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Found 7 papers, 2 papers with code
MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text
Material science synthesis procedures are a promising domain for scientific NLP, as proper modeling of these recipes could provide insight into new ways of creating materials.
Deep Reinforcement Learning for Inverse Inorganic Materials Design
A major obstacle to the realization of novel inorganic materials with desirable properties is the inability to perform efficient optimization across both materials properties and synthesis of those materials.
Augmenting Scientific Creativity with Retrieval across Knowledge Domains
Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas.
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text.
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Leveraging new data sources is a key step in accelerating the pace of materials design and discovery.
Graph similarity drives zeolite diffusionless transformations and intergrowth
Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis.
Graph Similarity
Materials Science
Automatically Extracting Action Graphs from Materials Science Synthesis Procedures
In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds.
