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Found 9 papers, 4 papers with code
Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-3
To that end, we present an approach using the powerful GPT-3 language model to extract structured multi-step seed-mediated growth procedures and outcomes for gold nanorods from unstructured scientific text.
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials.
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.
Structured information extraction from complex scientific text with fine-tuned large language models
Here, we present a simple sequence-to-sequence approach to joint named entity recognition and relation extraction for complex hierarchical information in scientific text.
ULSA: Unified Language of Synthesis Actions for Representation of Synthesis Protocols
This work is an important step towards creating a synthesis ontology and a solid foundation for autonomous robotic synthesis.
A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra
Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra.
COVIDScholar: An automated COVID-19 research aggregation and analysis platform
This has created a challenge to traditional methods of engagement with the research literature; the volume of new research is far beyond the ability of any human to read, and the urgency of response has lead to an increasingly prominent role for pre-print servers and a diffusion of relevant research across sources.
A critical examination of compound stability predictions from machine-learned formation energies
By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85, 014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids.
Materials Science Computational Physics
A Map of the Inorganic Ternary Metal Nitrides
Exploratory synthesis in novel chemical spaces is the essence of solid-state chemistry.
Materials Science
