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Recent advances in generative AI have accelerated the discovery of novel chemicals and materials.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
From louvain to leiden: guaranteeing well-connected communities
Traag, V. A., Waltman, L., and Van Eck, N. J · 2019
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Flashattention: Fast and memory-efficient exact attention with io-awareness
Dao, T., Fu, D., Ermon, S., Rudra, A., and Ré, C · 2022
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Hirretier, E., Balhorn, L. S., and Schweidtmann, A. M · 2022
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Flashattention-2: Faster attention with better parallelism and work partitioning
Dao, T · 2023
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Qlora: Efficient finetuning of quantized llms
Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L · 2023
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Efficient memory management for large language model serving with pagedattention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J., Zhang, H., and Stoica, I · 2023
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Data augmentation for machine learning of chemical process flowsheets
Schulze Balhorn, L., Hirretier, E., Luderer, L., and Schweidtmann, A. M · 2023
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A simple and effective pruning approach for large language models
Sun, M., Liu, Z., Bair, A., and Kolter, J. Z · 2023
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Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
Vogel, G., Balhorn, L. S., and Schweidtmann, A. M · 2023
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Qa-lora: Quantization-aware low-rank adaptation of large language models
Xu, Y., Xie, L., Gu, X., Chen, X., Chang, H., Zhang, H., Chen, Z., Zhang, X., and Tian, Q · 2023
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Seeking neural nuggets: Knowledge transfer in large language models from a parametric perspective
Zhong, M., An, C., Chen, W., Han, J., and He, P · 2023
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Flashattention on a napkin: A diagrammatic approach to deep learning io-awareness
Abbott, V. and Zardini, G · 2024
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Forest-of-thought: Scaling test-time compute for enhancing llm reasoning
Bi, Z., Han, K., Liu, C., Tang, Y., and Wang, Y · 2024
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Int-flashattention: Enabling flash attention for int8 quantization
Chen, S., Liu, Z., Wu, Z., Zheng, C., Cong, P., Jiang, Z., Wu, Y., Su, L., and Yang, T · 2024
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Chiang, Y., Hsieh, E., Chou, C.-H., and Riebesell, J · 2024
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From local to global: A graph rag approach to query-focused summarization
Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., Metropolitansky, D., Ness, R. O., and Larson, J · 2024
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Break the sequential dependency of llm inference using lookahead decoding
Fu, Y., Bailis, P., Stoica, I., and Zhang, H · 2024
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Gao, Y., Liu, Z., Zhang, W., Du, B., and Xia, G.-S · 2024
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An agentic approach to automatic creation of p&id diagrams from natural language descriptions
Gowiakar, S., Iyengar, S., Segal, S., and Kalyanaraman, S · 2024
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Saturn: Sample-efficient generative molecular design using memory manipulation
Guo, J. and Schwaller, P · 2024
Cited alongside, same era.
Retrieval-augmented generation with graphs (graphrag)
Han, H., Wang, Y., Shomer, H., Guo, K., Ding, J., Lei, Y., Halappanavar, M., Rossi, R. A., Mukherjee, S., Tang, X., et al · 2024
Cited alongside, same era.
G-retriever: Retrieval-augmented generation for textual graph understanding and question answering
He, X., Tian, Y., Sun, Y., Chawla, N., Laurent, T., LeCun, Y., Bresson, X., and Hooi, B · 2024
Cited alongside, same era.
Interact: Enabling interactive, question-driven learning in large language models
Kendapadi, A., Zaman, K., Menon, R. R., and Srivastava, S · 2024
Cited alongside, same era.
Shortened llama: Depth pruning for large language models with comparison of retraining methods
Llm-bip: Structured pruning for large language models with block-wise forward importance propagation
Wu, H · 2024
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Generative hierarchical materials search
Yang, S., Batzner, S., Gao, R., Aykol, M., Gaunt, A., McMorrow, B. C., Jimenez Rezende, D., Schuurmans, D., Mordatch, I., and Cubuk, E. D · 2024
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Honeycomb: A flexible llm-based agent system for materials science
Zhang, H., Song, Y., Hou, Z., Miret, S., and Liu, B · 2024
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Lookahead: An inference acceleration framework for large language model with lossless generation accuracy
Zhao, Y., Xie, Z., Liang, C., Zhuang, C., and Gu, J · 2024
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A survey on model compression for large language models
Zhu, X., Li, J., Liu, Y., Ma, C., and Wang, W · 2024
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Kim, B.-K., Kim, G., Kim, T.-H., Castells, T., Choi, S., Shin, J., and Song, H.-K · 2024
Cited alongside, same era.
Kristiadi, A., Strieth-Kalthoff, F., Skreta, M., Poupart, P., Aspuru-Guzik, A., and Pleiss, G · 2024
Cited alongside, same era.
Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., Zhao, C., Deng, C., Zhang, C., Ruan, C., et al · 2024
Cited alongside, same era.
Alphapruning: Using heavy-tailed self regularization theory for improved layer-wise pruning of large language models
Lu, H., Zhou, Y., Liu, S., Wang, Z., Mahoney, M. W., and Yang, Y · 2024
Cited alongside, same era.
Dynamic speculation lookahead accelerates speculative decoding of large language models
Mamou, J., Pereg, O., Korat, D., Berchansky, M., Timor, N., Wasserblat, M., and Schwartz, R · 2024
Cited alongside, same era.
text-embedding-3-small model
OpenAI · 2024
Cited alongside, same era.
A chemically-guided generative diffusion model for materials synthesis planning
Pan, E., Kwon, S., Liu, S., Xie, M., Duan, Y., Prein, T., Sheriff, K., Roman, Y., Moliner, M., Gómez-Bombarelli, R., et al · 2024
Cited alongside, same era.
vattention: Dynamic memory management for serving llms without pagedattention
Prabhu, R., Nayak, A., Mohan, J., Ramjee, R., and Panwar, A · 2024
Cited alongside, same era.
Talking like piping and instrumentation diagrams (p&ids)
Alimin, A. A., Goldstein, D. P., Balhorn, L. S., and Schweidtmann, A. M · 2025
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Inference-time scaling for complex tasks: Where we stand and what lies ahead
Balachandran, V., Chen, J., Chen, L., Garg, S., Joshi, N., Lara, Y., Langford, J., Nushi, B., Vineet, V., Wu, Y., et al · 2025
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Sets: Leveraging self-verification and self-correction for improved test-time scaling
Chen, J., Ren, J., Chen, X., Yang, C., Sun, R., and Arık, S. Ö · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al · 2025
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Li, X · 2025
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Cppo: Accelerating the training of group relative policy optimization-based reasoning models
Lin, Z., Lin, M., Xie, Y., and Ji, R · 2025
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Dwsim: Open source process simulator, 2025
Medeiros, D · 2025
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Optimizing test-time compute via meta reinforcement fine-tuning
Qu, Y., Yang, M. Y., Setlur, A., Tunstall, L., Beeching, E. E., Salakhutdinov, R., and Kumar, A · 2025
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2ssp: A two-stage framework for structured pruning of llms
Sandri, F., Cunegatti, E., and Iacca, G · 2025
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Singhi, N., Bansal, H., Hosseini, A., Grover, A., Chang, K.-W., Rohrbach, M., and Rohrbach, A · 2025
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The curse of depth in large language models
Sun, W., Song, X., Li, P., Yin, L., Zheng, Y., and Liu, S · 2025
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Darwinlm: Evolutionary structured pruning of large language models
Tang, S., Sieberling, O., Kurtic, E., Shen, Z., and Alistarh, D · 2025
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Beyond answers: Transferring reasoning capabilities to smaller llms using multi-teacher knowledge distillation
Tian, Y., Han, Y., Chen, X., Wang, W., and Chawla, N. V · 2025
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Z1: Efficient test-time scaling with code
Yu, Z., Wu, Y., Zhao, Y., Cohan, A., and Zhang, X.-P · 2025
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What, how, where, and how well? a survey on test-time scaling in large language models
Zhang, Q., Lyu, F., Sun, Z., Wang, L., Zhang, W., Guo, Z., Wang, Y., King, I., Liu, X., and Ma, C · 2025
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