Search Results - "Fu, Nihang"

Probabilistic generative transformer language models for generative design of molecules by Wei, Lai, Fu, Nihang, Song, Yuqi, Wang, Qian, Hu, Jianjun

“…Self-supervised neural language models have recently found wide applications in the generative design of organic molecules and protein sequences as well as…”
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Generative AI for Materials Discovery: Design Without Understanding by Hu, Jianjun, Li, Qin, Fu, Nihang

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Physics guided deep learning for generative design of crystal materials with symmetry constraints by Zhao, Yong, Siriwardane, Edirisuriya M. Dilanga, Wu, Zhenyao, Fu, Nihang, Al-Fahdi, Mohammed, Hu, Ming, Hu, Jianjun

“…Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and…”
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Scalable deeper graph neural networks for high-performance materials property prediction by Omee, Sadman Sadeed, Louis, Steph-Yves, Fu, Nihang, Wei, Lai, Dey, Sourin, Dong, Rongzhi, Li, Qinyang, Hu, Jianjun

“…Machine-learning-based materials property prediction models have emerged as a promising approach for new materials discovery, among which the graph neural…”
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MD-HIT: Machine learning for material property prediction with dataset redundancy control by Li, Qin, Fu, Nihang, Omee, Sadman Sadeed, Hu, Jianjun

“…Materials datasets usually contain many redundant (highly similar) materials due to the tinkering approach historically used in material design. This…”
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Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study by Omee, Sadman Sadeed, Fu, Nihang, Dong, Rongzhi, Hu, Ming, Hu, Jianjun

“…In real-world materials research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the…”
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Material transformers: deep learning language models for generative materials design by Fu, Nihang, Wei, Lai, Song, Yuqi, Li, Qinyang, Xin, Rui, Omee, Sadman Sadeed, Dong, Rongzhi, Siriwardane, Edirisuriya M Dilanga, Hu, Jianjun

“…Abstract Pre-trained transformer language models (LMs) on large unlabeled corpus have produced state-of-the-art results in natural language processing, organic…”
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Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models by Fu, Nihang, Hu, Jeffrey, Feng, Ying, Morrison, Gregory, Loye, Hans-Conrad Zur, Hu, Jianjun

“…Oxidation states (OS) are the charges on atoms due to electrons gained or lost upon applying an ionic approximation to their bonds. As a fundamental property,…”
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Crystal Composition Transformer: Self-Learning Neural Language Model for Generative and Tinkering Design of Materials by Wei, Lai, Li, Qinyang, Song, Yuqi, Stefanov, Stanislav, Dong, Rongzhi, Fu, Nihang, Siriwardane, Edirisuriya M D, Chen, Fanglin, Hu, Jianjun

“…Self-supervised neural language models have recently achieved unprecedented success from natural language processing to learning the languages of biological…”
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Author Correction: Physics guided deep learning for generative design of crystal materials with symmetry constraints by Zhao, Yong, Siriwardane, Edirisuriya M. Dilanga, Wu, Zhenyao, Fu, Nihang, Al-Fahdi, Mohammed, Hu, Ming, Hu, Jianjun

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Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property Prediction by Fu, Nihang, Wei, Lai, Hu, Jianjun

“…Deep learning models have been widely used for high-performance material property prediction. However, training such models usually requires a large amount of…”
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Simultaneously-Collected Multimodal Lying Pose Dataset: Enabling In-Bed Human Pose Monitoring by Liu, Shuangjun, Huang, Xiaofei, Fu, Nihang, Li, Cheng, Su, Zhongnan, Ostadabbas, Sarah

“…Computer vision field has achieved great success in interpreting semantic meanings from images, yet its algorithms can be brittle for tasks with adverse vision…”
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Generative Design of Inorganic Compounds Using Deep Diffusion Language Models by Dong, Rongzhi, Fu, Nihang, Siriwardane, Edirisuriya M. D., Hu, Jianjun

“…Due to the vast chemical space, discovering materials with a specific function is challenging. Chemical formulas are obligated to conform to a set of exacting…”
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Global Mapping of Structures and Properties of Crystal Materials by Li, Qinyang, Dong, Rongzhi, Fu, Nihang, Omee, Sadman Sadeed, Wei, Lai, Hu, Jianjun

“…Understanding materials’ composition–structure–function relationships is of critical importance for the design and discovery of novel functional materials…”
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DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition by Dong, Rongzhi, Zhao, Yong, Song, Yuqi, Fu, Nihang, Omee, Sadman Sadeed, Dey, Sourin, Li, Qinyang, Wei, Lai, Hu, Jianjun

“…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…”
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TCSP: a Template-Based Crystal Structure Prediction Algorithm for Materials Discovery by Wei, Lai, Fu, Nihang, Siriwardane, Edirisuriya M. D., Yang, Wenhui, Omee, Sadman Sadeed, Dong, Rongzhi, Xin, Rui, Hu, Jianjun

“…Fast and accurate crystal structure prediction (CSP) algorithms and web servers are highly desirable for the exploration and discovery of new materials out of…”
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Realistic material property prediction using domain adaptation based machine learning by Hu, Jeffrey, Liu, David, Fu, Nihang, Dong, Rongzhi

“…Materials property prediction models are usually evaluated using random splitting of datasets into training and test datasets, which not only leads to…”
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Data Efficient Infant Pose and Posture Recognition by Fu, Nihang

“…Infant motion analysis is a topic of critical importance in early childhood development studies. Among various measures of motor development, learning infant…”
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Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network by Gleaves, Daniel, Fu, Nihang, Dilanga Siriwardane, Edirisuriya M, Zhao, Yong, Hu, Jianjun

“…Data driven generative deep learning models have recently emerged as one of the most promising approaches for new materials discovery. While generator models…”
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Physical Encoding Improves OOD Performance in Deep Learning Materials Property Prediction by Fu, Nihang, Omee, Sadman Sadeed, Hu, Jianjun

“…Deep learning (DL) models have been widely used in materials property prediction with great success, especially for properties with large datasets. However,…”
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