Research paper

Review

A Critical Review of Machine Learning of Energy Materials. Advanced Energy Materials 2020.
From database to knowledge graph — using data in chemistry, 2019.
Data‐Driven Materials Science: Status, Challenges, and Perspectives. Advanced Science 2019.
New frontiers for the materials genome initiative. npj Computational Materials 2019.
Data-Driven Molecular Engineering of Solar-Powered Windows. Computing in Science & Engineering 2018.
Information Retrieval and Text Mining Technologies for Chemistry. Chemical Reviews 2017.

Creating Training Data for Scientific Named Entity Recognition with Minimal Human Effort. 2019.
Data Descriptor: Machine-learned and codified synthesis parameters of oxide materials
propnet: A Knowledge Graph for Materials Science, Matter, 2019.
ImageDataExtractor: A Tool To Extract 'and Quantify Data from Microscopy Images. Journal of Chemical Information and Modeling 2019.
Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature Journal of Chemical Information and Modeling 2019.
A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction. ACS Central Science 2019.
Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 2019.
Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction. Scientific Data 2018.
Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules. Journal of Cheminformatics 2018.
Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning. Chemistry of Materials 2017.

Text-mined dataset of inorganic materials synthesis recipes. Scientific Data 2019.
Comparative dataset of experimental and computational attributes of UV/vis absorption spectra. Scientific Data 2019.
Machine-learned and codified synthesis parameters of oxide materials. Scientific Data 2017.
Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning, 2017
A picture is worth a thousand words: applying natural language processing tools for creating a quantum materials database map. MRS Communications 2019.

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks. Journal of Chemical Information and Modeling, 2020.
Semi-supervised machine-learning classification of materials synthesis procedures. npj Computational Materials, 2019.
Robocrystallographer: automated crystal structure text descriptions and analysis. MRS Communications 2019.
Machine learning for heterogeneous catalyst design and discovery. AIChE Journal 2018.
Modelling Chemical Reasoning to Predict and Invent Reactions, 2017.