Space Kitty Archive

AI for Science

Machine learning that accelerates discovery: protein structure, machine-learned force fields, materials discovery, weather and autonomous laboratories.

250 papersnewest Oct 28, 2026

Most influential

  • Recent advances and applications of machine learning in solid-state materials science
    Jonathan Schmidt, Mário R. G. Marques, Silvana Botti et al. · 2019 · npj Computational Materials

    Abstract One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to so…

    Abstract excerpt by Jonathan Schmidt et al., CC-BY, https://doi.org/10.1038/s41524-019-0221-0; truncated.

    Article2,636 citationsdoi:10.1038/s41524-019-0221-0
  • Water electrolysis: from textbook knowledge to the latest scientific strategies and industrial developments
    Marian Chatenet, Bruno Georges Pollet, Dario R. Dekel et al. · 2022 · Chemical Society Reviews

    footprint. The viability of water electrolysis still hinges on the availability of durable earth-abundant electrocatalyst materials and the overall process efficiency. This review spans from the fundamentals of electrocatalytically initiated water splitting to the very latest scientific findings from university and institutional research, also covering speci…

    Abstract excerpt by Marian Chatenet et al., CC-BY, https://doi.org/10.1039/d0cs01079k; truncated.

    Review2,022 citationsdoi:10.1039/d0cs01079k
  • Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
    James E. Saal, Scott J. Kirklin, Muratahan Aykol et al. · 2013 · JOM
    Article2,540 citationsdoi:10.1007/s11837-013-0755-4
  • Inverse molecular design using machine learning: Generative models for matter engineering
    Benjamín Sánchez-Lengeling, Alán Aspuru‐Guzik · 2018 · Science
    Review2,071 citationsdoi:10.1126/science.aat2663
  • An autonomous laboratory for the accelerated synthesis of inorganic materials
    Nathan J. Szymanski, Bernardus Rendy, Yuxing Fei et al. · 2023 · Nature

    , we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel com…

    Abstract excerpt by Nathan J. Szymanski et al., CC-BY, https://doi.org/10.1038/s41586-023-06734-w; truncated.

    Article1,037 citationsdoi:10.1038/s41586-023-06734-w
  • Machine-learning-assisted materials discovery using failed experiments
    Paul Raccuglia, Katherine C. Elbert, Philip Adler et al. · 2016 · Nature
    Article1,724 citationsdoi:10.1038/nature17439
  • A universal graph deep learning interatomic potential for the periodic table
    Chi Chen, Shyue Ping Ong · 2022 · Nature Computational Science

    Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applications. Here, we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). T…

    Article997 citationsarXiv:2202.02450doi:10.1038/s43588-022-00349-3
  • Materials discovery and design using machine learning
    Yue Liu, Tianlu Zhao, Wangwei Ju et al. · 2017 · Journal of Materiomics
    Article1,276 citationsdoi:10.1016/j.jmat.2017.08.002
  • Unsupervised word embeddings capture latent knowledge from materials science literature
    Vahe Tshitoyan, John M. Dagdelen, Leigh Weston et al. · 2019 · Nature
    Article1,123 citationsdoi:10.1038/s41586-019-1335-8
  • Machine learning–enabled high-entropy alloy discovery
    Ziyuan Rao, Po‐Yen Tung, Ruiwen Xie et al. · 2022 · Science
    Article755 citationsdoi:10.1126/science.abo4940
  • Accelerating materials property predictions using machine learning
    Ghanshyam Pilania, Chenchen Wang, Xun Jiang et al. · 2013 · Scientific Reports

    The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechan…

    Abstract excerpt by Ghanshyam Pilania et al., CC-BY, https://doi.org/10.1038/srep02810; truncated.

    Article856 citationsdoi:10.1038/srep02810
  • Accelerating the discovery of materials for clean energy in the era of smart automation
    Daniel P. Tabor, Loı̈c M. Roch, Semion K. Saikin et al. · 2018 · Nature Reviews Materials

    The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing tr…

    Abstract excerpt by Daniel P. Tabor et al., CC-BY, https://doi.org/10.1038/s41578-018-0005-z; truncated.

    Review778 citationsdoi:10.1038/s41578-018-0005-z
  • The rise of self-driving labs in chemical and materials sciences
    Milad Abolhasani, Eugenia Kumacheva · 2023 · Nature Synthesis
    Article584 citationsdoi:10.1038/s44160-022-00231-0
  • Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements
    So Takamoto, Chikashi Shinagawa, Daisuke Motoki et al. · 2021 · Nature Communications

    Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for b…

    Article444 citationsarXiv:2106.14583doi:10.1038/s41467-022-30687-9
  • Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
    Shuaihua Lu, Qionghua Zhou, Yixin Ouyang et al. · 2018 · Nature Communications

    Rapidly discovering functional materials remains an open challenge because the traditional trial-and-error methods are usually inefficient especially when thousands of candidates are treated. Here, we develop a target-driven method to predict undiscovered hybrid organic-inorganic perovskites (HOIPs) for photovoltaics. This strategy, combining machine learnin…

    Abstract excerpt by Shuaihua Lu et al., CC-BY, https://doi.org/10.1038/s41467-018-05761-w; truncated.

    Article725 citationsdoi:10.1038/s41467-018-05761-w

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