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Found 15 papers, 6 papers with code
AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
Computational prediction of stable crystal structures has a profound impact on the large-scale discovery of novel functional materials.
Towards Imbalanced Large Scale Multi-label Classification with Partially Annotated Labels
In this work, we address the issue of label imbalance and investigate how to train classifiers using partial labels in large labeling spaces.
D-Score: A White-Box Diagnosis Score for CNNs Based on Mutation Operators
However, concerns have been raised with respect to the trustworthiness of these models: The standard testing method evaluates the performance of a model on a test set, while low-quality and insufficient test sets can lead to unreliable evaluation results, which can have unforeseeable consequences.
Discovery of 2D materials using Transformer Network based Generative Design
Recently, deep learning, data-mining, and density functional theory (DFT)-based high-throughput calculations are widely performed to discover potential new materials for diverse applications.
An Effective Approach for Multi-label Classification with Missing Labels
Through comprehensive experiments on three large-scale multi-label image datasets, i. e. MS-COCO, NUS-WIDE, and Pascal VOC12, we show that our method can handle the imbalance between positive labels and negative labels, while still outperforming existing missing-label learning approaches in most cases, and in some cases even approaches with fully labeled datasets.
Depth Monocular Estimation with Attention-based Encoder-Decoder Network from Single Image
Depth information is the foundation of perception, essential for autonomous driving, robotics, and other source-constrained applications.
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.
Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials
Here we propose BLMM Crystal Transformer, a neural network based probabilistic generative model for generative and tinkering design of inorganic materials.
MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art
The availability and easy access of large scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for materials property prediction, structure prediction, and generative design of materials.
Active learning based generative design for the discovery of wide bandgap materials
Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model.
AlphaCrystal: Contact map based crystal structure prediction using deep learning
To our knowledge, AlphaCrystal is the first neural network based algorithm for crystal structure contact map prediction and the first method for directly reconstructing crystal structures from materials composition, which can be further optimized by DFT calculations.
Protein Structure Prediction
Materials Science
Computational discovery of new 2D materials using deep learning generative models
Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties.
Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction
Machine learning (ML) methods have gained increasing popularity in exploring and developing new materials.
Machine Learning based prediction of noncentrosymmetric crystal materials
Noncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems, quantum computing, cybersecurity, and etc.
