Zobrazeno 1 - 10
of 435
pro vyhledávání: '"White, Andrew D."'
Autor:
Laurent, Jon M., Janizek, Joseph D., Ruzo, Michael, Hinks, Michaela M., Hammerling, Michael J., Narayanan, Siddharth, Ponnapati, Manvitha, White, Andrew D., Rodriques, Samuel G.
There is widespread optimism that frontier Large Language Models (LLMs) and LLM-augmented systems have the potential to rapidly accelerate scientific discovery across disciplines. Today, many benchmarks exist to measure LLM knowledge and reasoning on
Externí odkaz:
http://arxiv.org/abs/2407.10362
Large language models (LLMs) have emerged as powerful tools in chemistry, significantly impacting molecule design, property prediction, and synthesis optimization. This review highlights LLM capabilities in these domains and their potential to accele
Externí odkaz:
http://arxiv.org/abs/2407.01603
Autor:
Lála, Jakub, O'Donoghue, Odhran, Shtedritski, Aleksandar, Cox, Sam, Rodriques, Samuel G., White, Andrew D.
Large Language Models (LLMs) generalize well across language tasks, but suffer from hallucinations and uninterpretability, making it difficult to assess their accuracy without ground-truth. Retrieval-Augmented Generation (RAG) models have been propos
Externí odkaz:
http://arxiv.org/abs/2312.07559
Autor:
Ramos, Mayk Caldas, White, Andrew D.
Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy and enthalpy, resulting in long computations for relatively poor accura
Externí odkaz:
http://arxiv.org/abs/2307.05318
Autor:
Jablonka, Kevin Maik, Ai, Qianxiang, Al-Feghali, Alexander, Badhwar, Shruti, Bocarsly, Joshua D., Bran, Andres M, Bringuier, Stefan, Brinson, L. Catherine, Choudhary, Kamal, Circi, Defne, Cox, Sam, de Jong, Wibe A., Evans, Matthew L., Gastellu, Nicolas, Genzling, Jerome, Gil, María Victoria, Gupta, Ankur K., Hong, Zhi, Imran, Alishba, Kruschwitz, Sabine, Labarre, Anne, Lála, Jakub, Liu, Tao, Ma, Steven, Majumdar, Sauradeep, Merz, Garrett W., Moitessier, Nicolas, Moubarak, Elias, Mouriño, Beatriz, Pelkie, Brenden, Pieler, Michael, Ramos, Mayk Caldas, Ranković, Bojana, Rodriques, Samuel G., Sanders, Jacob N., Schwaller, Philippe, Schwarting, Marcus, Shi, Jiale, Smit, Berend, Smith, Ben E., Van Herck, Joren, Völker, Christoph, Ward, Logan, Warren, Sean, Weiser, Benjamin, Zhang, Sylvester, Zhang, Xiaoqi, Zia, Ghezal Ahmad, Scourtas, Aristana, Schmidt, KJ, Foster, Ian, White, Andrew D., Blaiszik, Ben
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article c
Externí odkaz:
http://arxiv.org/abs/2306.06283
Autor:
Medina, Jorge, White, Andrew D.
Evolutionary symbolic regression (SR) fits a symbolic equation to data, which gives a concise interpretable model. We explore using SR as a method to propose which data to gather in an active learning setting with physical constraints. SR with active
Externí odkaz:
http://arxiv.org/abs/2305.10379
The dual use of machine learning applications, where models can be used for both beneficial and malicious purposes, presents a significant challenge. This has recently become a particular concern in chemistry, where chemical datasets containing sensi
Externí odkaz:
http://arxiv.org/abs/2304.10510
Autor:
Medina, Jorge, White, Andrew D
Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we propose and study Bloom filters for testing if a molecule is present
Externí odkaz:
http://arxiv.org/abs/2304.05386
Autor:
Bran, Andres M, Cox, Sam, Schilter, Oliver, Baldassari, Carlo, White, Andrew D, Schwaller, Philippe
Over the last decades, excellent computational chemistry tools have been developed. Integrating them into a single platform with enhanced accessibility could help reaching their full potential by overcoming steep learning curves. Recently, large-lang
Externí odkaz:
http://arxiv.org/abs/2304.05376