High-throughput Computational Approaches Research Articles

High-throughput screening of single-atom catalysts on 1 T-TMD for highly active and selective CO2 reduction reaction: Computational and machine learning insights

The study of transition metals and various possible surface compositions has sparked great interest in low-cost materials, which exhibit high activity and selectivity in catalysis. While various single-atom catalysts loaded on transition metal dichalcogenide (TMD) substrates with excellent CO2 reduction performance have been identified, the relationship between catalytic activity and the intrinsic properties of TMD single-atom catalysts remains unclear. Hence, a high-throughput first-principle computational approach is proposed to screen 24 transition metals anchored on 8 TMD monolayers to determine their catalytic activity in CO2RR. The results show that Fe@CoS2, Pt@TiTe2 and Co@CoS2 exhibit exceptional performances with low CO2RR limiting-potentials of −0.045 eV, 0.75 eV, and 0.54 eV, respectively, showcasing selective pathways towards formic acid (HCOOH), methane (CH4), and methanol (CH3OH). Employing the Sure Independence Screening and Sparsifying Operator method(SISSO), key descriptors linking the performance of single-atom catalysts with their intrinsic features are identified, providing insights for the discovery of superior CO2RR catalysts. Moreover, it was observed that a feature of the anchored single atom, the difference between covalent radius and atomic radius (CR-R), is associated with multiple crucial reaction steps, exhibiting a strong linear relationship with the charge transfer of *COOH. This work not only identifies promising CO2RR catalysts but also establishes a predictive framework for screening catalysts based on their intrinsic properties, paving the way for future advancements in CO2 reduction research.

Abstract PO5-01-09: Exploring cellular heterogeneity of localised breast cancers

Abstract Breast cancer is a heterogeneous disease at multiple levels, ranging from subtype differences between patients (inter-patient heterogeneity) to the diverse composition of malignant cells, heterogeneity of hormone receptor (HR) expression and cellular makeup within single breast cancer samples (intra-tumour heterogeneity). Despite an increase in the number of effective therapies available, many patients will experience an incomplete treatment response and subsequent relapse. These adverse treatment outcomes may be attributed to the often overlooked but critical factor of cellular heterogeneity. In order to gain deeper insights into intra-tumour heterogeneity, we have applied single-cell technologies on a cohort comprising 250 primary, untreated breast cancers. We have optimized methods for tissue cryopreservation, eliminating the need for fresh sample processing, and multiplex tissue profiling. Together, these are cost-efficient processes that allow for improved handling of small tissue sizes, such as biopsies, and reduce batch effect. To ensure accurate and reliable data processing, we have developed a scalable computational workflow that includes benchmarked methods for sample SNP-demultiplexing, doublet detection, high-resolution cell annotation and cellular integration. Finally, we are extending our existing methods1 to study the cellular heterogeneity of breast cancers. Our method, scSubtyper, explores the phenotypic differences between malignant cells within tumours, by comparing each single cell to distinct features associated with different molecular subtypes and assigning each cell to one of these subtypes. Our previous study1 and preliminary results of this project revealed that over 90% of the samples exhibit a mix of malignant cells of different subtypes, and 50% of samples contain cells that have characteristics of all subtypes, demonstrating cellular heterogeneity exists not only exists between malignant cells, but also within malignant cells of a tumour. Our second approach, known as ecotyping, assesses patterns of cell type frequencies across samples and groups them based on similarity of these co-occurences. Our preliminary results have revealed the existence of 5 ecotypes that lack significant associations with samples clinical subtypes. Applying the same approach exclusively within the HR-positive samples identified 4 ecotypes characterized by distinct abundances of immune and stromal cells. This analysis revealed that ecotypes are not a simple surrogate for clinical and molecular subtypes, but their presence could drive different response to treatment. Together, our high-throughput tissue processing and computational approaches to studying intra-tumour heterogeneity are now being applied to our large, well annotated, clinical cohort. Supported by the preliminary results, we hypothesize that this study will play a vital role in optimizing breast cancer patient stratification to improve treatment management and outcome. 1. Wu, Sunny Z., et al. "A single-cell and spatially resolved atlas of human breast cancers." Nature genetics 53.9 (2021): 1334-1347. Citation Format: Beata Kiedik, Daniel Roden, Kate Harvey, Ghamdan Al-Eryani, Sunny Wu, Mun Hui, Sandra O'Toole, Elgene Lim, Charles Perou, Alex Swarbrick. Exploring cellular heterogeneity of localised breast cancers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-01-09.

High-throughput thermodynamic analysis of epitaxial growth of β-Ga2O3 by the chemical vapor deposition method from TMGa-H2O system

A comprehensive thermodynamic study of CVD β-Ga2O3 epitaxial films from TMGa-H2O (trimethylgallium-water) system is performed with a wide experimental condition range and high mesh grid number. The high-throughput computational approach was utilized, investigating the effects of temperature (500–1500 K), pressure (0–100,000 Pa) and H2O fraction (0–100%) on equilibrium condensed phase purity and gas speciation. The β-Ga2O3 growth regime expands with increasing temperature and H2O fraction. High purity (>99%) β-Ga2O3 is predicted under high H2O fractions (>80%) and temperatures from 1000–1300 K. And the dominant Ga species are Ga(g), Ga(OH) and Ga2O, while H2O, CO2 and CO carry O. Median temperatures of 1000–1200 K maximize TMGa conversion efficiency while minimizing graphite formation. The computations yield optimal CVD conditions of 1000–1200 K, 2000–10000 Pa, and VI: III> 7 for high quality β-Ga2O3 epitaxial growth, providing useful guidance for further experimental CVD process development.

Unleashing the potential of catalytic RNAs to combat mis-spliced transcripts.

Human transcriptome can undergo RNA mis-splicing due to spliceopathies contributing to the increasing number of genetic diseases including muscular dystrophy (MD), Alzheimer disease (AD), Huntington disease (HD), myelodysplastic syndromes (MDS). Intron retention (IR) is a major inducer of spliceopathies where two or more introns remain in the final mature mRNA and account for many intronic expansion diseases. Potential removal of such introns for therapeutic purposes can be feasible when utilizing bioinformatics, catalytic RNAs, and nano-drug delivery systems. Overcoming delivery challenges of catalytic RNAs was discussed in this review as a future perspective highlighting the significance of utilizing synthetic biology in addition to high throughput deep sequencing and computational approaches for the treatment of mis-spliced transcripts.

Free energy landscape and thermodynamics properties of novel mutations in PncA of pyrazinamide resistance isolates of Mycobacterium tuberculosis

Pyrazinamide (PZA) is one of the first-line antituberculosis therapy, active against non-replicating Mycobacterium tuberculosis (Mtb). The conversion of PZA into pyrazinoic acid (POA), the active form, required the activity of pncA gene product pyrazinamidase (PZase) activity. Mutations occurred in pncA are the primary cause behind the PZA resistance. However, the resistance mechanism is important to explore using high throughput computational approaches. Here we aimed to explore the mechanism of PZA resistance behind novel P62T, L120R, and V130M mutations in PZase using 200 ns molecular dynamics (MD) simulations. MD simulations were performed to observe the structural changes for these three mutants (MTs) compared to the wild types (WT). Root means square fluctuation, the radius of gyration, free energy landscape, root means square deviation, dynamic cross-correlation motion, and pocket volume were found in variation between WT and MTs, revealing the effects of P62T, L120R, and V130M. The free energy conformational landscape of MTs differs significantly from the WT system, lowering the binding of PZA. The geometric shape complementarity of the drug (PZA) and target protein (PZase) further confirmed that P62T, L120R, and V130M affect the protein structure. These effects on PZase may cause vulnerability to convert PZA into POA. Communicated by Ramaswamy H. Sarma

Hydrocarbons (C8–C12) separation in porous metallocavitand M-PPX (M = Cu, Ag, Au): From computational insight

High throughput computational approach has been employed to investigate the adsorption and separation of C8–C12 hydrocarbons (HCs) on a tube-shaped metallocavitand, pillarplex (PPX). The Cu (I), Ag (I), and Au (I) seamed PPX and their HC complexes has been examined using density functional theory (DFT), proclaiming Cu-PPX has a higher binding affinity with the HCs whereas Ag-PPX is lower. The thermodynamic properties reveal the host–guest complex is stable at ambient condition. Noncovalent interaction (NCI) analysis shows the interaction of guests within the cavity of PPX is mainly attributed by Van der Waals type. The energy decomposition analysis (EDA) enunciates the percentage contribution of dispersion interaction (ΔEdisp) is 59–62%, the electrostatic (ΔEelct) and orbital (ΔEorb) terms have a moderate contribution towards the total binding energy. The Grand canonical Monte Carlo (GCMC) simulation manifests the Cu-PPX and Au-PPX are the ideal hosts with the optimum capacity to store HCs at ambient conditions. The octane uptake is 18.01, 17.50, and 25.31 cm3STP/g in Cu, Ag and Au-PPX and the uptake reduces upon increasing the HC chain length. The isosteric heat of adsorption (Qst) is corroborated with the computed DFT data at ambient setup. The ideal adsorption solution theory (IAST) claims the C8 selectivity in Cu, Ag and Au-PPX is optimum at 298 K and the increasing temperature from 298 K to 323 K, the selectivity of the C9 and C12 in Cu-PPX and Au-PPX, respectively is remarkable after the complete separation of C8. Hence Cu-PPX and Au-PPX is the potential candidate for industrial use to separate the heavy HCs.

DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining.

The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization.

Controlling the physical properties of solid forms for active pharmaceutical ingredients (APIs) through cocrystallization is an important part of drug product development. However, it is difficult to know a priori which coformers will form cocrystals with a given API, and the current state-of-the-art for cocrystal discovery involves an expensive, time-consuming, and, at the early stages of pharmaceutical development, API material-limited experimental screen. We propose a systematic, high-throughput computational approach primarily aimed at identifying API/coformer pairs that are unlikely to lead to experimentally observable cocrystals and can therefore be eliminated with only a brief experimental check, from any experimental investigation. On the basis of a well-established crystal structure prediction (CSP) methodology, the proposed approach derives its efficiency by not requiring any expensive quantum mechanical calculations beyond those already performed for the CSP investigation of the neat API itself. The approach and assumptions are tested through a computational investigation on 30 potential 1:1 multicomponent systems (cocrystals and solvate) involving 3 active pharmaceutical ingredients and 9 coformers and one solvent. This is complemented with a detailed experimental investigation of all 30 pairs, which led to the discovery of five new cocrystals (three API–coformer combinations, a polymorphic cocrystal example, and one with different stoichiometries) and a cis-aconitic acid polymorph. The computational approach indicates that, for some APIs, a significant proportion of all potential API/coformer pairs could be investigated with only a brief experimental check, thereby saving considerable experimental effort.

Hit Expansion of a Noncovalent SARS-CoV-2 Main Protease Inhibitor

Inhibition of the SARS-CoV-2 main protease (Mpro) is a major focus of drug discovery efforts against COVID-19. Here we report a hit expansion of non-covalent inhibitors of Mpro. Starting from a recently discovered scaffold (The COVID Moonshot Consortium. Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitor Therapeutics. bioRxiv 2020.10.29.339317) represented by an isoquinoline series, we searched a database of over a billion compounds using a cheminformatics molecular fingerprinting approach. We identified and tested 48 compounds in enzyme inhibition assays, of which 21 exhibited inhibitory activity above 50% at 20 μM. Among these, four compounds with IC50 values around 1 μM were found. Interestingly, despite the large search space, the isoquinolone motif was conserved in each of these four strongest binders. Room-temperature X-ray structures of co-crystallized protein–inhibitor complexes were determined up to 1.9 Å resolution for two of these compounds as well as one of the stronger inhibitors in the original isoquinoline series, revealing essential interactions with the binding site and water molecules. Molecular dynamics simulations and quantum chemical calculations further elucidate the binding interactions as well as electrostatic effects on ligand binding. The results help explain the strength of this new non-covalent scaffold for Mpro inhibition and inform lead optimization efforts for this series, while demonstrating the effectiveness of a high-throughput computational approach to expanding a pharmacophore library.

Data-Driven Studies of the Magnetic Anisotropy of Two-Dimensional Magnetic Materials.

A key issue in layered materials is the dependence of their properties on their chemical composition and crystal structure in addition to the dimensionality. For instance, atomically thin magnetic structures exhibit novel spin properties that do not exist in the bulk. We use first-principles calculations, based on density functional theory, and machine learning to study the magnetocrystalline anisotropy of a set of single-layer two-dimensional structures that are derived from changing the chemical composition of the ferromagnetic semiconductor Cr2Ge2Te6. We discuss trends and identify descriptors for the magnetocrystalline anisotropy in monolayers with the chemical formula A2B2X6. Our data-driven study aims to provide physical insights into the microscopic origins of magnetic anisotropy in two dimensions. For instance, we demonstrate that hybridization plays a key role in determining the magnetic anisotropy of the materials investigated in this study. In addition, we demonstrate that first-principles calculations can be combined with machine learning to create a high-throughput computational approach for the targeted design of quantum materials with potential applications in areas ranging from sensing to data storage.

Sabatier principle of metal-support interaction for design of ultrastable metal nanocatalysts.

The stability of supported nanocatalysts is crucial to meeting environmental and energy challenges and necessitates fundamental theory to relieve trial-and-error experimentation and accelerate lab-to-fab translation. Here, we report a Sabatier principle of metal-support interaction for stabilizing metal nanocatalysts against sintering based on the kinetic simulations of 323 metal-support pairs using scaling relations from 1252 energetics data. Too strong of an interaction is shown to trigger Ostwald ripening, whereas too weak of an interaction stimulates particle migration and coalescence. High-throughput screening of supports enables the sintering resistance of nanocatalysts to reach the Tammann temperature on homogeneous supports and far beyond it on heteroenergetic supports. This theory, which is substantiated by first-principles neural network molecular dynamics simulations and experiments, paves the way for the design of ultrastable nanocatalysts.

Rapid screening of high-throughput ground state predictions

High-throughput computational thermodynamic approaches are becoming an increasingly popular tool to uncover novel compounds. However, traditional methods tend to be limited to stability predictions of stoichiometric phases at absolute zero. Such methods thus carry the risk of identifying an excess of possible phases that do not survive to temperatures of practical relevance. We demonstrate how the Calphad formalism, informed by simple first-principles input can be simply used to overcome this problem at a low computational cost and deliver quantitatively useful phase diagram predictions at all temperatures. We illustrate the method by re-assessing prior compound formation predictions and reconcile these findings with long-standing experimental evidence to the contrary.

High-throughput analysis of the interactions between viral proteins and host cell RNAs

RNA-protein interactions of a virus play a major role in the replication of RNA viruses. The replication and transcription of these viruses take place in the cytoplasm of the host cell; hence, there is a probability for the host RNA-viral protein and viral RNA-host protein interactions. The current study applies a high-throughput computational approach, including feature extraction and machine learning methods, to predict the affinity of protein sequences of ten viruses to three categories of RNA sequences. These categories include RNAs involved in the protein-RNA complexes stored in the RCSB database, the human miRNAs deposited at the mirBase database, and the lncRNA deposited in the LNCipedia database. The results show that evolution not only tries to conserve key viral proteins involved in the replication and transcription but also prunes their interaction capability. These proteins with specific interactions do not perturb the host cell through undesired interactions. On the other hand, the hypermutation rate of NSP3 is related to its affinity to host cell RNAs. The Gene Ontology (GO) analysis of the miRNA with affiliation to NSP3 suggests that these miRNAs show strongly significantly enriched GO terms related to the known symptoms of COVID-19. Docking and MD simulation study of the obtained miRNA through high-throughput analysis suggest a non-coding RNA (an RNA antitoxin, ToxI) as a natural aptamer drug candidate for NSP5 inhibition. Finally, a significant interplay of the host RNA-viral protein in the host cell can disrupt the host cell's system by influencing the RNA-dependent processes of the host cells, such as a differential expression in RNA. Furthermore, our results are useful to identify the side effects of mRNA-based vaccines, many of which are caused by the off-label interactions with the human lncRNAs.

High-throughput search for RE (La, Ce and Y) contained stoichiometric compound in steels

The design of rare earth (RE) bearing steels requires a thorough understanding of the formation tendency of RE involved phases in steels, while searching for binary and ternary compounds with a wide variety of composition and permutation need a remarkable amount of experimentation which is nearly infeasible. In the present work, we perform a thorough search for the RE-contained compounds in steels by a data-driven high-throughput computational approach. The search results indicate that RE may react with O and N to form a large amounts of oxide and nitride inclusions, while only Y participate in the formation of sulfide inclusion Y2MnS4 and Y2CaS4. For the case of ternary compounds in Fe-based solid solution, it is found that RE prefers to form ternary phases with the non-metallic elements, i.e., B, C, O, P and Si, and only Y is found to combine with metal Cr to form YCr4Fe8. Finally, our screen suggests that RE can participate in the formation of the nano-scale precipitates of κ-carbides, L12 precipitates and B2 precipitates, but MC and M2C carbides.

A Review on Drug Repurposing: A Strategy to Treat Human Coronavirus Disease (COVID-19)

Novel coronavirus pandemic has created a massive public health emergency causing around 1.85 million deaths world-wide till 5th January, 2021. New SARS (Severe Acute Respiratory Syndrome) coronavirus strain known as SARS-CoV-2 is the causative agent which infected more than 84 million people across the globe. Current epicentre of the pandemic has shifted to Europe and United States and Indian subcontinent from its place of origin-Wuhan City, Hubei province in China. Due to limited availability of vaccines against SARS-CoV-2 or its related β-coronavirus (SARS-CoV or MERS-CoV), mass immunization is currently not possible. Thus, use of curative therapies could be the only choice of intervention. Therefore, rapid treatment of millions of COVID-19 patients in limited time can only be achieved by repurposing pre-approved and existing drugs. Network-based high-throughput computational approach has also predicted several repurposable drugs. Cheaper, less toxic and well tolerated drugs such as antimalarial drugs: Chloroquine (CQ) & Hydroxychloroquine (HCQ); antiviral drugs: Remdesivir, Lopinavir and Ritonavir are among many others that have been proposed for the COVID-19 treatment. Presently limited controlled clinical trials are underway to assess the therapeutic outcome of these repurposed drugs along with novel candidate vaccines and medicines. Beside these, convalescent plasma therapy has also emerged as potential therapeutic approach being tested in several countries. This review focuses on few of the promising repurposed drugs and their outcomes that are presently under evaluation for their safety and efficacy against the coronavirus disease 2019 (COVID-19).

MicroRNAs Biogenesis, Functions and Role in Tumor Angiogenesis.

microRNAs (miRNAs) are small non-coding RNA molecules, evolutionary conserved. They target more than one mRNAs, thus influencing multiple molecular pathways, but also mRNAs may bind to a variety of miRNAs, either simultaneously or in a context-dependent manner. miRNAs biogenesis, including miRNA transcription, processing by Drosha and Dicer, transportation, RISC biding, and miRNA decay, are finely controlled in space and time.miRNAs are critical regulators in various biological processes, such as differentiation, proliferation, apoptosis, and development in both health and disease. Their dysregulation is involved in tumor initiation and progression. In tumors, they can act as onco-miRNAs or oncosuppressor-miRNA participating in distinct cellular pathways, and the same miRNA can perform both activities depending on the context.In tumor progression, the angiogenic switch is fundamental. miRNAs derived from tumor cells, endothelial cells, and cells of the surrounding microenvironment regulate tumor angiogenesis, acting as pro-angiomiR or anti-angiomiR.In this review, we described miRNA biogenesis and function, and we update the non-classical aspects of them. The most recent role in the nucleus, as transcriptional gene regulators and the different mechanisms by which they could be dysregulated, in tumor initiation and progression, are treated. In particular, we describe the role of miRNAs in sprouting angiogenesis, vessel co-option, and vasculogenic mimicry. The role of miRNAs in lymphoma angiogenesis is also discussed despite the scarcity of data.The information presented in this review reveals the need to do much more to discover the complete miRNA network regulating angiogenesis, not only using high-throughput computational analysis approaches but also morphological ones.

Accelerated Development of a Scalable Ring-Closing Metathesis to Manufacture AMG 176 Using a Combined High-Throughput Experimentation and Computational Modeling Approach

AMG 176 is a drug candidate in our oncology pipeline. Ring-closing metathesis (RCM) is a key reaction in the AMG 176 synthesis that is used to construct the 16-membered macrocycle. Process intensification was executed on a compressed timeline by combining high-throughput experimentation and computational analysis using density functional theory, which led to the identification of an optimal 4-bromobenzoate protecting group on the allylic alcohol moiety. Comprehensive process improvements led to a reduction in reaction volume from 800 to 50 L/kg with a >20% yield improvement compared with the discovery route. The RCM process developed from these studies was instrumental in the clinical advancement of the AMG 176 program.

High-throughput computational screening of oxide double perovskites for optoelectronic and photocatalysis applications

Oxide double perovskites A2B′B′′O6 are a class of emerging materials in the fields of optoelectronics and catalysis. Due to the chemical flexibilities of perovskite structures, there are multiple elemental combinations of cations A, B′, and B′′, which leading to tremendous candidates. In this study, we comprehensively screened stable oxide double perovskite A2B′B′′O6 from a pool of 2,018 perovskite candidates using a high-throughput computational approach. By considering a tolerance factor (t)–octahedral factor (μ) phase diagram, 138 candidates withFm3¯m, P21/c, and R3c phases were selected and systematically studied via first-principles calculations based on density functional theory. The screening procedure finally predicted the existence of 21 stable perovskites, and 14 among them have never been reported. Verification with existing experimental results demonstrates that the prediction accuracy for perovskite formability is approximately 90%. The predicted oxide double perovskites exhibit quasi-direct bandgaps ranging from 0 to 4.4 eV with a significantly small direct-indirect bandgap difference, balanced electron and hole effective masses, and strong optical absorptions. The newly predicted oxide double perovskites may enlarge the pool of material candidates for applications in optoelectronics and photocatalysis. This study provides a route for computational screening of novel perovskites for functional applications.

Physics Data Production on HPC: Experience to be efficiently running at scale

The Solenoidal Tracker at RHIC (STAR) is a multi-national supported experiment located at the Brookhaven National Lab and is currently the only remaining running experiment at RHIC. The raw physics data captured from the detector is on the order of tens of PBytes per data acquisition campaign, making STAR fit well within the definition of a big data science experiment. The production of the data has typically run using a High Throughput Computing (HTC) approach either done on a local farm or via Grid computing resources. Especially, all embedding simulations (complex workflow mixing real and simulated events) have been run on standard Linux resources at NERSC’s Parallel Distributed Systems Facility (PDSF). However, as per April 2019 PDSF has been retired and High Performance Computing (HPC) resources such as the Cray XC-40 Supercomputer known as “Cori” have become available for STAR’s data production as well as embedding. STAR has been the very first experiment to show feasibility of running a sustainable data production campaign on this computing resource. In this contribution, we hope to share with the community the best practices for using such resource efficiently. The use of Docker containers with Shifter is the standard approach to run on HPC at NERSC – this approach encapsulates the environment in which a standard STAR workflow runs. From the deployment of a tailored Scientific Linux environment (with the set of libraries and special configurations required for STAR to run) to the deployment of third-party software and the STAR specific software stack, we’ve learned it has become impractical to rely on a set of containers comprising each specific software release. To this extent, a solution based on the CernVM File System (CVMFS) for the deployment of software and services has been deployed but it doesn’t stop there. One needs to make careful scalability considerations when using a resource like Cori, such as avoiding metadata lookups, scalability of distributed filesystems, and real limitations of containerized environments on HPC. Additionally, CVMFS clients are not compatible on Cori nodes and one needs to rely on an indirect NFS mount scheme using custom services known as DVS servers designed to forward data to worker nodes. In our contribution, we will discuss our strategies from the past and our current solution based on CVMFS. The second focus of our presentation will be to discuss strategies to find the most efficient use of database Shifter containers serving our data production (a near “database as a service” approach) and the best methods to test and scale your workflow efficiently.

High-throughput thermodynamic screening of carbide/refractory metal cermets for ultra-high temperature applications

Carbide/refractory metal cermets possess attractive combinations of thermal and mechanical properties for ultra-high temperature structural applications. Using a high-throughput computational thermodynamic approach, 16 carbide/refractory metal cermet systems were identified, out of 1808 possible combinations, that could be fabricated by the displacive compensation of porosity (DCP) method using molten copper alloys as infiltrators at 1300 °C. Experimental results for ZrC/W and ZrC/Mo, and other proposed systems in the literature provide a level of validation to this approach. We found that copper alloys are more suitable for DCP than aluminum alloys owing to the low melting temperature of copper alloys (e.g., Zr2Cu and Ti2Cu), and the minimal chemical reaction as well as very limited mutual solid solubility of copper with refractory metals. Our results show that high-throughput thermodynamics calculation is a robust approach for systematically and thoroughly identifying refractory metal cermets that are suitable for DCP.