Deep learning of experimental electrochemistry for battery cathodes across diverse compositions

Artificial intelligence (AI) has emerged as a powerful tool in the discovery and optimization of novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. In this study, we present a comprehensive machine-learning model (DRXNet) for battery informatics and demonstrate the application in discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past five years, resulting in a dataset of more than 30,000 discharge voltage profiles with 14 different metal species. Learning from this extensive dataset, our DRXNet model can automatically capture critical features in the cycling curves of DRX cathodes under various conditions. Illustratively, the model gives rational predictions of the discharge capacity for diverse compositions in the Li--Mn--O--F chemical space and high-entropy systems. As a universal model trained on diverse chemistries, our approach offers a data-driven solution to facilitate the rapid identification of novel cathode materials, accelerating the development of next-generation batteries for carbon neutralization

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 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

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

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. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2,654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories.Funding provided by: U.S. Department of EnergyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000015Award Number: DE-AC02-05-CH11231, D2S2 program KCD2S2Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: DMR-192237

Machine Learning Inorganic Solid-state Synthesis from Materials Science Literature

Solid-state synthesis prediction is a key accelerator for the rapid design of advanced inorganic 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. To achieve predictive synthesis for the desired material, one potential approach is to learn synthesis design patterns from a large volume of experimental synthesis procedures. Nevertheless, a comprehensive, large-scale database of structured synthesis procedures for inorganic materials does not exist. Provided the ability of converting unstructured text to structured information, the decades of solid-state chemistry literature constitutes a treasure trove of synthesis data. Therefore, this study aims at: (1) developing natural language processing (NLP) algorithms to text mine a large-scale inorganic synthesis dataset from materials science literature, and (2) developing machine learning algorithms for precursor selection in solid-state synthesis based on the text-mined dataset.Although many general-purpose NLP methods exist, text mining for inorganic synthesis requires dedicated development of models for information retrieval (Chapter 2). During the development of a text-mining pipeline, 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. By integrating our information retrieval model for precursors and targets, and also the ones for other synthesis variables, we established a fully automated text-mining pipeline that extracts the structured data of synthesis procedures from the literature. Starting from 4,973,165 materials science papers, we applied our text-mining pipeline and successfully extracted 33,343 solid-state synthesis procedures. The quality of the text-mined synthesis dataset is validated by the high accuracy of 93% at the chemistry level, where each extracted reaction has the target and precursor materials consistent with the original literature report. This dataset for inorganic solid-state synthesis is currently the largest of its kind and paves the way toward the development of data-driven approaches for rational synthesis design.Using the extracted data, we conducted a meta-analysis to study the similarities and differences between precursors in the context of solid-state synthesis (Chapter 3). 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 the "chemical similarity" of precursors can be extracted from text data, without the need to include any explicit domain knowledge. Quantifying the similarity of precursors offers a reference for suggesting candidate reactants when researchers alter existing recipes by replacing precursors. The capability of creating alternative recipes constitutes an important step toward developing a predictive synthesis model.While the selection of alternative precursors is enabled by the similarity of precursors, it is limited to existing materials. To learn which precursors to recommend for the synthesis of a novel target material, we further developed a representation learning model to evaluate the similarity of targets (Chapter 4). The data-driven approach learns "chemical similarity" of target materials and refers the synthesis of a new target to precedent synthesis procedures of similar target materials, mimicking human synthesis design. When proposing five precursor sets for each of 2,654 unseen test target materials, our recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories. Overall, this study contributes a valuable large-scale synthesis dataset and interpretable precursor selection algorithms to the materials science community, representing a step forward in the prediction of solid-state synthesis

On the controlling mechanism of the upper turnover states in the NTC regime

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

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. To understand how double bonds affect the fate of peroxy radicals, we selected three representative peroxy radicals from heptane, heptene, and heptadiene having zero, one, and two double CC bonds, respectively, for study. The potential energy surfaces were explored at the CBS-QB3 level, and the reaction rate constants were computed using canonical/variational transition state theories. We have found that the double bond is responsible for the very different bond dissociation energies of the various types of C–H bonds, which in turn affect significantly the reaction kinetics of alkenylperoxy radicals

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 applying machine learning to the ab-initio computed phase stability of 3881 potential NASICONs we can extract a simple two-dimensional descriptor that is extremely good at separating stable from unstable NASICONS. This machine-learned "tolerance factor" contains information on the Na content, the radii and electronegativities of the elements, and the Madelung energy. We test the predictive capability of this approach by selecting six predicted NASICON compositions. Five out of the six resulted in a phase pure NASICON while the sixth composition led to a NASICON that coexisted with other phases, validating the efficacy of this approach. This work not only provide tools to understand synthetic accessibility of NASICON-type materials, but also demonstrate an efficient paradigm for discovering new materials with complicate composition and atomic structure

Opportunities and challenges of text mining in materials research

© 2021 The Author(s) 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

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. 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