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Found 11 papers, 10 papers with code
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
In real-world material research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials.
Generative Design of inorganic compounds using deep diffusion language models
Based on the DFT calculation results, six new materials, including Ti2HfO5, TaNbP, YMoN2, TaReO4, HfTiO2, and HfMnO2, with formation energy less than zero have been found.
MD-HIT: Machine learning for materials property prediction with dataset redundancy control
This issue is well known in the field of bioinformatics for protein function prediction, in which a redundancy reduction procedure (CD-Hit) is always applied to reduce the sample redundancy by ensuring no pair of samples has a sequence similarity greater than a given threshold.
Composition based oxidation state prediction of materials using deep learning
This work proposes a novel deep learning based BERT transformer language model BERTOS for predicting the oxidation states of all elements of inorganic compounds given only their chemical composition.
Probabilistic Generative Transformer Language models for Generative Design of Molecules
Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction.
Materials Transformers Language Models for Generative Materials Design: a benchmark study
We also find that the properties of the generated samples can be tailored by training the models with selected training sets such as high-bandgap materials.
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints
Discovering new materials is a challenging task in materials science crucial to the progress of human society.
Semi-supervised teacher-student deep neural network for materials discovery
For synthesizability prediction, our model significantly increases the baseline PU learning's true positive rate from 87. 9\% to 97. 9\% using 1/49 model parameters.
Scalable deeper graph neural networks for high-performance materials property prediction
Machine learning (ML) based materials discovery has emerged as one of the most promising approaches for breakthroughs in materials science.
Invariant Representation Learning for Infant Pose Estimation with Small Data
However, while the applications of human pose estimation have become more and more broad, models trained on large-scale adult pose datasets are barely successful in estimating infant poses due to the significant differences in their body ratio and the versatility of their poses.
Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions
Computer vision (CV) has achieved great success in interpreting semantic meanings from images, yet CV algorithms can be brittle for tasks with adverse vision conditions and the ones suffering from data/label pair limitation.
