Synthetic Cluster Research Articles

PHANGS-HST: new methods for star cluster identification in nearby galaxies

ABSTRACT We present an innovative and widely applicable approach for the detection and classification of stellar clusters, developed for the PHANGS-HST Treasury Program, an NUV-to-I band imaging campaign of 38 spiral galaxies. Our pipeline first generates a unified master source list for stars and candidate clusters, to enable a self-consistent inventory of all star formation products. To distinguish cluster candidates from stars, we introduce the Multiple Concentration Index (MCI) parameter, and measure inner and outer MCIs to probe morphology in more detail than with a single, standard concentration index (CI). We improve upon cluster candidate selection, jointly basing our criteria on expectations for MCI derived from synthetic cluster populations and existing cluster catalogues, yielding model and semi-empirical selection regions (respectively). Selection purity (confirmed clusters versus candidates, assessed via human-based classification) is high (up to 70 per cent) for moderately luminous sources in the semi-empirical selection region, and somewhat lower overall (outside the region or fainter). The number of candidates rises steeply with decreasing luminosity, but pipeline-integrated Machine Learning (ML) classification prevents this from being problematic. We quantify the performance of our PHANGS-HST methods in comparison to LEGUS for a sample of four galaxies in common to both surveys, finding overall agreement with 50–75 per cent of human verified star clusters appearing in both catalogues, but also subtle differences attributable to specific choices adopted by each project. The PHANGS-HST ML-classified Class 1 or 2 catalogues reach ∼1 mag fainter, ∼2 × lower stellar mass, and are 2−5 × larger in number, than attained in the human classified samples.

Rare-Earth Elements Can Structurally and Energetically Replace the Calcium in a Synthetic Mn4CaO4-Cluster Mimicking the Oxygen-Evolving Center in Photosynthesis.

The oxygen-evolving center (OEC) in photosynthesis is a unique biological Mn4CaO5 cluster catalyzing the water-splitting reaction. A great current challenge is to achieve a robust and precise mimic of the OEC in the laboratory. Herein, we report synthetic Mn4XO4 clusters (X = calcium, yttrium, gadolinium) that closely resemble the OEC with regard to the main metal-oxide core and peripheral ligands, as well as the oxidation states of the four Mn ions and the redox potential of the cluster. We demonstrate that rare-earth elements can structurally replace the calcium in neutral Mn4XO4 clusters. All three Mn4XO4 clusters with different redox-inactive metal ions display essentially the same redox properties, challenging the conventional view that the Lewis acidity of the redox-inactive metal ions could modulate the redox potential of the heteronuclear-oxide clusters. The new synthetic rare-earth element-containing Mn4XO4 clusters reported here provide robust and structurally well-defined chemical models and shed new light on the design of new water-splitting catalysts in artificial photosynthesis.

Construction of recombinant Escherichia coli producing nitrogenase-related proteins from Azotobacter vinelandii.

Biological nitrogen fixation by nitrogenase has attracted attention as an alternative method to chemical nitrogen fixation, which requires large amounts of fossil fuels. Azotobacter vinelandii, which produces an oxygen-sensitive nitrogenase, can fix nitrogen even under aerobic conditions; therefore, the heterologous expression of nif-related genes from A. vinelandii is a promising strategy for developing a biological nitrogen fixation method. We assembled 17 nif-related genes, which are scattered throughout the genome of A. vinelandii, into synthetic gene clusters by overlap-extension-PCR and seamless cloning and expressed them in Escherichia coli. The transcription and translation of the 17 nif-related genes were evaluated by RT-qPCR and LC-MS/MS, respectively. The constructed E. coli showed nitrogenase activity under anaerobic and microaerobic conditions. This strain would be a useful model for examining the effect of other genes from A. vinelandii on nitrogen fixation by expressing them in addition to the minimal set of nif-related genes.

Comparing LIME and SHAP using Synthetic Polygonal Data Clusters

Comparing LIME and SHAP using Synthetic Polygonal Data Clusters

DBWGIE-MR: A density-based clustering algorithm by using the weighted grid and information entropy based on MapReduce

The main target of this paper is to design a density-based clustering algorithm using the weighted grid and information entropy based on MapReduce, noted as DBWGIE-MR, to deal with the problems of unreasonable division of data gridding, low accuracy of clustering results and low efficiency of parallelization in big data clustering algorithm based on density. This algorithm is implemented in three stages: data partitioning, local clustering, and global clustering. For each stage, we propose several strategies to improve the algorithm. In the first stage, based on the spatial distribution of data points, we propose an adaptive division strategy (ADG) to divide the grid adaptively. In the second stage, we design a weighted grid construction strategy (NE) which can strengthen the relevance between grids to improve the accuracy of clustering. Meanwhile, based on the weighted grid and information entropy, we design a density calculation strategy (WGIE) to calculate the density of the grid. And last, to improve the parallel efficiency, core clusters computing algorithm based on MapReduce (COMCORE-MR) are proposed to parallel compute the core clusters of the clustering algorithm. In the third stage, based on disjoint-set, we propose a core cluster merging algorithm (MECORE) to speed-up ratio the convergence of merged local clusters. Furthermore, based on MapReduce, a core clusters parallel merging algorithm (MECORE-MR) is proposed to get the clustering algorithm results faster, which improves the core clusters merging efficiency of the density-based clustering algorithm. We conduct the experiments on four synthetic clusters. Compared with H-DBSCAN, DBSCAN-MR and MR-VDBSCAN, the experimental results show that the DBWGIE-MR algorithm has higher stability and accuracy, and it takes less time in parallel clustering.

Point-group selection rules and universal momentum-transfer dependencies for inelastic neutron scattering on molecular spin clusters

Recent significant progress in inelastic neutron scattering (INS) has rendered this technique even more useful for the characterization of magnetic systems, including molecular spin clusters. By so-called four-dimensional INS on single-crystal probes, excitation spectra can be recorded in large portions of momentum-transfer (Q) and energy-transfer (E) space. Spin-selection rules permit $\mathrm{\ensuremath{\Delta}}S=0,\ifmmode\pm\else\textpm\fi{}1$ transitions between different spin multiplets. Additional selection rules can be imposed by point-group symmetry but were not discussed yet. As most synthetic spin clusters with interesting magnetic properties have high molecular symmetry, a clear understanding of this issue will be helpful for interpreting INS spectra. Here we discuss point-group INS selection rules for magnetically isotropic or anisotropic spin clusters. Rings and a number of spin polyhedra with cubic or icosahedral symmetry are chosen as illustrative and relevant examples. These systems exhibit a significant number of point-group selection rules in isotropic spin models, and most of them maintain a smaller number of selection rules in anisotropic spin models. We also explain how the Q dependence of certain excitations depends exclusively on the point-group symmetry of the states involved in the transition, an aspect that had thus far only been detailed for spin rings. We provide the universal Q-dependent intensity functions (and their powder-averaged forms) for a set of polyhedra (cube, icosahedron, truncated tetrahedron, cuboctahedron, dodecahedron, icosidodecahedron, and truncated icosahedron). Overall, these results help to disentangle the relevant dynamical information contained in INS spectra from those features that are entirely determined by molecular symmetry.

Maximum helium content of multiple populations in the globular cluster NGC 6752

Context. Multiple populations in globular clusters are usually explained by the formation of stars out of material with a chemical composition that is polluted to different degrees by the ejecta of short-lived, massive stars. But the nature of the “polluters” remains elusive. Different types of stars have been proposed to account for the observed chemical patterns of multiple populations. Among other things, these differ by the amount of helium they spread in the surrounding medium. Aims. In this study we investigate whether the present-day photometric method used to infer the helium content of multiple populations indeed gives the true value or underestimates it by missing very He-rich, but rare stars. This check is important to discriminate between the different polluter scenarios. We focus on the specific case of NGC 6752. Methods. We compute atmosphere models and synthetic spectra along isochrones produced for this cluster for a very broad range of He abundances covering the predictions of the different scenarios, including the extreme case of the fast-rotating massive star (FRMS) scenario. We use the same abundances in isochrones and atmosphere models to ensure consistency. We calculate synthetic photometry in HST filters best suited to study the helium content. We subsequently build synthetic clusters with various distributions of stars. We finally determine the maximum helium mass fraction of these synthetic clusters using a method similar to that applied to observational data. In particular, we select nonpolluted and very He-rich stars from the so-called chromosome map. Results. We re-determine the maximum helium mass fraction Y in NGC 6752, and find a value consistent with published results. We build toy models of clusters with various distributions of multiple populations and ensure that we are able to recover the input maximum Y. We then build synthetic clusters with the populations predicted by the FRMS scenario and find that while we slightly underestimate the maximum Y value, we are still able to detect stars much more He-rich than the current observed maximum Y. This result still holds even in synthetic clusters that contain less He-rich stars than predicted by the FRMS scenario. It is easier to determine the maximum Y on main sequence stars than on red giant branch stars, but qualitatively the results are unaffected by the sample choice. Conclusions. We show that in NGC 6752 it is unlikely that stars more He-rich than the current observational limit of about 0.3 (in mass fraction) are present.

PyUPMASK: an improved unsupervised clustering algorithm

Aims.We present pyUPMASK, an unsupervised clustering method for stellar clusters that builds upon the original UPMASK package. The general approach of this method makes it plausible to be applied to analyses that deal with binary classes of any kind as long as the fundamental hypotheses are met. The code is written entirely in Python and is made available through a public repository.Methods.The core of the algorithm follows the method developed in UPMASK but introduces several key enhancements. These enhancements not only make pyUPMASK more general, they also improve its performance considerably.Results.We thoroughly tested the performance of pyUPMASK on 600 synthetic clusters affected by varying degrees of contamination by field stars. To assess the performance, we employed six different statistical metrics that measure the accuracy of probabilistic classification.Conclusions.Our results show that pyUPMASK is better performant than UPMASK for every statistical performance metric, while still managing to be many times faster.

Dinitrogen binding and activation at a molybdenum-iron-sulfur cluster.

The Fe-S clusters of nitrogenases carry out the life-sustaining conversion of N2 to NH3. Although progress continues to be made in modelling the structural features of nitrogenase cofactors, no synthetic Fe-S cluster has been shown to form a well-defined coordination complex with N2. Here we report that embedding an [MoFe3S4] cluster in a protective ligand environment enables N2 binding at Fe. The bridging [MoFe3S4]2(μ-η1:η1-N2) complex thus prepared features a substantially weakened N-N bond despite the relatively high formal oxidation states of the metal centres. Substitution of one of the [MoFe3S4] cubanes with an electropositive Ti metalloradical induces additional charge transfer to the N2 ligand with generation of Fe-N multiple-bond character. Structural and spectroscopic analyses demonstrate that N2 activation is accompanied by shortened Fe-S distances and charge transfer from each Fe site, including those not directly bound to N2. These findings indicate that covalent interactions within the cluster play a critical role in N2 binding and activation.

2Fe-2S] Cluster Supported by Redox-Active o-Phenylenediamide Ligands and Its Application toward Dinitrogen Reduction.

As prevalent cofactors in living organisms, iron-sulfur clusters participate in not only the electron-transfer processes but also the biosynthesis of other cofactors. Many synthetic iron-sulfur clusters have been used in model studies, aiming to mimic their biological functions and to gain mechanistic insight into the related biological systems. The smallest [2Fe-2S] clusters are typically used for one-electron processes because of their limited capacity. Our group is interested in functionalizing small iron-sulfur clusters with redox-active ligands to enhance their electron storage capacity, because such functionalized clusters can potentially mediate multielectron chemical transformations. Herein we report the synthesis, structural characterization, and catalytic activity of a diferric [2Fe-2S] cluster functionalized with two o-phenylenediamide ligands. The electrochemical and chemical reductions of such a cluster revealed rich redox chemistry. The functionalized diferric cluster can store up to four electrons reversibly, where the first two reduction events are ligand-based and the remainder metal-based. The diferric [2Fe-2S] cluster displays catalytic activity toward silylation of dinitrogen, affording up to 88 equiv of the amine product per iron center.

Goodness-of-fit test for isochrone fitting in the Gaia era

Context. The increasing precision in observational data made available by recent surveys means that the reliability of stellar models can be tested. For this purpose, a firm theoretical basis is crucial for evaluating the agreement of the data and theoretical predictions. Aims This paper presents a rigorous derivation of a goodness-of-fit statistics for colour-magnitude diagrams (CMD). We discuss the reliability of the underlying assumptions and their validity in real-world testing. Methods. We derived the distribution of the sum of squared Mahalanobis distances of stellar data and theoretical isochrone for a generic set of data and models. We applied this to the case of synthetic CMDs that were constructed to mimic real data of open clusters in the Gaia sample. Then, we analysed the capability of distinguishing among different sets of input physics and parameters that were used to compute the stellar models. To do this, we generated synthetic clusters from isochrones computed with these perturbed quantities, and we evaluated the goodness-of-fit with respect to the reference unperturbed isochrone. Results. We show that when r magnitudes are available for each of the N observational objects and p hyperparameters are estimated in the fit, the error distribution follows a χ2 distribution with (r − 1)N − p degrees of freedom. We show that the linearisation of the isochrone between support points introduces negligible deviation from this result. We investigated the possibility of detecting the effects on stellar models that are induced when the following physical quantities were varied: convective core overshooting efficiency, 14N(p, γ)15O reaction rate, microscopic diffusion velocities, outer boundary conditions, and colour transformation (bolometric corrections). We conducted the analysis at three different ages, 150 Myr, 1 Gyr, and 7 Gyr, and accounted for errors in photometry from 0.003 mag to 0.03 mag. The results suggest that it is possible to detect the effect induced by only some of the perturbed quantities. The effects induced by a change in the diffusion velocities or in the 14N(p, γ)15O reaction rate are too small to be detected even when the smallest photometric uncertainty is adopted. A variation in the convective core overshooting efficiency was detectable only for photometric errors of 0.003 mag and only for the 1 Gyr case. The effects induced by the outer boundary conditions and the bolometric corrections are the largest; the change in outer boundary conditions is detected for photometric errors below about 0.01 mag, while the variation in bolometric corrections is detectable in the whole photometric error range. As a last exercise, we addressed the validity of the goodness-of-fit statistics for real-world open cluster CMDs, contaminated by field stars or unresolved binaries. We assessed the performance of a data-driven cleaning of observations, aiming to select only single stars in the main sequence from Gaia photometry. This showed that this selection is possible only for a very precise photometry with errors of few millimagnitudes.

An Aromatic Micelle‐Based Saccharide Cluster with Changeable Fluorescent Color and its Protein Interactions

AbstractTo develop a new type of synthetic saccharide clusters with changeable fluorescent colors, we herein designed a multisaccharide‐coated aromatic micelle. The new cluster forms in water through the quantitative assembly of bent polyaromatic amphiphiles bearing three mannose groups. The spherical assembly, with a 2 nm‐sized polyaromatic core and ca. 18 saccharide pendants, is stable even under high dilution conditions (up to 0.02 mM). The emission intensity and color of the saccharide cluster can be altered from moderate blue (ΦF=19 %) to strong red, orange, and green (ΦF up to 67 %) upon encapsulation of hydrophobic fluorescent dyes in water. Moreover, the present fluorescent clusters, both with and without the dyes, display selective interactions with mannose‐binding proteins in vitro.

Further support for a trio of mass-to-light deviations in Abell 370: free-form grale lens inversion using BUFFALO strong lensing data

ABSTRACT We use the Beyond Ultra-deep Frontier Fields and Legacy Observations (BUFFALO) strong lensing image catalogue of the merging galaxy cluster Abell 370 to obtain a mass model using free-form lens inversion algorithm grale. The improvement of the strong lensing data quality results in a lens plane rms of only 0.45 arcsec, about a factor of two lower than that of our existing HFF v4 reconstruction. We attribute the improvement to spectroscopic data and use of the full reprocessed HST mosaics. In our reconstructed mass model, we found indications of three distinct mass features in Abell 370: (i) a ∼35 kpc offset between the northern BCG and the nearest mass peak, (ii) a ∼100 kpc mass concentration of roughly critical density ∼250 kpc east of the main cluster, and (iii) a probable filament-like structure passing N-S through the cluster. While (i) is present in some form in most publicly available reconstructions spanning the range of modelling techniques: parametric, hybrid, and free-form, (ii) and (iii) are recovered by only about half of the reconstructions. We tested our hypothesis on the presence of the filament-like structure by creating a synthetic cluster – Irtysh IIIc – mocking the situation of a cluster with external mass. We also computed the source plane magnification distributions. Using them, we estimated the probabilities of magnifications in the source plane, and scrutinized their redshift dependence. Finally, we explored the lensing effects of Abell 370 on the luminosity functions of sources at zs = 9.0, finding it consistent with published results.

Genome sequencing and comparative genomic analysis of highly and weakly aggressive strains of Sclerotium rolfsii, the causal agent of peanut stem rot

BackgroundStem rot caused by Sclerotium rolfsii is a very important soil-borne disease of peanut. S. rolfsii is a necrotrophic plant pathogenic fungus with an extensive host range and worldwide distribution. It can infect peanut stems, roots, pegs and pods, leading to varied yield losses. S. rolfsii strains GP3 and ZY collected from peanut in different provinces of China exhibited a significant difference in aggressiveness on peanut plants by artificial inoculation test. In this study, de-novo genome sequencing of these two distinct strains was performed aiming to reveal the genomic basis of difference in aggressiveness.ResultsScleotium rolfsii strains GP3 and ZY, with weak and high aggressiveness on peanut plants, exhibited similar growth rate and oxalic acid production in laboratory. The genomes of S. rolfsii strains GP3 and ZY were sequenced by Pacbio long read technology and exhibited 70.51 Mb and 70.61 Mb, with contigs of 27 and 23, and encoded 17,097 and 16,743 gene models, respectively. Comparative genomic analysis revealed that the pathogenicity-related gene repertoires, which might be associated with aggressiveness, differed between GP3 and ZY. There were 58 and 45 unique pathogen-host interaction (PHI) genes in GP3 and ZY, respectively. The ZY strain had more carbohydrate-active enzymes (CAZymes) in its secretome than GP3, especially in the glycoside hydrolase family (GH), the carbohydrate esterase family (CBM), and the polysaccharide lyase family (PL). GP3 and ZY also had different effector candidates and putative secondary metabolite synthetic gene clusters. These results indicated that differences in PHI, secreted CAZymes, effectors and secondary metabolites may play important roles in aggressive difference between these two strains.ConclusionsThe data provided a further understanding of the S. rolfsii genome. Genomic comparison provided clues to the difference in aggressiveness of S. rolfsii strains.

Exploring the hydrostatic mass bias in MUSIC clusters: application to the NIKA2 mock sample

ABSTRACT Clusters of galaxies are useful tools to constrain cosmological parameters, only if their masses can be correctly inferred from observations. In particular, X-ray and Sunyaev–Zeldovich (SZ) effect observations can be used to derive masses within the framework of the hydrostatic equilibrium. Therefore, it is crucial to have a good control of the possible mass biases that can be introduced when this hypothesis is not valid. In this work, we analysed a set of 260 synthetic clusters from the MUSIC simulation project at redshifts 0 ≤ z ≤ 0.82. We estimate the hydrostatic mass of the MUSIC clusters from X-ray only (temperature and density) and from X-ray and SZ (density and pressure). Then, we compare them with the true 3D dynamical mass. The biases are of the order of 20 per cent. We find that using the temperature instead of the pressure leads to a smaller bias, although the two values are compatible within 1σ. Non-thermal contributions to the total pressure support, arising from bulk motion and turbulence of the gas, are also computed and show that they are sufficient to account for this bias. We also present a study of the correlation between the mass bias and the dynamical state of the clusters. A clear correlation is shown between the relaxation state of the clusters and the bias factor. We applied the same analysis on a subsample of 32 objects, already selected for supporting the NIKA2 SZ Large Program.

Comparative Transcriptome Analysis of Streptomyces nodosus Mutant With a High-Yield Amphotericin B.

Antibiotics play an important role in human health. Most antibiotics are derived from microbial secondary metabolites. Amphotericin is a polyene macrolide antibiotic synthesized by Streptomyces nodosus. S. nodosus ZJB2016050 with high-yield amphotericin B (AmB) was obtained by traditional mutagenesis using S. nodosus ATCC14899 as the original strain. The differences in the characterization of the two strains were found in color, mycelium morphology, and AmB yield. Subsequent comparative transcriptome explained the yield differences between the two strains. Pathways including the carbohydrate metabolic pathway and the secondary product synthesis pathway were targeted. The upregulation of glucokinase, phosphoglycerate mutase, and pyruvate dehydrogenase accelerates the consumption of glucose and has great effects on the accumulation of precursors. One of the competitive secondary metabolites of the polyketone synthetase (PKS) II type sapromomycin analog synthesis gene cluster was downregulated, which competes for malonyl-CoA. Five PKS modules (except for the first module amphA) of the amphotericin synthetic gene cluster in the high-yielding strain were downregulated, which resulted in the total amphotericin A (AmA) and AmB of S. nodosus ZJB2016050 being less than that of the wild-type S. nodosus ATCC14899. Combined with gene differential expression in the pentose phosphate pathway and the reaction mechanism of the ER5 domain, the reason that S. nodosus ZJB2016050 preferred to synthesize AmB was probably related to intracellular reduction.

Structure, reactivity, and spectroscopy of nitrogenase-related synthetic and biological clusters.

The reduction of dinitrogen (N2) is essential for its incorporation into nucleic acids and amino acids, which are vital to life on earth. Nitrogenases convert atmospheric dinitrogen to two ammonia molecules (NH3) under ambient conditions. The catalytic active sites of these enzymes (known as FeM-cofactor clusters, where M = Mo, V, Fe) are the sites of N2 binding and activation and have been a source of great interest for chemists for decades. In this review, recent studies on nitrogenase-related synthetic molecular complexes and biological clusters are discussed, with a focus on their reactivity and spectroscopic characterization. The molecular models that are discussed span from simple mononuclear iron complexes to multinuclear iron complexes and heterometallic iron complexes. In addition, recent work on the extracted biological cofactors is discussed. An emphasis is placed on how these studies have contributed towards our understanding of the electronic structure and mechanism of nitrogenases.

Genomics-guided isolation and identification of active secondary metabolites of Bacillus velezensis BA-26

Here we report the discovery and identification of antibacterial substances from the secondary metabolites of a new strain Bacillus velezensis BA-26. The whole genome of B. velezensis BA-26 was sequenced. Its genome data were annotated, and four potentially unknown gene clusters and nine known secondary metabolite synthetic gene clusters were analyzed and excavated by antiSMASH software. Based on the predicted secondary metabolites from the genome of strain BA-26, the material identification of isolated and purified extracellular secondary metabolites was conducted in combination with mass spectrometry data. A total of 24 antifungal compounds were identified, namely, iturin and various fengycins. The minimum inhibitory concentration (MIC) of iturin to Botrytis cinerea was 62.50 µg·mL−1. The MICs of C14 fengycin A, C16 fengycin A and C18 fengycin A were 62.50, 31.25 and 0.49 µg·mL−1, respectively, indicating that the increase in the number of carbon atoms in side chain fatty acids of fengycin improves its biological activity. Two kinds of anti-disease fungal compounds from B. velezensis BA-26, namely, iturin and fengycin, were purified and identified; they were found to have antibacterial activities against common pathogenic fungi. Therefore, B. velezensis BA-26 may be potentially used as a biological control agent. This study enriches the genome information on B. velezensis, elucidates the active components of B. velezensis BA-26 antibacterial substances, and provides a useful reference for using strain BA-26 as a biological control agent. Supplemental data for this article is available online at https://doi.org/10.1080/13102818.2021.1934540 .

Machine Learning Regression Algorithm Predicts Multi-component Crystal Configuration Energy

Some machine learning algorithm tools, such as neural networks and Gaussian process regression, are increasingly being applied to the exploration of materials. Here, we have developed a form to use this nonlinear interpolation tool to describe properties that depend on the degrees of freedom in multi-component solids. A symmetrically adapted clustering function is used to distinguish different atomic order degrees. These features are used as the input of neural networks, Gaussian process regression and other algorithmic models, and some inherent properties of materials, such as formation energy, can be reproduced by the trained machine algorithm model. We use this technique to reproduce the expansion Hamiltonian of a synthetic cluster with multi-body interaction, and calculate the formation energy of ZrO based on first principles. The form proposed in this paper and the results shown that complex multi-body interactions can be approximated by nonlinear models involving smaller clusters. The training models used in this paper to predict energy include neural networks, Gaussian process regression, random forests, and support vectors regression, using MSE and coefficient of determination to evaluate the prediction results, and adding genetic algorithms in the feature selection process can remove some redundant features and improve the prediction efficiency and accuracy. The results show that the neural network is the best algorithm model which selected in this article, the prediction effect of support vector regression is relatively inferior.

Identification of molecular cluster evaporation rates, cluster formation enthalpies and entropies by Monte Carlo method

Abstract. We address the problem of identifying the evaporation rates for neutral molecular clusters from synthetic (computer-simulated) cluster concentrations. We applied Bayesian parameter estimation using a Markov chain Monte Carlo (MCMC) algorithm to determine cluster evaporation/fragmentation rates from synthetic cluster distributions generated by the Atmospheric Cluster Dynamics Code (ACDC) and based on gas kinetic collision rate coefficients and evaporation rates obtained using quantum chemical calculations and detailed balances. The studied system consisted of electrically neutral sulfuric acid and ammonia clusters with up to five of each type of molecules. We then treated the concentrations generated by ACDC as synthetic experimental data. With the assumption that the collision rates are known, we tested two approaches for estimating the evaporation rates from these data. First, we studied a scenario where time-dependent cluster distributions are measured at a single temperature before the system reaches a steady state. In the second scenario, only steady-state cluster distributions are measured but at several temperatures. Additionally, in the latter case, the evaporation rates were represented in terms of cluster formation enthalpies and entropies. This reparameterization reduced the number of unknown parameters, since several evaporation rates depend on the same cluster formation enthalpy and entropy values. We also estimated the evaporation rates using previously published synthetic steady-state cluster concentration data at one temperature and compared our two cases to this setting. Both the time-dependent and the two-temperature steady-state concentration data allowed us to estimate the evaporation rates with less variance than in the steady-state single-temperature case. We show that temperature-dependent steady-state data outperform single-temperature time-dependent data for parameter estimation, even if only two temperatures are used. We can thus conclude that for experimentally determining evaporation rates, cluster distribution measurements at several temperatures are recommended over time-dependent measurements at one temperature.