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@article{chen2020descriptor,
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@article{schlexer2019machine,
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  note={Category: competitor - ML for heterogeneous catalysis}
}

@article{tran2018active,
  title={Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution},
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  year={2018},
  note={Category: competitor - Active learning for catalyst discovery}
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@article{zhong2020accelerated,
  title={Accelerated discovery of CO2 electrocatalysts using active machine learning},
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@article{jablonka2024leveraging,
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  volume={6},
  number={2},
  pages={161--169},
  year={2024},
  note={Category: competitor - LLMs for chemistry predictions}
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@article{bran2024chemcrow,
  title={ChemCrow: Augmenting large-language models with chemistry tools},
  author={Bran, Andres M and Cox, Sam and White, Andrew D and Schwaller, Philippe},
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  volume={6},
  number={5},
  pages={525--535},
  year={2024},
  note={Category: competitor - LLMs with chemistry tools}
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@article{boiko2023autonomous,
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  author={Boiko, Daniil A and MacKnight, Robert and Gomes, Gabe},
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  pages={570--578},
  year={2023},
  note={Category: competitor - Autonomous chemistry research with LLMs}
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@article{xin2021high,
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@article{yao2018carbothermal,
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@article{pedersen2023high,
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@article{li2024multi,
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@article{ma2017efficient,
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@article{rajan2024machine,
  title={Machine learning-assisted screening of corrosion-resistant materials},
  author={Rajan, Arjun Subramonian and Hanke, Felix and Militzer, Matthias and Asselin, Edouard},
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  year={2024},
  note={Category: competitor - ML for materials screening}
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@article{szymanski2023autonomous,
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  author={Szymanski, Nathan J and Rendy, Bernardus and Fei, Yuxing and Kumar, Rishi E and He, Tanjin and Milsted, David and McDermott, Matthew J and Gallant, Max and Cubuk, Ekin Dogus and Merchant, Amil and others},
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  volume={624},
  number={7990},
  pages={86--91},
  year={2023},
  note={Category: competitor - Autonomous materials synthesis}
}

@article{merchant2023scaling,
  title={Scaling deep learning for materials discovery},
  author={Merchant, Amil and Batzner, Simon and Schoenholz, Samuel S and Aykol, Muratahan and Cheon, Gowoon and Cubuk, Ekin Dogus},
  journal={Nature},
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  pages={80--85},
  year={2023},
  note={Category: competitor - Deep learning for materials discovery}
}

@article{friedlingstein2024global,
  title={Global Carbon Budget 2024},
  author={Friedlingstein, P and O'Sullivan, Michael and Jones, Matthew W and Andrew, Robbie M and Hauck, Judith and Landsch{\"u}tzer, Peter and Le Qu{\'e}r{\'e}, Corinne and Li, Hongmei and Luijkx, Ingrid T and Olsen, Are and Peters, Glen P and Peters, Wouter and Pongratz, Julia and Schwingshackl, Clemens and Sitch, Stephen and Canadell, Josep G and Ciais, Philippe and Jackson, Robert B and others},
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@article{jiao2019copper,
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@article{kovacic2020photocatalytic,
  title={Photocatalytic CO2 Reduction: A Review of Ab Initio Mechanism, Kinetics, and Multiscale Modeling Simulations},
  author={Kova{\v{c}}i{\v{c}}, {\v{Z}}eljko and Likozar, Bla{\v{z}} and Hu{\v{s}}, Matej},
  journal={ACS Catalysis},
  volume={10},
  number={24},
  pages={14984--15007},
  year={2020},
  publisher={American Chemical Society}
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@article{he2023threedfourfivehea,
  title={A 3d-4d-5d High Entropy Alloy as a Bifunctional Oxygen Catalyst for Robust Aqueous Zinc-Air Batteries},
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  volume={35},
  number={34},
  pages={2303719},
  year={2023},
  publisher={Wiley}
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@article{ding2020highentropy,
  title={High Entropy Alloy Based Nanomaterials for Electrocatalysis},
  author={Ding, Zhenhui and Bian, Jikang and Shuang, Shuai and Liu, Xiaodan and Hu, Yuanyuan and Sun, Chuanwei and Yang, Yong},
  journal={Advanced Functional Materials},
  volume={30},
  number={52},
  pages={2007405},
  year={2020},
  publisher={Wiley},
  note={Review article on HEA electrocatalysts including OER applications}
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@article{microsoft2023impact,
  title={The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4},
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  journal={arXiv preprint arXiv:2311.07361},
  year={2023}
}