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Работы, на которые ссылается эта работа

Работ: 48

Работа: Accurate classification of materials with elEmBERT: Element embeddings for chemical benchmarks

  1. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

    Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier +8

    Статья2013Цитирований: 12
    ABI

  2. AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale

    Kefallinos, Dionysios, Alexandris, Georgios, Maras, Alexis +5

    Препринт2018Цитирований: 11
    ABI

  3. Scikit-learn: Machine Learning in Python

    PedregosaFabian, VaroquauxGaël, GramfortAlexandre +13

    Статья2011Цитирований: 6
    ABI

  4. MoleculeNet: a benchmark for molecular machine learning

    Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg +5

    Статья2017Цитирований: 6
    ABI

  5. Crystallography Open Database – an open-access collection of crystal structures

    S. Gražulis, Daniel Chateigner, Robert T. Downs +7

    Статья2009Цитирований: 4
    ABI

  6. Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

    Muhammad Naveed Akhter, Saad Mekhilef, Hazlie Mokhlis +1

    Статья2019Цитирований: 4
    ABI

  7. Applications of machine learning in drug discovery and development

    Jessica Vamathevan, Dominic A. Clark, Paul Czodrowski +8

    Обзорная статья2019Цитирований: 4
    ABI

  8. Machine Learning for Catalysis Informatics: Recent Applications and Prospects

    Takashi Toyao, Zen Maeno, Satoru Takakusagi +3

    Статья2019Цитирований: 4
    ABI

  9. Predicting the state of charge and health of batteries using data-driven machine learning

    Man‐Fai Ng, Jin Zhao, Qingyu Yan +2

    Статья2020Цитирований: 4
    ABI

  10. Efficient Estimation of Word Representations in Vector Space

    Tomáš Mikolov, Kai Chen, Greg S. Corrado +1

    Препринт2013Цитирований: 3
    ABI

  11. On representing chemical environments

    Albert P. Bartók, Risi Kondor, Gábor Cśanyi

    Статья2013Цитирований: 3
    ABI

  12. The atomic simulation environment—a Python library for working with atoms

    Ask Hjorth Larsen, Jens Jørgen Mortensen, Jakob Blomqvist +31

    Статья2017Цитирований: 3
    ABI

  13. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

    Tian Xie, Jeffrey C. Grossman

    Статья2018Цитирований: 3
    ABI

  14. Machine-learning guided discovery of a new thermoelectric material

    Yuma Iwasaki, Ichiro Takeuchi, Valentin Stanev +10

    Статья2019Цитирований: 3
    ABI

  15. The rise of the X-ray atomic pair distribution function method: a series of fortunate events

    Simon J. L. Billinge

    Статья2019Цитирований: 3
    ABI

  16. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

    Chi Chen, Weike Ye, Yunxing Zuo +2

    Статья2019Цитирований: 3
    ABI

  17. Machine learning assisted materials design and discovery for rechargeable batteries

    Yue Liu, Biru Guo, Xinxin Zou +2

    Статья2020Цитирований: 3
    ABI

  18. Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

    Shuo Zhang, Yang Liu, Lei Xie

    Препринт2020Цитирований: 3
    ABI

  19. Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings

    Shufeng Kong, Francesco Ricci, Dan Guevarra +3

    Статья2022Цитирований: 3
    ABI

  20. Unified representation of molecules and crystals for machine learning

    Haoyan Huo, Matthias Rupp

    Статья2022Цитирований: 3
    ABI

  21. Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review

    Vaishali A. Sawant, Rashmi Deshmukh, Chetan J. Awati

    Обзорная статья2022Цитирований: 3
    ABI

  22. PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

    Oliver T. Unke, Markus Meuwly

    Статья2019Цитирований: 2
    ABI

  23. Benchmarking graph neural networks for materials chemistry

    Victor Fung, Jiaxin Zhang, Eric Juarez +1

    Статья2021Цитирований: 2
    ABI

  24. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

    Katja Hansen, Franziska Biegler, Raghunathan Ramakrishnan +4

    Статья2015Цитирований: 2
    ABI

  25. Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals

    Shokirbek Shermukhamedov, Dilorom Mamurjonova, Thana Maihom +1

    СтатьяMachine Learning in Materials ScienceJournal of Chemical Information and Modeling2024Цитирований: 1
    ABI