NLP for Materials Science

Introduction

Data have always been a fundamental ingredient for scientific discovery. In materials science, large amounts and heterogeneous data are being produced every day, such as scientific publications, lab reports, manuals, tables, and so on. The different materials classes and materials properties that are of interest or explored lead to data that range many orders of magnitude.

To accelerate material discovery (and also scientific discovery in other domains), we need to transform large and heterogeneous data into a form readily consumed and mined. By coupling such data with domain expertise, we can build upon previous findings, rapidly enter a new field, connect individual research efforts, link across disciplines, and accelerate discovery. Natural language processing (NLP) is therefore playing a key role in understanding and unlocking the rich datasets in material science, especially for understanding scientific literature and extracting useful information from them. Capturing unstructured information from the vast and evergrowing number of scientific publications has substantial promise to enable the creation of experimental-based databases currently lacking and meet the various needs in the materials domain. Developing data mining techniques for material science literature may also prevent information loss. Without structuring information, scientists cannot make the necessary connections among findings.

However, directly applying NLP techniques developed in the general domain to the material science domain cannot give us satisfactory performance in different tasks. The reasons include but are not limited to the following: i) the content and style of material science literature are different from general domain texts such as news articles, which leads to degraded performance for NLP tasks; ii) understanding the literature requires significant in-domain expert knowledge; and iii) we lack high-quality and large-scale labeled training datasets for NLP tasks in the material science domain. It's also challenging and expensive to create new datasets.

In this research project, we aim to develop effective NLP techniques for materials discovery by mapping current NLP techniques to the domain of material discovery or developing a new algorithmic pipeline.

Relevant Materials

Publications

Open-Source Resources

News Media