Selective formation of metastable polymorphs in solid-state synthesis.

Metastable polymorphs often result from the interplay between thermodynamics and kinetics. Despite advances in predictive synthesis for solution-based techniques, there remains a lack of methods to design solid-state reactions targeting metastable materials. Here, we introduce a theoretical framework to predict and control polymorph selectivity in solid-state reactions.

Selective Synthesis of Defect-Rich LaMnO by Low-Temperature Anion Cometathesis.

The synthesis of complex oxides at low temperatures brings forward aspects of chemistry not typically considered. This study focuses on perovskite LaMnO, which is of interest for its correlated electronic behavior tied to the oxidation state and thus the spin configuration of manganese. Traditional equilibrium synthesis of these materials typically requires synthesis reaction temperatures in excess of 1000 °C, followed by subsequent annealing steps at lower temperatures and different (O) conditions to manipulate the oxygen content postsynthesis (e.

Design principles for NASICON super-ionic conductors.

Na Super Ionic Conductor (NASICON) materials are an important class of solid-state electrolytes owing to their high ionic conductivity and superior chemical and electrochemical stability. In this paper, we combine first-principles calculations, experimental synthesis and testing, and natural language-driven text-mined historical data on NASICON ionic conductivity to achieve clear insights into how chemical composition influences the Na-ion conductivity. These insights, together with a high-throughput first-principles analysis of the compositional space over which NASICONs are expected to be stable, lead to the successful synthesis and electrochemical investigation of several new NASICONs solid-state conductors.

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature.

Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material.

COVIDScholar: An automated COVID-19 research aggregation and analysis platform.

The ongoing COVID-19 pandemic produced far-reaching effects throughout society, and science is no exception. The scale, speed, and breadth of the scientific community's COVID-19 response lead to the emergence of new research at the remarkable rate of more than 250 papers published per day. This posed a challenge for the scientific community as traditional methods of engagement with the literature were strained by the volume of new research being produced.

Machine-Learning Rationalization and Prediction of Solid-State 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 data sets text-mined from scientific journal articles.

Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities.

Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles. Many heuristic methods for controlling the morphological characteristics of gold nanoparticles are well known. However, the underlying mechanisms controlling their size and shape remain poorly understood, partly due to the immense range of possible combinations of synthesis parameters.

Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature.

The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations.

Synthetic accessibility and stability rules of NASICONs.

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.

Opportunities and challenges of text mining in aterials research.

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.

Author Correction: Text-mined dataset of inorganic materials synthesis recipes.

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

Text-mined dataset of inorganic materials synthesis recipes.

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.

Effect of hindered internal rotation treatments on predicting the thermodynamic properties of alkanes.

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.

An adaptive distance-based group contribution method for thermodynamic property prediction.

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.

Theoretical Analysis of the Effect of C═C Double Bonds on the Low-Temperature Reactivity of Alkenylperoxy Radicals.

Biodiesel contains a large proportion of unsaturated fatty acid methyl esters. Its combustion characteristics, especially its ignition behavior at low temperatures, have been greatly affected by these C═C double bonds. In this work, we performed a theoretical analysis of the effect of C═C double bonds on the low-temperature reactivity of alkenylperoxy radicals, the key intermediates from the low-temperature combustion of biodiesel.