7 citations · 14 across the 9 of their papers we have counts for
9 papers
In context learning Foundation models for Materials Property Prediction with Small datasets
Qinyang Li, Rongzhi Dong, Nicholas Miklaucic +6
Foundation models (FMs) have recently shown remarkable in-context learning (ICL) capabilities across diverse scientific domains. In this work, we introduce a unified in-context lea…
Data-Driven Topological Analysis of Polymorphic Crystal Structures
Sourin Dey, Nicholas Miklaucic, Sadman Sadeed Omee +5
Polymorphism, the ability of a compound to crystallize in multiple distinct structures, plays a vital role in determining the physical, chemical, and functional properties of mater…
Out-of-distribution materials property prediction using adversarial learning based fine-tuning
Qinyang Li, Nicholas Miklaucic, Jianjun Hu
The accurate prediction of material properties is crucial in a wide range of scientific and engineering disciplines. Machine learning (ML) has advanced the state of the art in this…
Deep Learning-Based Classification of Gamma Photon Interactions in Room-Temperature Semiconductor Radiation Detectors
Sandeep K. Chaudhuri, Qinyang Li, Krishna C. Mandal +1
Photon counting radiation detectors have become an integral part of medical imaging modalities such as Positron Emission Tomography or Computed Tomography. One of the most promisin…
Global mapping of structures and properties of crystal materials
Qinyang Li, Rongzhi Dong, Nihang Fu +3
Understanding material composition-structure-function relationships is of critical importance for the design and discovery of novel functional materials. While most such studies fo…
Materials Transformers Language Models for Generative Materials Design: a benchmark study
Nihang Fu, Lai Wei, Yuqi Song +6
Pre-trained transformer language models on large unlabeled corpus have produced state-of-the-art results in natural language processing, organic molecule design, and protein sequen…
Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials
Lai Wei, Qinyang Li, Yuqi Song +4
Self-supervised neural language models have recently achieved unprecedented success, from natural language processing to learning the languages of biological sequences and organic…
DeepXRD, a Deep Learning Model for Predicting of XRD spectrum from Materials Composition
Rongzhi Dong, Yong Zhao, Yuqi Song +6
One of the long-standing problems in materials science is how to predict a material's structure and then its properties given only its composition. Experimental characterization of…
Scalable deeper graph neural networks for high-performance materials property prediction
Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu +5
Machine learning (ML) based materials discovery has emerged as one of the most promising approaches for breakthroughs in materials science. While heuristic knowledge based descript…