–continuum (CAC) simulations to assess the contribution of the internal stress induced by the microscale dislocation pileup at an atomically structured interface to the atomic-scale phase transformations (PTs), reverse PTs, and twinning. The main novelty of this work is to unify the atomistic description of the interface and the coarse-grained (CG) description of the lagging dislocations away from the interface within one single framework. Our major findings are: (a) the interface dynamically responds to a pileup by forming steps/ledges, the height of which is proportional to the number of dislocations arriving at the interface; (b) the pileup-induced internal stress concentration profile follows neither the classical Eshelby model nor the super-dislocation model alone, but a combination of them; (c) when the pre-sheared sample is compressed, a direct square-to-hexagonal PT occurs ahead of the pileup tip and eventually grows into a wedge shape. The two variants of the hexagonal phases form a twin with respect to each other; (d) upon a further increase of the loading, part of the newly formed hexagonal phase transforms back to the square phase. The square product phase resulting from this reverse PT forms a twin with respect to the initial square phase. All phase boundaries (PBs) and twin boundaries (TBs) are stationary and correspond to zero thermodynamic Eshelby driving forces; and (e) the microscale dislocation pileup-induced internal shear stress and the structural change at the atomic-scale interface reduces the stress required for initiating a PT by a factor of 5.5, comparing with that in the sample containing no dislocations. This work is the first characterization of the behavior of PTs/twinning resulting from the reaction between a microscale dislocation slip and an atomically structured interface. In conclusion, the gained knowledge will advance our understanding of how the multi-phase material behaves in many complex physical processes, such as the synthesis of multi-phase high-entropy alloys or superhard ceramics under high-pressure torsion, deep mantle earthquakes in geophysics, and so on, which all involve dislocation slip, PTs, twinning, and their interactions across from the atomistic to the microscale and beyond.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Using bursty announcements for detecting BGP routing anomalies

Despite the robust structure of the Internet, it is still susceptible to disruptive routing updates that prevent network traffic from reaching its destination. Our research shows that BGP announcements that are associated with disruptive updates tend to occur in groups of relatively high frequency, followed by periods of infrequent activity. We hypothesize that we may use these bursty characteristics to detect anomalous routing incidents. In this work, we use manually verified ground truth metadata and volume of announcements as a baseline measure, and propose a burstiness measure that detects prior anomalous incidents with high recall and better precision than the volume baseline. We quantify the burstiness of inter-arrival times around the date and times of four large-scale incidents: the Indosat hijacking event in April 2014, the Telecom Malaysia leak in June 2015, the Bharti Airtel Ltd. hijack in November 2015, and the MainOne leak in November 2018; and three smaller scale incidents that led to traffic interception: the Belarusian traffic direction in February 2013, the Icelandic traffic direction in July 2013, and the Russian telecom that hijacked financial services in April 2017. Our method leverages the burstiness of disruptive update messages to detect these incidents. We describe limitations, open challenges, and how this method can be used for routing anomaly detection.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

TAUOLA update for decay channels with e + e – pairs in the final state

With the arrival of high luminosity B-factories like the Belle II experiment, τ decay measurements have become more precise than ever, allowing rarer processes to be explored, and finer details of τ decays to be studied. These are important to understand the spectrum of intermediate particles produced in τ decays. Therefore Monte Carlo generators, like the TAUOLA program, have to facilitate precision analysis as well as confront new models that constantly emerge with the availability of high statistics experimental data. New decay channels and models may lead to large variation of matrix elements size within the available phase space, as in the case when e + e - pairs are present in final state. It requires appropriate presampler of the phase space generator and a proper documentation to help users introduce their own models. While releasing a new update, it is important for the TAUOLA Monte Carlo library to maintain the general structure of the previous versions to preserve backward compatibility. The aim is to minimize changes from the user perspective. Finally, this paper presents a demonstration of new models implementation facilitated by the current update.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Modeling the effect of habitat availability and quality on endangered winter-run Chinook salmon ( Oncorhynchus tshawytscha ) production in the Sacramento Valley

Reconciliation between water uses and sustaining aquatic species populations requires an effort to identify and quantify essential habitat characteristics for ecosystem health and valuation methods to predict ecosystem response to restoration actions. This process is particularly challenging for anadromous fish species such as California's Sacramento River winter-run Chinook salmon, due to their limited geographic range and diverse life history habitat requirements. Tools, such as life-cycle models, are needed to manage population dynamics and quantify the composite effects of processes across space and time. Nevertheless, complex institutions can hinder result interpretation and communication, and limit model use in decision-making. This paper focuses on the federally endangered and endemic Sacramento River winter-run Chinook (Oncorhynchus tshawytscha) by developing a Winter-Run Habitat-based Population Model (WRHAP). Additionally, WRHAP is a conceptual, freshwater rearing stage model that includes alternative rearing habitats reported in the literature (e.g., floodplains, off-channel and tributaries), defines rules of habitat use based on instream conditions, and incorporates a juvenile growth module that combines bioenergetics modeling with empirical growth rates. Model outputs reasonably follow observed out-migration patterns and provide a realistic smolt size distribution arriving at the San Francisco Bay. This effort demonstrates the importance of currently “non-critical” habitats (as defined by the Endangered Species Act) for juvenile development, with floodplain habitat contributing to a quarter of out-migrating biomass (despite < 18% availability and <10% of total rearing days), and off-channel growth being one of the most sensitive parameters (explaining ~13% of average juvenile weight variance). The model shows the utility of a simple population model to explore relationships between habitat quality/quantity and juvenile development, and to assist water/environmental management and decision-making processes focused on species recovery.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Landslide monitoring using seismic refraction tomography – The importance of incorporating topographic variations

Seismic refraction tomography provides images of the elastic properties of subsurface materials in landslide settings. Seismic velocities are sensitive to changes in moisture content, which is a triggering factor in the initiation of many landslides. However, the application of the method to long-term monitoring of landslides is rarely used, given the challenges in undertaking repeat surveys and in handling and minimizing the errors arising from processing time-lapse surveys. This work presents a simple method and workflow for producing a reliable time-series of inverted seismic velocity models. This method is tested using data acquired during a recent, novel, long-term seismic refraction monitoring campaign at an active landslide in the UK. Potential sources of error include those arising from inaccurate and inconsistent determination of first-arrival times, inaccurate receiver positioning, and selection of inappropriate inversion starting models. At our site, a comparative analysis of variations in seismic velocity to real-world variations in topography over time shows that topographic error alone can account for changes in seismic velocity of greater than ±10% in a significant proportion (23%) of the data acquired. The seismic velocity variations arising from real material property changes at the near-surface of the landslide, linked to other sources of environmental data, are demonstrated to be of a similar magnitude. Over the monitoring period we observe subtle variations in the bulk seismic velocity of the sliding layer that are demonstrably related to variations in moisture content. This highlights the need to incorporate accurate topographic information for each time-step in the monitoring time-series. The goal of the proposed workflow is to minimize the sources of potential errors, and to preserve the changes observed by real variations in the subsurface. Following the workflow produces spatially comparable, time-lapse velocity cross-sections formulated from disparate, discretely-acquired datasets. These practical steps aim to aid the use of the seismic refraction tomography method for the long-term monitoring of landslides prone to hydrological destabilization.

Whiteley, J. S.↗

DOE OSTI2024

Efficient distributed continual learning for steering experiments in real-time

Deep learning has emerged as a powerful method for extracting valuable information from large volumes of data. However, when new training data arrives continuously (i.e., is not fully available from the beginning), incremental training suffers from catastrophic forgetting (i.e., new patterns are reinforced at the expense of previously acquired knowledge). Training from scratch each time new training data becomes available would result in extremely long training times and massive data accumulation. Rehearsal-based continual learning has shown promise for addressing the catastrophic forgetting challenge, but research to date has not addressed performance and scalability. To fill this gap, we propose an approach based on a distributed rehearsal buffer that efficiently complements data-parallel training on multiple GPUs to achieve high accuracy, short runtime, and scalability. It leverages a set of buffers (local to each GPU) and uses several asynchronous techniques for updating these local buffers in an embarrassingly parallel fashion, all while handling the communication overheads necessary to augment input minibatches using unbiased, global sampling. We further propose a generalization of rehearsal buffers to support both classification and generative learning tasks, as well as more advanced rehearsal strategies (notably Dark Experience Replay, leveraging knowledge distillation). We illustrate this approach with a real-life HPC streaming application from the domain of ptychographic image reconstruction. Furthermore, we run extensive experiments on up to 128 GPUs of the ThetaGPU supercomputer to compare our approach with baselines representative of training-from-scratch (the upper bound in terms of accuracy) and incremental training (the lower bound). Results show that rehearsal-based continual learning achieves a top-5 validation accuracy close to the upper bound, while simultaneously exhibiting a runtime close to the lower bound.

Asynchronous data management↗

DOE OSTI2024

Monitoring spatiotemporal evolution of fractures during hydraulic stimulations at the first EGS collab testbed using anisotropic elastic-waveform inversion

The EGS Collab project acquired continuous active-source seismic monitoring (CASSM) data before, during, and after hydraulic stimulations at the first testbed at the depth of 4850 ft (1478 m) at the Sanford Underground Research Facility in Lead, South Dakota, for monitoring fracture creation and evolution. CASSM acquisition was conducted using 24 hydrophones, 18 accelerometers, and 17 piezoelectric sources within four fracture-parallel wells and two orthogonal wells. 3D anisotropic traveltime tomography and anisotropic elastic-waveform inversion of the campaign cross-borehole seismic data show that the rock within the stimulation region is a heterogeneous horizontal transverse isotropic medium. Here we use these inversion results as the initial models and apply 3D anisotropic first-arrival traveltime tomography and 3D anisotropic elastic-waveform inversion to the CASSM data acquired after each stimulation in May, 2018 and December, 2018. We observe the spatiotemporal evolution of seismic velocities and anisotropic parameters caused by hydraulic fracture stimulations, showing the regions of rock alternation caused by hydraulic fracture stimulation.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

3D seismic imaging of a fracture damage zone controlling reservoir compartmentalization at the Raft River EGS using multi-azimuth walkaway VSP

Accurate imaging of steeply dipping fracture zones in crystalline enhanced geothermal systems (EGS) is critical for constraining permeability architecture and guiding stimulation design. However, such structures remain poorly resolved by conventional surface seismic methods. We present a fully three-dimensional (3D) elastic-waveform inversion-migration workflow applied to multi-azimuth walkaway vertical seismic profiling (VSP) data acquired in a deviated borehole at the Raft River EGS. The workflow integrates first-arrival traveltime tomography, multi-scale elastic waveform inversion (EWI), and elastic least-squares reverse-time migration (ELSRTM) to recover high-resolution compressional-and shear-wave velocity models and to image structural discontinuities in the crystalline basement. The results reveal a laterally continuous low-velocity anomaly, with shear-wave velocity reductions of 25-30%, consistent with fractureinduced mechanical weakening. Two steeply dipping discontinuities bound a 50-80 m wide fracture damage zone. Independent constraints from microseismic clustering and geochemical compartmentalization corroborate the geometry and structural significance of this feature. Synthetic modeling further confirms that structures of this scale are resolvable (~30 m). These findings indicate that the Narrows structure is a distributed fracture damage zone rather than a discrete fault plane. By resolving fracture-zone geometry at the tens-of-meters scale using a single borehole, this workflow provides a practical and transferable approach for improving structural characterization, reducing uncertainty in permeability architecture, and supporting reservoir modeling and stimulation design in fractured crystalline EGS reservoirs.

58 GEOSCIENCES↗

DOE OSTI2021

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement. Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Detecting an underground tunnel by applying joint traveltime and waveform inversion

Underground tunnel and void detection is a challenging geophysical problem, and many methods have been proposed. Seismic techniques are promising because of the large seismic velocity contrasts between the air-filled void and the surrounding sediment and concrete. We apply a joint seismic traveltime and waveform inversion method to image a buried tunnel with concrete walls and a void space inside. The joint inversion images the top of the concrete tunnel as a high-velocity anomaly and the void space as a low-velocity anomaly. The location of the velocity anomalies predicted by the method agrees with the known location of the tunnel. As a comparison, the stand-alone full waveform inversion is also applied to the data. The first-arrival traveltime tomography shows weak nonlinearity but fails to image the hidden low-velocity layer. Full waveform inversion is able to image complex near-surface structures, but fitting waveforms may not honor the traveltime fit, especially when the data contain noise. Synthetic and real data tests show that the joint inversion method retains the advantages of both traveltime inversion and full waveform inversion and overcomes their respective drawbacks at the same time. The field example shows the joint inversion provides a better reconstruction of the high-velocity feature representing the top of the concrete wall in terms of its magnitude and location.

58 GEOSCIENCES↗

DOE OSTI2021

Key Elements of Auroral Substorm Development and their Relationship to Recent Observations of Detached Sub-Auroral Phenomena Including STEVE-like Emissions

Since Akasofu introduced the concept of the substorm in 1964, numerous refinements have been added to the phenomenological model of how auroral substorms develop. Here, we review several of the most important of these including; polar cap patches, intensifications of the Polar Activated Band (PAB) at the poleward edge of the bulge, the development of new arcs poleward, the generation of auroral streamers, the evolution of streamers into auroral torches and omega bands, the association of streamers with particle injection and BBF activity in the tail, and wedgelet-like magnetic perturbations on the ground. We also review the concept of “contact breakups” which are breakups that appear to be triggered by the arrival of streamer-like forms in the equatorward regions of the oval. Finally, we remind the community that the addition of detached sub-auroral emissions was made to the phenomenological picture of substorm develop in the late 1960s along with the concept of auroral streamers. Newer observations of detached sub-auroral (STEVE-like emissions) from the Viking/UVI and POLAR/VIS imagers are presented which confirm that they are east-west aligned bands of emission that separate away from the equatorward edge of the auroral oval in response to intensifications of the poleward edge of the bulge and subsequent streamer production. We propose a new model for the formation of detached sub-auroral (STEVE-like) emissions in which a non-linear growth of a SAPS-driven instability (e.g. like the shear-flow/ballooning instability) at the plasmapause results in the disruption of the boundary separating hot plasma sheet particle from the cold plasmaspheric plasma. It is proposed that the resulting intermixing of plasma populations leads to both the observed STEVE-like emissions and also provides a source of cold plasma on open drift paths that can feed the long-lived drainage plumes that have recently been discovered.

58 GEOSCIENCES↗

DOE OSTI2026

Coupling geophysical, geological, geochemical and mineralogical assessments to examine preferential contaminant transport pathways in interbedded fractured bedrock

This study shows that a multi-faceted approach, combining borehole geophysical logging and surface seismic P-wave first-arrival tomography with confirmatory coring, well installation, and chemical and mineralogical analysis, is effective for identifying difficult-to-locate preferential contaminant transport pathways in deeper fractured bedrock. Seismic tomography detected porous 10–20 m wide elongated fractured conduits that allow acidic groundwater contaminated with uranium (U) and nitrate (NO 3 - ) to migrate within interbedded shale-limestone bedrock over 1000 m from a former disposal facility (S-3 Ponds site) located at the DOE Y-12 National Security Complex in Tennessee (USA). Conventional drilling techniques would easily miss these conduits because they are oriented parallel with fractured bedding planes. Synchrotron analysis of aquifer solids revealed that > 95 % of the U is hexavalent (U VI ). This uranyl (UO 2 2+ ) species is coordinated with carbonate, iron oxide, silicate and phosphate minerals within cemented white to yellow precipitates, which contain U concentrations as high as ∼21.6 % by weight fraction. Identifying the presence of these mineral phases, enables a further understanding of the potential effectiveness of remediation actions. The combination of methodologies presented here can also be applied to other explorations, such as the detection of water supply.

Fate and transport↗

DOE OSTI2023

Locating anionic hydrogen in Ba 3 (Yb,Lu) 2 O 5 H 2 : A combined approach of X-ray diffraction, crystal chemistry, and DFT calculations

By a combination of x-ray diffraction, structural chemistry, and DFT calculations, the presence and location of anionic hydrogen in the two new, layered lanthanide oxyhydrides, Ba 3 Ln 2 O 5 H 2 (Ln = Yb, Lu) is inferred. Single crystals of the compounds have been synthesized from a molten barium flux with the addition of small amounts of BaH 2 . These phases crystallize in space group I4/mmm (#139, Z = 2) with lattice parameters a = 4.3336(2) Å and c = 22.7197(6) Å, and a = 4.3291(1) Å and c = 22.597(1) Å, respectively. The Ba 3 Ln 2 O 5 H 2 phases comprise two different structural moieties: a perovskite double layer of stoichiometry Ba 2 Ln 2 O 5 H – formed by corner-connected LnO 5 tetragonal bi-pyramids with a terminating hydrogen anion, and a puckered rocksalt-type (BaH) + layer that is stretched along the c-axis. DFT calculations were used to arrive at hydrogen positions that minimize energy and are consistent with structural chemistry principles. Furthermore, the calculations show that the valence band edge is dominated by oxygen 2p orbitals with hydrogen 1s states admixed. The conduction band is formed by barium 5d-orbitals and Lu (Yb) 5d-orbitals. These are characteristics of materials with anionic H – . These new phases are isostructural with the Ba 3 Ln 2 O 5 Cl 2 (Ln = Gd–Lu) family of compounds with the chlorine atom in the same apical position as the hydrogen atom. Finally, steric effects limit the size of the lanthanide ion for Ba 3 Ln 2 O 5 H 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Impact of galactic cosmic ray simulation on nutritional content of foods

Foods packaged for future deep-space exploration missions may be prepositioned ahead of astronaut arrival, and will be exposed to galactic cosmic rays (GCRs) and solar radiation in deep space at higher levels and different spectrums than those found in low-Earth orbit (LEO). In this study, we have evaluated the impact of a GCR simulation (approximately 0.5 and 5 Gy doses) at the NASA Space Radiation Laboratory (NSRL) on two retort thermostabilized food products that are good sources of radiation labile nutrients (thiamin, vitamin E, or unsaturated fats). No trends or nutritional differences were found between the radiation-treated samples and the control immediately after treatment or one-year after treatment. Small changes in a few nutrients were measured following one-year of storage. Further studies are needed to confirm these results, as the foods in this study were heterogeneous, and this may have masked meaningful changes due to pouch-to-pouch variations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Towards controlled synthesis of 2D crystals by chemical vapor deposition (CVD)

We report the emergence of two-dimensional (2D) materials has captured the imagination of researchers since graphene was first exfoliated from graphite in 2004. Their exotic properties give rise to many exciting potential applications in advanced electronic, optoelectronic, energy and biomedical technologies. Scalable growth of high quality 2D materials is crucial for their adoption in technological applications the same way the arrival of high quality silicon single crystals was to the semiconductor industry. A huge amount of effort has been devoted to grow large-area, highly crystalline 2D crystals such as graphene and transition metal dichalcogenides (TMDs) through various methods. While CVD growth of wafer-scale monolayer graphene and TMDs has been demonstrated, considerable challenges still remain. In this perspective, we advocate for the focus on the crystal growth morphology as an underpinning for understanding, diagnosing and controlling the CVD process and environment for 2D material growth. Like snowflakes in nature, 2D crystals exhibit a rich variety of morphologies under different growth conditions. The mapping of crystal shapes in the growth parameter space “encodes” a wealth of information, the deciphering of which will lead to better understanding of the fundamental growth mechanism and materials properties. To this end, we envision a collective effort by the 2D materials community to establish the correlation between crystal shapes and the intrinsic thermodynamic and kinetic parameters for CVD reactions through integrated crystal growth experiment, database development and machine learning assisted predictive modeling, which will pave a robust path towards controlled synthesis of 2D materials and heterostructures.

36 MATERIALS SCIENCE↗