Search Results for author:
Found 33 papers, 16 papers with code
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.
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.
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.
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.
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.
NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique
While there exists a wide variety of graph neural networks (GNN) for node classification, only a minority of them adopt mechanisms that effectively target noise propagation during the message-passing procedure.
SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification
Our extensive benchmark experiments show that our hybrid deep network models trained with combined contrastive and cross-entropy loss achieved the state-of-the-art performance on three benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of 99. 75\%, 93. 4\%, and 86. 49\% respectively.
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.
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 Property Prediction with Uncertainty Quantification: A Benchmark Study
Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models.
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.
MLatticeABC: Generic Lattice Constant Prediction of Crystal Materials using Machine Learning
Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials.
Materials Science Computational Physics
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.
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.
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
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.
A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images
A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network.
ComplexFace: a Multi-Representation Approach for Image Classification with Small Dataset
State-of-the-art face recognition algorithms are able to achieve good performance when sufficient training images are provided.
Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials
The percentage of chemically valid (charge neutral and electronegativity balanced) samples out of all generated ones reaches 84. 5% by our GAN when trained with materials from ICSD even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules.
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.
Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks
Extensive benchmark experiments over 2, 170 Fm-3m face-centered-cubic (FCC) materials show that our ECD based CNNs can achieve good performance for elasticity prediction.
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets
Machine Reading Comprehension (MRC) is a challenging Natural Language Processing(NLP) research field with wide real-world applications.
NODE-SELECT: A FLEXIBLE GRAPH NEURAL NETWORK BASED ON REALISTIC PROPAGATION SCHEME
In this paper we propose the NODE-SELECT graph neural network (NSGNN): a novel and flexible graph neural network that uses subsetting filters to learn the contribution from the nodes selected to share their information.
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.
Crystal structure prediction of materials with high symmetry using differential evolution
Our experimental results show that our proposed algorithm CMCrystalHS can effectively solve the problem of inconsistent contact map dimensions and predict the crystal structures with high symmetry.
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.
Predicting Lattice Phonon Vibrational Frequencies Using Deep Graph Neural Networks
Our work demonstrates the capability of deep graph neural networks to learn to predict phonon spectrum properties of crystal structures in addition to phonon density of states (DOS) and electronic DOS in which the output dimension is constant.
Physics guided deep learning generative models for crystal materials discovery
Deep learning based generative models such as deepfake have been able to generate amazing images and videos.
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.
Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm
While crystal structure prediction (CSP) remains a longstanding challenge, we introduce ParetoCSP, a novel algorithm for CSP, which combines a multi-objective genetic algorithm (MOGA) with a neural network inter-atomic potential (IAP) model to find energetically optimal crystal structures given chemical compositions.
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.
Deep Learning-Based Classification of Gamma Photon Interactions in Room-Temperature Semiconductor Radiation Detectors
Photon counting radiation detectors have become an integral part of medical imaging modalities such as Positron Emission Tomography or Computed Tomography.
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.
