Search Results for author:
Found 11 papers, 5 papers with code
GPCR-BERT: Interpreting Sequential Design of G Protein Coupled Receptors Using Protein Language Models
In this paper, we developed the GPCR-BERT model for understanding the sequential design of G Protein-Coupled Receptors (GPCRs).
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction
A text based description of 326000 molecules were collected using ChatGPT and used to train LLM to learn the representation of molecules.
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction
We extend this paradigm by utilizing LLMs for material property prediction by introducing our model Materials Informatics Transformer (MatInFormer).
MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction
Furthermore, we revealed that MOFormer can be more data-efficient on quantum-chemical property prediction than structure-based CGCNN when training data is limited.
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction
Machine learning (ML) models have been widely successful in the prediction of material properties.
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast
On most benchmarks, the generic GNN pre-trained by iMolCLR rivals or even surpasses supervised learning models with sophisticated architecture designs and engineered features.
AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning
Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures.
FaultNet: A Deep Convolutional Neural Network for bearing fault classification
In this work, we analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults.
Ranked #2 on
Classification
on CWRU Bearing Dataset
(using extra training data)
W-Net: Dense Semantic Segmentation of Subcutaneous Tissue in Ultrasound Images by Expanding U-Net to Incorporate Ultrasound RF Waveform Data
To the best of our knowledge, this is also the first deep-learning or CNN approach for segmentation that analyses ultrasound raw RF data along with the gray image.
Orbital Graph Convolutional Neural Network for Material Property Prediction
We benchmarked the performance of the OGCNN model with that of: 1) the crystal graph convolutional neural network (CGCNN), 2) other state-of-the-art descriptors for material representations including Many-body Tensor Representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), and 3) other conventional regression machine learning algorithms where different crystal featurization methods have been used.
Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning
The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody.
