UC Berkeley

Innovations of novel materials often involve synthesizing new compounds with better materials properties. However, computationally designing synthesis methods for these new compounds remains an uncharted new area of research. This thesis proposes to use machine-learning approaches to predict materials synthesis routes by training on synthesis information from the published scientific literature. However, most inorganic materials synthesis information in the scientific literature is locked-up in written natural language and must be parsed using natural language processing and information retrieval techniques. Therefore, this thesis aims to achieve two objectives: 1) constructing a text-mining pipeline that extracts solid-state synthesis datasets from scientific papers, and 2) implementing an interpretable machine-learning method to predict solid-state synthesis conditions.

Training information retrieval systems usually requires large manually labeled datasets, which are not widely available in materials informatics. To alleviate the lack of labeled datasets, we demonstrate a semi-supervised machine-learning method (Chapter 3), which is implemented for the classification of paragraphs in papers. Without any human labeling efforts, latent Dirichlet allocation can cluster keywords into topics corresponding to specific experimental synthesis steps. Guided by a small amount of annotation, supervised training methods, such as random forest, can then associate these steps with different synthesis methods, such as solid-state or hydrothermal synthesis. Using the topic modeling results, we also show a Markov chain representation of the order of experimental steps, which reconstructs a flowchart of synthesis procedures.

To fulfill the first objective, we have extracted a dataset of "codified recipes" for solid-state synthesis using an automated text-mining pipeline (Chapter 4). The dataset currently consists of over 30,000 solid-state synthesis entries. Every entry contains synthesis information including input materials, target materials, experimental operations, the associated processing parameters and synthesis conditions, and the balanced synthesis reaction equation. This dataset is the first-ever collection of machine-readable solid-state synthesis experiments and enables data mining of various aspects of inorganic materials synthesis.

To fulfill the second objective, we have built a machine-learning approach that predicts solid-state synthesis conditions (heating temperature and heating time) using the above-mentioned dataset (Chapter 5). We used dominance importance ranking analysis and discovered that optimal heating temperatures have strong correlations with the stability of precursor materials. This correlation extends Tamman's rule from intermetallics to oxide systems, suggesting the importance of reaction kinetics in solid-state synthesis. Heating times are shown to be strongly correlated with the chosen experimental procedures and instrument setups, which may be indicative of the selection bias in the dataset. Our machine-learning models achieve good synthesis prediction performance and general applicability for diverse chemical systems.

While focusing particularly on solid-state synthesis, this thesis demonstrates a scalable framework to unlock the large amount of inorganic materials synthesis information from the literature, and machine-learn robust and interpretable synthesis predictors. At the end of this thesis, we outline several interesting future research topics which expand the work into a broader context of materials informatics and synthesis science.