Large Language Models Open New Way of AI-Assisted Molecule Design for Chemists DOI Creative Commons
Shoichi Ishida, Tomohiro Sato,

Teruki Honma

et al.

Published: April 22, 2024

Recent advancements in artificial intelligence (AI)-based molecular design methodologies have offered synthetic chemists new ways to functional molecules with their desired properties. While various AI-based molecule generators significantly advanced toward practical applications, effective use still requires specialized knowledge and skills concerning AI techniques. Here, we develop a large language model (LLM)-powered chatbot, ChatChemTS, that enables using an generator through only chat interactions, including automated construction of reward functions for the specified Our study showcases utility ChatChemTS de novo cases involving chromophores anticancer drugs (epidermal growth factor receptor inhibitors), exemplifying single- multiobjective optimization scenarios, respectively. is provided as open-source package on GitHub at https://github.com/molecule-generator-collection/ChatChemTS.

Language: Английский

Recent advances in artificial intelligence boosting materials design for electrochemical energy storage DOI Creative Commons
X.-B. Liu, Kexin Fan, Xinmeng Huang

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 490, P. 151625 - 151625

Published: April 24, 2024

In the rapidly evolving landscape of electrochemical energy storage (EES), advent artificial intelligence (AI) has emerged as a keystone for innovation in material design, propelling forward design and discovery batteries, fuel cells, supercapacitors, many other functional materials. This review paper elucidates burgeoning role AI materials from foundational machine learning (ML) techniques to its current pivotal advancing frontiers science storage, including enhancing performance, durability, safety battery technologies, cell efficiency longevity, fine-tuning supercapacitors achieve superior capabilities. Collectively, we present comprehensive overview recent advancements that have significantly accelerated development next-generation EES, offering insights into future research trajectories potential unlock new horizons science.

Language: Английский

Citations

28
Assessment of Students Use of Generative Artificial Intelligence: Prompting Strategies and Prompt Engineering in Chemistry Education DOI Creative Commons
Sebastian Tassoti

Journal of Chemical Education, Journal Year: 2024, Volume and Issue: 101(6), P. 2475 - 2482

Published: May 22, 2024

The rapid integration of generative artificial intelligence (AI) into educational settings prompts an urgent examination its efficacy and the strategies that students employ to harness potential. This study focuses on preservice chemistry teachers use AI for chemistry-specific problem-solving task completion. We found there is a prevalent reliance copy-pasting tactics in initial prompting approaches, need guidance improve their abilities. By implementing "Five S" framework, we explore evolution student resultant satisfaction with AI-generated responses. Our findings indicate that, while initially struggle nuances effective prompting, adoption structured frameworks significantly enhances perceived quality answers. research sheds light current state among but also underscores importance targeted refine interaction academic contexts. In particular, suggest critical engagement methodological prompt engineering maximize benefits technologies.

Language: Английский

Citations

23
A Review of Large Language Models and Autonomous Agents in Chemistry DOI Creative Commons
Mayk Caldas Ramos, Christopher J. Collison, Andrew Dickson White

et al.

Chemical Science, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 9, 2024

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 these domains their potential to accelerate scientific discovery through automation. We also LLM-based autonomous agents: LLMs with a broader set of interact surrounding environment. These agents perform diverse tasks such paper scraping, interfacing automated laboratories, planning. As are an emerging topic, we extend the scope our beyond chemistry discuss across any domains. covers recent history, current capabilities, design agents, addressing specific challenges, opportunities, future directions chemistry. Key challenges include data quality integration, model interpretability, need for standard benchmarks, while point towards more sophisticated multi-modal enhanced collaboration between experimental methods. Due quick pace this field, repository has been built keep track latest studies: https://github.com/ur-whitelab/LLMs-in-science.

Language: Английский

Citations

13
Large Language Models for Inorganic Synthesis Predictions DOI
Seong-Min Kim, Yousung Jung, Joshua Schrier

et al.

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(29), P. 19654 - 19659

Published: July 11, 2024

We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting synthesizability inorganic compounds selection precursors needed to perform synthesis. The predictions LLMs are comparable to─and sometimes better than─recent bespoke machine learning these tasks but require only minimal user expertise, cost, time develop. Therefore, this strategy can serve both as an effective strong baseline future studies various chemical applications a practical tool experimental chemists.

Language: Английский

Citations

8
Can LLMs Revolutionize Text Mining in Chemistry? A Comparative Study with Domain-Specific Tools DOI

Manisha Kumari,

Rohit Chauhan, Prabha Garg

et al.

Computer Standards & Interfaces, Journal Year: 2025, Volume and Issue: 94, P. 103997 - 103997

Published: March 2, 2025

Language: Английский

Citations

1
When Metal Nanoclusters Meet Smart Synthesis DOI
Zhucheng Yang, Anye Shi, Ruixuan Zhang

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 24, 2024

Atomically precise metal nanoclusters (MNCs) represent a fascinating class of ultrasmall nanoparticles with molecule-like properties, bridging conventional metal-ligand complexes and nanocrystals. Despite their potential for various applications, synthesis challenges such as understanding varied synthetic parameters property-driven persist, hindering full exploitation wider application. Incorporating smart methodologies, including closed-loop framework automation, data interpretation, feedback from AI, offers promising solutions to address these challenges. In this perspective, we summarize the that has been demonstrated in nanomaterials explore research frontiers MNCs. Moreover, perspectives on inherent opportunities MNCs are discussed, aiming provide insights directions future advancements emerging field AI Science, while integration deep learning algorithms stands substantially enrich by offering enhanced predictive capabilities, optimization strategies, control mechanisms, thereby extending MNC synthesis.

Language: Английский

Citations

5
An intelligent approach: Integrating ChatGPT for experiment planning in biochar immobilization of soil cadmium DOI
Hongwei Yang, Jie Wang,

Rumeng Mo

et al.

Separation and Purification Technology, Journal Year: 2024, Volume and Issue: 352, P. 128170 - 128170

Published: May 29, 2024

Language: Английский

Citations

4
Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies DOI Creative Commons

Justin P. Edaugal,

Difan Zhang, Dupeng Liu

et al.

Chem & Bio Engineering, Journal Year: 2025, Volume and Issue: 2(4), P. 210 - 228

Published: March 5, 2025

As the chemical industry shifts toward sustainable practices, there is a growing initiative to replace conventional fossil-derived solvents with environmentally friendly alternatives such as ionic liquids (ILs) and deep eutectic (DESs). Artificial intelligence (AI) plays key role in discovery design of novel development green processes. This review explores latest advancements AI-assisted solvent screening specific focus on machine learning (ML) models for physicochemical property prediction separation process design. Additionally, this paper highlights recent progress automated high-throughput (HT) platforms screening. Finally, discusses challenges prospects ML-driven HT strategies optimization. To end, provides insights advance future

Language: Английский

Citations

0
Towards an automated workflow in materials science for combining multi-modal simulation and experimental information using data mining and large language models DOI Creative Commons
Balduin Katzer, Steffen Klinder, Katrin Schulz

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112186 - 112186

Published: March 1, 2025

Language: Английский

Citations

0
Large language models open new way of AI-assisted molecule design for chemists DOI Creative Commons
Shoichi Ishida, Tomohiro Sato, Teruki Honma

et al.

Journal of Cheminformatics, Journal Year: 2025, Volume and Issue: 17(1)

Published: March 24, 2025

Abstract Recent advancements in artificial intelligence (AI)-based molecular design methodologies have offered synthetic chemists new ways to functional molecules with their desired properties. While various AI-based molecule generators significantly advanced toward practical applications, effective use still requires specialized knowledge and skills concerning AI techniques. Here, we develop a large language model (LLM)-powered chatbot, ChatChemTS, that assists users designing using an generator through only chat interactions, including automated construction of reward functions for the specified Our study showcases utility ChatChemTS de novo cases involving chromophores anticancer drugs (epidermal growth factor receptor inhibitors), exemplifying single- multiobjective optimization scenarios, respectively. is provided as open-source package on GitHub at https://github.com/molecule-generator-collection/ChatChemTS . Scientific contribution application utilizing generator, ChemTSv2, solely interactions. This demonstrates LLMs possess potential utilize software, such generators, which require technical skills.

Language: Английский

Citations

0