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A Unified Language of Synthesis Actions (ULSA) for describing inorganic synthesis is developed to help create a synthesis ontology and a solid foundation for autonomous robotic synthesis.

Thermodynamics has strong predictive power for materials synthesis by identifying the stability region of target phases, but does not give explicit information about the relative competitiveness of undesired byproduct phases in synthesis. In this work, we propose a quantitative and computable measure to guide the selection of synthesis conditions. Defining thermodynamic competition as the difference in driving force between a target phase and its competing phases, we hypothesize that phase-pure synthesis becomes more likely when this thermodynamic competition is minimized. We systematically validate this hypothesis with two approaches: (1) we analyze large-scale solution synthesis procedures as text-mined from the literature, and show that experimentally-optimized synthesis conditions are near the predicted thermodynamic optimum point, and (2) direct experimental evaluation of synthesis in LiIn(IO3)4 and LiFePO4, which show that phase-pure synthesis occurs only when thermodynamic competition is minimized. Our work demonstrates that a quantitative assessment of thermodynamic competition is an effective descriptor for synthesis optimization and a promising tool for optimizing aqueous solution-based experimental synthesis conditions.

There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materials but also complicates the interpretation and understanding of solid-state reaction mechanisms. Here, we demonstrate a machine-learning approach that predicts synthesis conditions using large solid-state synthesis datasets text-mined from scientific journal articles. Using feature importance ranking analysis, we discovered that optimal heating temperatures have strong correlations with the stability of precursor materials quantified using melting points and formation energies ($\Delta G_f$, $\Delta H_f$). In contrast, features derived from the thermodynamics of synthesis-related reactions did not directly correlate to the chosen heating temperatures. This correlation between optimal solid-state heating temperature and precursor stability extends Tamman's rule from intermetallics to oxide systems, suggesting the importance of reaction kinetics in determining synthesis conditions. Heating times are shown to be strongly correlated with the chosen experimental procedures and instrument setups, which may be indicative of human bias in the dataset. Using these predictive features, we constructed machine-learning models with good performance and general applicability to predict the conditions required to synthesize diverse chemical systems. Codes and data used in this work can be found at: https://github.com/CederGroupHub/s4.

In this paper we develop the stability rules for NASICON-structured materials, as an example of compounds with complex bond topology and composition. By first-principles high-throughput computation of 3881 potential NASICON phases, we have developed guiding stability rules of NASICON and validated the ab initio predictive capability through the synthesis of six attempted materials, five of which were successful. A simple two-dimensional descriptor for predicting NASICON stability was extracted with sure independence screening and machine learned ranking, which classifies NASICON phases in terms of their synthetic accessibility. This machine-learned tolerance factor is based on the Na content, elemental radii and electronegativities, and the Madelung energy and can offer reasonable accuracy for separating stable and unstable NASICONs. This work will not only provide tools to understand the synthetic accessibility of NASICON-type materials, but also demonstrates an efficient paradigm for discovering new materials with complicated composition and atomic structure., Sodium super ionic conductors are a class of promising energy storage materials owing to their high ionic conductivity. Here, the authors conducted high throughput calculations to understand the synthetic accessibility of NASICON-type materials and demonstrate an efficient paradigm for discovering new complex materials.

Polyoxymethylene dimethyl ethers (PODE) have higher cetane number and oxygen content than mostly used oxygenated fuels. Thus, PODE are believed to be a promising fuel substitute for compression ignition engines and have been intensively studied in compression ignition engines. In recent 5 years, due to the technical breakthrough for industrialized production of PODE in China, PODE property measurement, synthesis technology and application in compression ignition engines have been investigated extensively. This article summarizes the findings concerning PODE synthesis mainly in China and PODE application in compression ignition engines around the world. The measuring results of properties of PODE with different polymerization degrees and effects of PODE on the properties of PODE/diesel blends are integrated. Then, the present synthesis technologies which have realized industrialized production and the potential synthesis technologies in future are discussed. Furthermore, the results of application of PODE in the conventional diesel combustion mode and new compression modes are discussed. Finally, the preliminary findings of PODE oxidation mechanism are demonstrated. In all the polymers of PODE, PODE4–5 exhibit better properties for engine application based on measuring data. Blending PODE into diesel increases fuel cetane number moderately. PODE have been proved to improve combustion and emission characteristics both in conventional and new compression ignition modes in most of the experimental investigations. The absence of C C bond in the molecule structure, avoiding the pathways of the formation of alkenes, provides more potential in reducing soot emissions. Although significant achievements have been achieved in the synthesis of PODE, realization of PODE synthesis from methanol/formaldehyde still need to be investigated.

Research publications are the major repository of scientific knowledge. However, their unstructured and highly heterogenous format creates a significant obstacle to large-scale analysis of the information contained within. Recent progress in natural language processing (NLP) has provided a variety of tools for high-quality information extraction from unstructured text. These tools are primarily trained on non-technical text and struggle to produce accurate results when applied to scientific text, involving specific technical terminology. During the last years, significant efforts in information retrieval have been made for biomedical and biochemical publications. For materials science, text mining (TM) methodology is still at the dawn of its development. In this review, we survey the recent progress in creating and applying TM and NLP approaches to materials science field. This review is directed at the broad class of researchers aiming to learn the fundamentals of TM as applied to the materials science publications., Graphical Abstract, Data Analysis; Computing Methodology; Computational Materials Science; Materials Design

Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one major problem is the difficulty of identifying which materials from a synthesis paragraph are precursors or are target materials. In this study, we developed a two-step Chemical Named Entity Recognition (CNER) model to identify precursors and targets, based on information from the context around material entities. Using the extracted data, we conducted a meta-analysis to study the similarities and differences between precursors in the context of solid-state synthesis. To quantify precursor similarity, we built a substitution model to calculate the viability of substituting one precursor with another while retaining the target. From a hierarchical clustering of the precursors, we demonstrate that "chemical similarity" of precursors can be extracted from text data. Quantifying the similarity of precursors helps provide a foundation for suggesting candidate reactants in a predictive synthesis model., Comment: Chemistry of Materials (2020)

Polyoxymethylene Dimethyl Ether (PODEn, n = 3–5) is a potential alternative fuel or a green fuel additive for diesel engines, owing to the unique C–O alternating chain structure (CH3O[CH2O]nCH3) and high cetane number. In this paper, the rate constants for five reaction classes about the model molecule PODE1, including hydrogen abstraction, beta-scission of corresponding R radicals, isomerization of RO2 radicals, and decomposition of QOOH radicals, were calculated with canonical transition state theory (CTST) at the CCSD(T)-F12a/aug-cc-pVTZ//M06-2X/def2-TZVPP or CBS-QB3//B3LYP/CBSB7 level of theory. Based on the quantum chemistry and chemical kinetic study of the model molecule PODE1, the first detailed reaction mechanism (225 species, 1082 reactions) which can describe the ignition characteristics of PODE3 at low and intermediate temperature was developed. To validate this mechanism, rapid compression machine (RCM) was used to conduct the quasi-homogeneous experiments to measure the total ignition delay time at various effective temperatures (640–865 K) for three different PODE3/O2/N2 mixtures (ϕ = 0.5, O2:N2 = 1:8; ϕ = 1.0, O2:N2 = 1:15; ϕ = 1.5, O2:N2 = 1:20) and two different effective pressures (10 bar, 15 bar). Homogeneous Charge Compression Ignition (HCCI) experiments fueled with PODEn (n = 1–4) mixture, in which the mass fraction of PODE3 is 88.9% were also conducted in a naturally aspirated single-cylinder HCCI research engine at 1600 r/min, two exhaust gas recirculation (EGR) rates (0%, 42%), and three different charge-mass equivalence ratios for each EGR rate to take the real engine working condition into consideration. Good agreement was achieved in the comparison of the experimental data and the simulation results utilizing our newly developed mechanism for PODE3. This surrogate model will contribute to the design of fuels blended with PODEn, and to the prediction of the combustion and emission characteristics of engines using PODEn.

When considering hindered internal rotation, we usually have several options, including (i) single structure harmonic oscillator (SS-HO) approximation that considers the lowest-energy conformer only and approximates all molecular vibrations as harmonic oscillations, (ii) one-dimensional (1-D) internal rotation treatment that replaces the corresponding vibrational mode with one-dimensional torsion, and (iii) the multistructural method with torsional anharmonicity (MS-T) that considers the multiple-structure and torsional anharmonicity. These methods differ greatly in computational cost and accuracy. To evaluate the effect of different treatments on predicting thermodynamic properties, we calculated enthalpy, entropy, and heat capacity for a series of normal and branched alkanes using six different methods, including the SS-HO treatment, three 1-D methods, the MS-T method, and the group additivity (GA) method. The comparison of the computational results with experimental data shows that GA and two 1-D methods proposed in this study are more suitable for reliable and rapid predictions of thermodynamic properties for large hydrocarbons with many carbon–carbon single bonds.

Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of “codified recipes” for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis., Measurement(s)solid-state synthesis dataTechnology Type(s)natural language processing Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9906608

Branched alkanes represent an important class of compounds in conventional fuels and some bio-derived fuels. This study is dedicated to the investigation of the low-temperature oxidation chemistry of 2,6-dimethylheptane using a combination of experimental and computational methods. All the reactants, transition states, and products in the first oxidation stage, which are crucial to the initiation reactions in the low-temperature reaction chain, were optimized through the B3LYP/CBSB7 level of theory and a kinetic mechanism that included the new reaction pathways was assembled. Ignition delay time measurements were carried out in a rapid compression machine and the results were compared with modeling predictions. The kinetic mechanism is able to capture both the first and total ignition delay times with a root-mean-square deviation of 39.6%. In addition, sensitivity analysis is performed to quantify the impact of newly developed chemistry of 2,6-dimethylheptane on ignition delay time. Rate parameters found in this study may be applicable to other branched alkanes with similar molecular structure.

The existence of the first-stage ignition delay (FID) negative temperature coefficient (NTC) behavior was confirmed by rapid compression machine experiments using iso-octane and methyl-cyclohexane. The first-stage NTC behavior of iso-octane is observed in the temperature range 757–782 K under 20 bar and φ = 1. For methyl-cyclohexane, the observed first-stage NTC temperature range is 750–785 K under 15 bar and φ = 0.5. In further iso-octane experiments, the FID is found to be sensitive to the O2 concentration and insensitive to the dilution gas and fuel concentrations. The effects of the FID and its NTC behavior on the total ignition delay NTC were analyzed using a detailed n-heptane mechanism. The contributions to the total ignition delay NTC from the reduced second-stage initial temperature, pressure, and less reactive species pool, together with the NTC of FID were discussed quantitatively. For the first-stage NTC behavior, five competing reactions were identified as being important based on sensitivity analysis, reaction pathway analysis, and simplified mechanism method. They are the backward reaction of second O2 addition, RO2 ⇔ alkene + HO2, QOOH ⇔ cyclic-ether + OH, QOOH ⇔ alkene + HO2, and the beta scission reaction of the alkyl radical. Their competition with the low-temperature branching channel finally leads to the first-stage NTC behavior.

Using n-butane, n-heptane and iso-octane as representative fuels exhibiting NTC (negative temperature coefficient) behavior, comprehensive computational studies with detailed mechanisms and theoretical analysis were performed to investigate the upper stationary point, denoted as turnover states, on the NTC curve near the higher temperature regime, where the ignition delay τ exhibits a local maximum. It is found that the global behavior of the turnover states exhibits distinctive thermodynamic and kinetic characteristics under different pressures, in that the ignition delay at the turnover states shows an Arrhenius dependence on the temperature T and an approximate inverse quadratic power law dependence on the pressure P. These global behaviors imply that the temperature and pressure of the turnover states are not independent and can be correlated by Arrhenius dependence, as ln P ∝ 1/T. Further theoretical analyses demonstrate that such turnover states result from the competition between the low-temperature chain branching reactions and the decomposition of the intermediate species, and therefore correspond to a critical value, α, of the ratio of OH production from low-temperature chemistry. In addition, the ignition delay at the turnover state can be well correlated by the analytical expression derived by Peters et al., with the further demonstration that the pressure dependence of the turnover ignition delay mainly result from the H2O2 decomposition reaction. Comparison of the present results with the literature experimental data of n-heptane ignition delay time shows very good agreement.

In the search for an accurate yet inexpensive method to predict thermodynamic properties of large hydrocarbon molecules, we have developed an automatic and adaptive distance-based group contribution (DBGC) method. The method characterizes the group interaction within a molecule with an exponential decay function of the group-to-group distance, defined as the number of bonds between the groups. A database containing the molecular bonding information and the standard enthalpy of formation (Hf,298K) for alkanes, alkenes, and their radicals at the M06-2X/def2-TZVP//B3LYP/6-31G(d) level of theory was constructed. Multiple linear regression (MLR) and artificial neural network (ANN) fitting were used to obtain the contributions from individual groups and group interactions for further predictions. Compared with the conventional group additivity (GA) method, the DBGC method predicts Hf,298K for alkanes more accurately using the same training sets. Particularly for some highly branched large hydrocarbons, the discrepancy with the literature data is smaller for the DBGC method than the conventional GA method. When extended to other molecular classes, including alkenes and radicals, the overall accuracy level of this new method is still satisfactory.

This work presents the time histories of intermediate species during the auto-ignition of iso -butanol at high pressure and intermediate temperature conditions obtained using a rapid compression machine and recently developed fast sampling system. Iso -butanol ignition delays were acquired for iso -butanol/O 2 mixture with an inert/O 2 ratio of 7.26, equivalence ratio of 0.4, in the temperature range of 840–950 K and at pressure of 25 bar. Fast sampling and gas chromatography were used to acquire and quantify the intermediate species during the ignition delay of the same mixture at P = 25.3 bar and T = 905 K. The ignition delay times and quantitative measurements of the mole fraction time histories of methane, ethene, propene, iso -butene, iso -butyraldehyde, iso -butanol, and carbon monoxide were compared with predictions from the detailed mechanisms developed by Sarathy et al., Merchant et al., and Cai et al. It is shown that while the Sarathy mechanism well predicts the overall ignition delay time, it overpredicts ethene by a factor of 6–10, underpredicts iso -butene by a factor of 2, and overpredicts iso -butyraldehyde by a factor of 2. Reaction path and sensitivity analyses were carried out to identify the reactions responsible for the observed inadequacy. The rates of iso -butanol hydrogen atom abstraction by OH radical and the beta-scission reactions of hydroxybutyl radicals were updated based on recently published quantum calculation results. Significant improvements were achieved in predicting ignition delay at high pressures (25 and 30 bar) and the species concentrations of ethene and iso -butene. However, the updated mechanism still overpredicts iso -butyraldehyde concentrations. Also, the updated mechanism degrades the prediction in ignition delay at lower pressure (15 bar) compared to the original mechanism developed by Sarathy et al.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.