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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.10980">arXiv:2402.10980</a> <span> [<a href="https://arxiv.org/pdf/2402.10980">pdf</a>, <a href="https://arxiv.org/format/2402.10980">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sprueill%2C+H+W">Henry W. Sprueill</a>, <a href="/search/cs?searchtype=author&query=Edwards%2C+C">Carl Edwards</a>, <a href="/search/cs?searchtype=author&query=Agarwal%2C+K">Khushbu Agarwal</a>, <a href="/search/cs?searchtype=author&query=Olarte%2C+M+V">Mariefel V. Olarte</a>, <a href="/search/cs?searchtype=author&query=Sanyal%2C+U">Udishnu Sanyal</a>, <a href="/search/cs?searchtype=author&query=Johnston%2C+C">Conrad Johnston</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+H">Hongbin Liu</a>, <a href="/search/cs?searchtype=author&query=Ji%2C+H">Heng Ji</a>, <a href="/search/cs?searchtype=author&query=Choudhury%2C+S">Sutanay Choudhury</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.10980v5-abstract-short" style="display: inline;"> The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with quantum-chemistry based feedback from 3D atomistic representations. Our approach formulates catalyst discovery as an uncertain environment where an agent… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10980v5-abstract-full').style.display = 'inline'; document.getElementById('2402.10980v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.10980v5-abstract-full" style="display: none;"> The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with quantum-chemistry based feedback from 3D atomistic representations. Our approach formulates catalyst discovery as an uncertain environment where an agent actively searches for highly effective catalysts via the iterative combination of large language model (LLM)-derived hypotheses and atomistic graph neural network (GNN)-derived feedback. Identified catalysts in intermediate search steps undergo structural evaluation based on spatial orientation, reaction pathways, and stability. Scoring functions based on adsorption energies and reaction energy barriers steer the exploration in the LLM's knowledge space toward energetically favorable, high-efficiency catalysts. We introduce planning methods that automatically guide the exploration without human input, providing competitive performance against expert-enumerated chemical descriptor-based implementations. By integrating language-guided reasoning with computational chemistry feedback, our work pioneers AI-accelerated, trustworthy catalyst discovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10980v5-abstract-full').style.display = 'none'; document.getElementById('2402.10980v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, accepted by ICML 2024, final version</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.14420">arXiv:2310.14420</a> <span> [<a href="https://arxiv.org/pdf/2310.14420">pdf</a>, <a href="https://arxiv.org/format/2310.14420">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sprueill%2C+H+W">Henry W. Sprueill</a>, <a href="/search/cs?searchtype=author&query=Edwards%2C+C">Carl Edwards</a>, <a href="/search/cs?searchtype=author&query=Olarte%2C+M+V">Mariefel V. Olarte</a>, <a href="/search/cs?searchtype=author&query=Sanyal%2C+U">Udishnu Sanyal</a>, <a href="/search/cs?searchtype=author&query=Ji%2C+H">Heng Ji</a>, <a href="/search/cs?searchtype=author&query=Choudhury%2C+S">Sutanay Choudhury</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.14420v1-abstract-short" style="display: inline;"> Discovering novel catalysts requires complex reasoning involving multiple chemical properties and resultant trade-offs, leading to a combinatorial growth in the search space. While large language models (LLM) have demonstrated novel capabilities for chemistry through complex instruction following capabilities and high quality reasoning, a goal-driven combinatorial search using LLMs has not been ex… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.14420v1-abstract-full').style.display = 'inline'; document.getElementById('2310.14420v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.14420v1-abstract-full" style="display: none;"> Discovering novel catalysts requires complex reasoning involving multiple chemical properties and resultant trade-offs, leading to a combinatorial growth in the search space. While large language models (LLM) have demonstrated novel capabilities for chemistry through complex instruction following capabilities and high quality reasoning, a goal-driven combinatorial search using LLMs has not been explored in detail. In this work, we present a Monte Carlo Tree Search-based approach that improves beyond state-of-the-art chain-of-thought prompting variants to augment scientific reasoning. We introduce two new reasoning datasets: 1) a curation of computational chemistry simulations, and 2) diverse questions written by catalysis researchers for reasoning about novel chemical conversion processes. We improve over the best baseline by 25.8\% and find that our approach can augment scientist's reasoning and discovery process with novel insights. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.14420v1-abstract-full').style.display = 'none'; document.getElementById('2310.14420v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP2023) Findings </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 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