Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Databases”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Sox9 mediated transcriptional activation of FOXK2 is critical for colorectal cancer cells proliferation

FOXK2, which belongs to the fork head DNA binding protein family, has been shown to play a critical role in tumorigenesis. Here, we detected FOXK2 expression and its clinical significance in colorectal cancer, which has not been fully investigated before. Results from public database and our cohort indicated that FOXK2 was transcriptionally activated in colorectal cancer tissues compared to non-cancer tissues. High expression of FOXK2 was significantly correlated with poor survival. In vitro cell experiments suggested that FOXK2 promoted cell proliferation. Furthermore, we found that oncogene SOX9 was responsible for the up-regulation of FOXK2 by directly binding on its promoter. Depletion of FOXK2 attenuated SOX9 induced cell growth. In addition, we observed that the expression of FOXK2 was significantly associated with the expression of SOX9 both in the public database and our colorectal cancer tissues. The patients with SOX9{sup +}FOXK2{sup +} had a poor overall survival (p = 0.0084). In conclusion, our data suggested that SOX9 transcriptionally activated FOXK2 was involved in the pathogenesis of colorectal cancer and might be a novel target for colorectal cancer therapy. - Highlights: • FOXK2 is up-regulated in colorectal cancer and associated with poor prognosis. • FOXK2 promotes proliferation of CRC cells. • FOXK2 is transcriptional activated by SOX9. • FOXK2 expression is correlated with SOX9 in CRC tissues.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Dysregulation of long noncoding RNAs in mouse testes and spermatozoa after exposure to cadmium

There is increasing evidence that cadmium (Cd) exposure can cause male subfertility and even complete infertility in mammals. Long noncoding (lnc) RNAs are critical for spermatogenesis, and their dysregulation might lead to male infertility. However, whether they are involved in Cd-induced subfertility is unknown. Here we found that intraperitoneal exposure to Cd in mice led to male subfertility indicated by reductions in testicular sperm production and motility, and by abnormal morphology. Testicular and sperm RNAs were used to investigate lncRNA expression profiles by strand-specific RNA sequencing at the transcriptome level to help determine any RNA-related mechanisms in Cd-induced subfertility. The Cd-treated testes and spermatozoa exhibited aberrant expression profiles for lncRNAs and mRNAs. Of the lncRNAs, there were 139 with upregulated expression and 174 with downregulated expression in testes; in contrast, 685 were upregulated and 375 were downregulated in spermatozoa. For mRNA expression, 214 were upregulated and 226 were downregulated in testes; 272 were upregulated and 111 were downregulated in spermatozoa. Gene ontology and pathway analyses showed that the functions of differentially expressed lncRNA targets and mRNAs were closely linked with many processes involved in spermatogenesis. Additionally, many newly identified lncRNAs showed inducible expression, suggesting that they might be good candidate markers for Cd-induced male reproductive toxicity. This study provides a preliminary database for further exploring lncRNA-related mechnisms in male infertility induced by Cd. - Highlights: • LncRNA profiles were changed by Cd exposure in mouse testes and spermatozoa. • Differentially expressed lncRNA targets and mRNAs were linked with spermatogenesis. • Providing a database for exploring lncRNA-related mechnisms in male infertility. • LncRNA might be candidate markers for Cd-induced male reproductive toxicity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Identification of repaglinide as a therapeutic drug for glioblastoma multiforme

Glioblastoma multiforme (GBM) is a highly aggressive brain tumor with a median survival time of only 14 months after treatment. It is urgent to find new therapeutic drugs that increase survival time of GBM patients. To achieve this goal, we screened differentially expressed genes between long-term and short-term survived GBM patients from Gene Expression Omnibus database and found gene expression signature for the long-term survived GBM patients. The signaling networks of all those differentially expressed genes converged to protein binding, extracellular matrix and tissue development as revealed in BiNGO and Cytoscape. Drug repositioning in Connectivity Map by using the gene expression signature identified repaglinide, a first-line drug for diabetes mellitus, as the most promising novel drug for GBM. In vitro experiments demonstrated that repaglinide significantly inhibited the proliferation and migration of human GBM cells. In vivo experiments demonstrated that repaglinide prominently prolonged the median survival time of mice bearing orthotopic glioma. Mechanistically, repaglinide significantly reduced Bcl-2, Beclin-1 and PD-L1 expression in glioma tissues, indicating that repaglinide may exert its anti-cancer effects via apoptotic, autophagic and immune checkpoint signaling. Taken together, repaglinide is likely to be an effective drug to prolong life span of GBM patients. - Highlights: • Gene expression signarue in long-term survived GBM patients are identified from Gene Expression Omnibus database. • Repaglinide is identified as a survival-related drug for GBM via drug repositioning in CMap. • Repaglinide effectively kills GBM cells, inhibits GBM cell migration and increases survival of mice bearing orthotopic glioma. • Repaglinide reduces Bcl-2, Beclin-1 and PD-L1 in GBM tissues.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SUPCRTBL: A revised and extended thermodynamic dataset and software package of SUPCRT92

The computer-enabled thermodynamic database associated with SUPCRT92 (Johnson et al., 1992) enables the calculation of the standard molal thermodynamic properties of minerals, gases, aqueous species, and reactions for a wide range of temperatures and pressures. However, new data on the thermodynamic properties of both aqueous species and minerals have become available since the database’s initial release in 1992 and its subsequent updates. Here, in light of these developments, we have expanded SUPCRT92’s thermodynamic dataset and have modified the accompanying computer code for thermodynamic calculations by using newly available properties.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Ab initio simulations on the pure Cr lattice stability at 0K: Verification with the Fe-Cr and Ni-Cr binary systems

Significant discrepancies have been observed and discussed on the lattice stability of Cr between the predictions from the ab initio calculations and the CALPHAD approach. In the current work, we carefully examined the possible structures for pure Cr and reviewed the history back from how Kaufman originally determined the Gibbs energy of FCC-Cr in the 1970s. The reliability of Cr lattice stability derived by the CALPHAD and ab initio approaches was systematically discussed. It is concluded that the Cr lattice stability based on the CALPHAD approach has large uncertainty. Meanwhile, we cannot claim that the ab initio H FCC-Cr is error-free as FCC-Cr is an unstable phase under ambient conditions. The present work shows that the ab initio HFCC-Cr can be a viable scientific approach. As both approaches have their limitations, the present work propose to integrate the ab initio results into the CALPHAD platform for the development of the next generation CALPHAD database. As a result, the Fe-Cr and Ni-Cr binary systems were chosen as two case studies demonstrating the capability to adopt the ab initio Cr lattice stability directly into the current CALPHAD database framework.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Genomic materials design: CALculation of PHAse Dynamics

The CALPHAD system of fundamental phase-level databases, now known as the Materials Genome, has enabled a mature technology of computational materials design and qualification that has already met the acceleration goals of the national Materials Genome Initiative. Here, as first commercialized by QuesTek Innovations, the methodology combines efficient genomic-level parametric design of new material composition and process specifications with multidisciplinary simulation-based forecasting of manufacturing variation, integrating efficient uncertainty management. Recent projects demonstrated under the multi-institutional CHiMaD Design Center notably include novel alloys designed specifically for additive manufacturing. With the proven success of the CALPHAD-based Materials Genome technology, current university research emphasizes new methodologies for affordable accelerated expansion of more accurate CALPHAD databases. Rapid adoption of these new capabilities by US apex corporations has compressed the materials design and development cycle to under 2 years, enabling a new “materials concurrency” integrated into a new level of concurrent engineering supporting an unprecedented level of manufacturing innovation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Implementation of an extensible property modeling framework in ESPEI with applications to molar volume and elastic stiffness models

Property models are becoming more widely adopted by commercial Calphad databases, but they are not nearly as common in non-commercial or traditional academic Calphad databases. A primary driver is that user-friendly Calphad modeling tools that support property models are not widely available. Here we present new property modeling capabilities that have been implemented in ESPEI (the Extensible, Self-optimizing Phase Equilibrium Infrastructure). These capabilities include both generating property model parameters from data and improvements to the algorithmic selection of the most appropriate model from a series of candidates. Additionally, two illustrative examples are given that use ESPEI to fit different property models. First, we generate molar volume model parameters for Group IV, V, and VI refractory BCC alloys based on the model by Lu et al. (2005). Second, we demonstrate the extensibility of ESPEI’s property modeling capabilities by implementing a custom PyCalphad model for BCC elastic stiffness parameters to generate and compare parameters to the ones assessed by Marker et al. (2018) using the same data. Property models generated by ESPEI can be used in PyCalphad or further optimized with uncertainty quantification using ESPEI.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Simulation of the properties of MgO-MgfCl{sub 2}-H{sub 2}O system by thermodynamic method

Magnesium oxychloride cement (MOC) has been investigated by many researchers who have studied its hydration products and properties. Previous works mainly concentrated on experimental studies. This study uses a thermodynamic approach to understand the formation conditions of two major hydration products of MOC, Mg{sub 3}(OH){sub 5}Cl · 4H{sub 2}O (phase 5) and Mg{sub 2}(OH){sub 3}Cl · 4H{sub 2}O (phase 3) at room temperature. The hydration reaction equilibrium of MOC phases has been predicted by using the geochemistry speciation code PHREEQC together with the extended ‘Pitzer.dat’ database. The results show that the formation of the hydration products are controlled by MgCl{sub 2} concentration, activity of H{sub 2}O and pH values of the system. The equilibrium of solid phase diagram, the solubility of different hydration phases are consistent with those results obtained from experimental studies, which validates the thermodynamic model. The phase diagram provides qualitative insights on the synthesis of pure phase 3 and phase 5. For MgO-MgCl{sub 2}-H{sub 2}O system, the minimum MgCl{sub 2} concentration for phase 5 and phase 3 to form are 1.47 mol · kg{sup −} {sup 1} and 2.25 mol · kg{sup −} {sup 1}, respectively. Increasing water volume can result in the transformation from phase 5 and phase 3 to brucite. Additionally, the stability analysis suggests that phase 5 prefers to formation at a higher condition of the a{sub Mg}2 +, pH and a{sub H2O} as compared to the phase 3. Phosphate can significantly influence the hydration products composition of MOC during hydration but no new phosphate appears. - Highlights: • A thermodynamic database was established for magnesium oxychloride cement system on the basis of Pitzer.dat. • Thermodynamic behaviors of magnesium oxychloride cement hydration was modeled. • Calculated phase diagram of magnesium oxychloride cement was plotted.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Shrinkage-induced deformations and creep of structural concrete: 1-year measurements and numerical prediction

Highlights: • Extensive experimental study on drying shrinkage, creep and microcracking of concrete • All specimens prepared from a single batch of ordinary-strength structural concrete • 1st year of measurements of (not only) non-uniformly drying beams with span up to 3-m • The database is downloadable from free-to-use research data repository • Modified MPS model for concrete creep used in blind prediction of all experiments The material models for creep and shrinkage operating on the material point level in FEM are usually intended for challenging complex applications and structures, where the average cross-sectional approach does not suffice. The identification of the growing number of material parameters induced by increasing model capabilities relies on very specific and narrow-oriented yet interconnected experiments which are scarce. The presented comprehensive experiments aim to provide a clearer image of the complicated interaction among the basic phenomena: drying, shrinkage, creep, and microcracking. The cornerstone of this ongoing research is a unique set of 30 partially-sealed unreinforced concrete beams with span 1.75–3.0 m subjected to drying. To minimize material variation, all specimens in this study were cast from a single batch of ordinary strength structural concrete with slag-blended binder. The resulting experimental database will be suitable both for validation and development of the constitutive models.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predicting oxidation damage of ultra high-temperature carbide ceramics in extreme environments using machine learning

Determining the oxidation resistance of UHTC carbides in extreme environments is challenging theoretically and experimentally due to the high dimensional complexity of influencing variables and intricate testing setups. Herein we demonstrate the use of machine learning (ML) models trained with experimental literature data to predict the oxide thickness of UHTC carbides exposed to air based on composition, mean grain size, relative densification, holding time, and temperature. A multi-dimensional database with 76 occurrences is created containing experimental results of Hf, Zr, and Ta carbides plus additives. In this study, the preprocessed database is then used to train ML models to predict their oxidation behavior. The trained model predicts the oxidation damage in the form of an average oxide thickness in UHTC carbides with a Mean Absolute Error (MAE) of ±65.45 μm for samples in the testing set that developed thicknesses up to 1000 μm. The model successfully predicted oxidation damage for a recession rate lower than 60 μm/min. It is noticed that the ensemble method MAE is increased to ±134.34 μm while forecasting the oxidation of samples with a recession rate higher than the threshold. The unprecedented approach is a novel way to predict the damage through the oxidation of carbide compounds before processing for a smarter design with room for improvement.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Evaluation of experimentally measured and model-calculated pH for rock-brine-CO2 systems under geologic CO2 sequestration conditions

pH is an essential parameter for understanding the geochemical reactions that occur in rock-brine-CO2 systems when CO2 is injected into deep geologic formations for long-term storage. Due to a lack of reliable experimental methods, most laboratory studies conducted under geological CO2 sequestration (GCS) conditions have relied on thermodynamic modeling to estimate pH. The accuracy of these model predictions is typically uncertain. In our previous work, we have developed a method for pH determination by in-situ spectrophotometry. In the present work, we expanded the applicable pH range for this method and measured the pH of several rock-brine-CO2 systems at GCS conditions for five rock samples collected from ongoing GCS demonstration projects. Experimental measurements were compared with pH values calculated using several geochemical modeling approaches. The effect of different thermodynamic databases on the accuracy of model prediction was evaluated. Results indicate that the accuracy of model calculations is rock-dependent. For rocks comprised of carbonate and sandstone, model results generally agreed well with experimentally measured pH; however, for basalt, significant differences were observed. These discrepancies may be due to the models’ failure to fully account for certain reaction occurring between the basalt minerals the CO2-saturated brine solutions.

pH, carbon sequestration↗
DOE OSTI2018

Corrigendum to “Experimental Determination of Solubilities of Di-calcium Ethylenediaminetetraacetic Acid Hydrate [Ca 2 C 10 H 12 N 2 O 8 •7H 2 O(s)] in NaCl and MgCl 2 Solutions to High Ionic Strengths and Its Pitzer Model: Applications to Geological Disposal of Nuclear Waste and Other Low Temperature Environments”

In the published version (Xiong et al., 2017), there was an error for the reaction coefficient for the dissolution reaction of Ca 2 C 10 H 12 N 2 O 8 •7H 2 O(s) in the database used for the modeling. In the database for the modeling, the coefficient for water (i.e., 7H 2 O) was inadvertently omitted.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

An a priori DNS study of the shadow-position mixing model

In this study, the modeling of mixing by molecular diffusion is a central aspect for transported probability density function (tPDF) methods. In this paper, the newly-proposed shadow position mixing model (SPMM) is examined, using a DNS database for a temporally evolving di-methyl ether slot jet flame. Two methods that invoke different levels of approximation are proposed to extract the shadow displacement (equivalent to shadow position) from the DNS database. An approach for a priori analysis of the mixing-model performance is developed. The shadow displacement is highly correlated with both mixture fraction and velocity, and the peak correlation coefficient of the shadow displacement and mixture fraction is higher than that of the shadow displacement and velocity. This suggests that the composition-space localness is reasonably well enforced by the model, with appropriate choices of model constants. The conditional diffusion of mixture fraction and major species from DNS and from SPMM are then compared, using mixing rates that are derived by matching the mixture fraction scalar dissipation rates. Good qualitative agreement is found, for the prediction of the locations of zero and maximum/minimum conditional diffusion locations for mixture fraction and individual species. Similar comparisons are performed for DNS and the IECM (interaction by exchange with the conditional mean) model. The agreement between SPMM and DNS is better than that between IECM and DNS, in terms of conditional diffusion iso-contour similarities and global normalized residual levels. It is found that a suitable value for the model constant c that controls the mixing frequency can be derived using the local normalized scalar variance, and that the model constant a controls the localness of the model. A higher-Reynolds-number test case is anticipated to be more appropriate to evaluate the mixing models, and stand-alone transported PDF simulations are required to more fully enforce localness and to assess model performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Systematically derived thermodynamic properties for alkane oxidation

Key combustion properties, such as ignition delay time, show strong sensitivity to the thermochemistry of the main species in the standard radical oxidation pathway at low temperatures (600-1000K). Significant uncertainties persist in current estimates of thermodynamic properties, particularly for the larger species of relevance to common practical fuels. In this article, we use advanced computational schemes to evaluate thermodynamic properties for the fuel, fuel radical, peroxy, hydroperoxy-alkyl radical, and hydroperoxide species for a set of 17 fuels containing up to 9 carbon atoms, and with various degrees of branching in the alkane backbone. The procedure, termed STAR-1D, combines conformer sampling to find the minimum geometry, B2PLYP-D3/cc-pVTZ harmonic frequency evaluations, and omega B97X-D/cc-pVTZ one-dimensional torsional mapping. It includes two physically-based scaling routines: a frequency-dependent scaling to B2PLYP-D3/cc-pVTZ anharmonic frequencies and a scaling of one-dimensional omega B97X-D/ccpVTZ torsional profiles to reproduce the product of the B2PLYP-D3/cc-pVTZ frequencies in the harmonic limit. Substantive comparisons with existing experimental databases, together with careful examinations of key theoretical assumptions, are used to explore the accuracy of the predictions. These computationally intensive explorations of 195 species were facilitated by automated thermochemistry software. Further, high accuracy 0 K heats of formation from a separate study are used with the STAR-1D computations to generate NASA polynomial representations. In addition to their intrinsic value, the present results also provide a reliable database for the optimization of group additivity or machine learning schemes for scaling to larger combustion systems. Towards this end a complementary extensive conformational analysis is carried out for the medium sized species and the thermodynamic properties of the lowest energy hydrogen-bonded and non-hydrogen bonded conformers are contrasted for larger hydroperoxy-alkyl radical species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Group additivity values for the heat of formation of C 2 –C 8 alkanes, alkyl hydroperoxides, and their radicals

A set of 58 group additivity values (GAV) for the calculation of the heat of formation is derived from an extensive and accurate database of 192 ab initio heats of formation. The ab initio values are from companion calculations at the CCSD(T)–F12/cc-pVTZ-F12//B2PLYPD3/cc-pVTZ level of theory employing second order connectivity based hierarchy reference reactions with ANL energies for the reference species. This database of 192 species consists of alkanes (RH), alkyl radicals ($\dot{\text {R}}$), alkyl hydroperoxides (RO 2 H), alkyl-peroxy (R$\dot{\text {O}}$ 2 ) radicals, and hydroperoxy-alkyl ($\dot{\text {Q}}$OOH) radicals of all of the isomers of C 2 –C 5 alkane fuels and a select set of C 6 –C 8 isomers. The GAV and ab initio based enthalpies for this dataset show excellent agreement, with an estimated 2σ uncertainty of 0.9 kcal mol –1 . Among the 58 GAV terms, 40 are refinements of previously reported terms, while 18 are newly developed. These new GAV terms are mainly non-next-nearest neighbor interactions (NNI) and β-corrections. The inclusion of these new groups significantly improves the accuracy of the GAV estimates of the heats of formation. Finally, the updated GAVs can be used with increased confidence to estimate the heats of formation of combustion relevant hydrocarbons and oxygenated hydrocarbons and their corresponding radicals, which are important in predicting low-temperature chemistry and are critical in the development of accurate chemical kinetic models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Group additivity values for entropy and heat capacities of C 2 –C 8 alkanes, alkyl hydroperoxides, and their radicals

Group additivity values for the thermodynamic properties of oxygenated radicals are poorly determined due to the absence of high quality reference data. Here, a set of 58 group additive values (GAV) for the standard entropy and heat capacity of relevance to alkane oxidation is derived from fits to an extensive and accurate database of standard entropies (298.15 K) and heat capacities (300–3000 K) recently calculated with the “STAR-1D” formalism. The 192 species in this database represent the alkanes (RH), alkyl radicals ($\dot{R}$), alkyl hydroperoxides (RO 2 H), alkyl-peroxy (R$\dot{O}$ 2 ) and hydroperoxy-alkyl ($\dot{Q}$OOH) radicals for all of the isomers of C 2 –C 5 alkane fuels and a select number of isomers of C 6 –C 9 species. The STAR-1D thermochemical data for this set of species was previously obtained from a coupling of scaled B2PLYPD3/cc-pVTZ vibrational analyses with scaled ωB97X-D/cc-pVTZ one-dimensional hindered rotor corrections. The 2σ uncertainties in the GAV results relative to the STAR-1D data set are 2.4 cal K –1 mol –1 for the entropies and at most 2.0 cal K –1 mol –1 for the heat capacities in the temperature range 500–800 K. The 2σ fitting uncertainties in the heat capacity gradually reduce at higher temperatures reaching a value of 0.8 cal K –1 mol –1 at 2000 K decreasing to only 0.5 cal K –1 mol –1 at 3000 K. The high degree of accuracy for the GAV representations is obtained through the introduction of various new group terms, together with the re-optimization of existing group terms. Among the full set of 58 GAV terms, 25 include non-next-nearest neighbor interactions (NNI) and β-corrections. The updated GAVs can be applied in the prediction of entropies and heat capacities for a wide range of hydrocarbons and hydroperoxide species and their radicals, which is important to the accurate prediction of fuel reactivity at low-temperatures in the range 600–1000 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows

We introduce atomate, an open-source Python framework for computational materials science simula-tion, analysis, and design with an emphasis on automation and extensibility. Built on top of open sourcePython packages already in use by the materials community such as pymatgen, FireWorks, and custodian,atomate provides well-tested workflow templates to compute various materials properties such as elec-tronic bandstructure, elastic properties, and piezoelectric, dielectric, and ferroelectric properties. Atomatealso enables the computational characterization of materials by providing workflows that calculate X-rayabsorption (XAS), Electron energy loss (EELS) and Raman spectra. One of the major features of atomate isthat it provides both fully functional workflows as well as reusable components that enable one to com-pose complex materials science workflows that use a diverse set of computational tools. Additionally,atomate creates output databases that organize the results from individual calculations and contains abuilder framework that creates summary reports for each computed material based on multiplesimulations.

Mathew, Kiran↗