Shaping the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by Fine-Tuned GPT Models

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(51), P. 28284 - 28295

Published: Dec. 13, 2023

We construct a data set of metal-organic framework (MOF) linkers and employ fine-tuned GPT assistant to propose MOF linker designs by mutating modifying the existing structures. This strategy allows model learn intricate language chemistry in molecular representations, thereby achieving an enhanced accuracy generating structures compared with its base models. Aiming highlight significance design strategies advancing discovery water-harvesting MOFs, we conducted systematic variant expansion upon state-of-the-art MOF-303 utilizing multidimensional approach that integrates extension multivariate tuning strategies. synthesized series isoreticular aluminum termed Long-Arm MOFs (LAMOF-1 LAMOF-10), featuring bear various combinations heteroatoms their five-membered ring moiety, replacing pyrazole either thiophene, furan, or thiazole rings combination two. Beyond consistent robust architecture, as demonstrated permanent porosity thermal stability, LAMOF offers generalizable synthesis strategy. Importantly, these 10 LAMOFs establish new benchmarks for water uptake (up 0.64 g g-1) operational humidity ranges (between 13 53%), expanding diversity MOFs.

Language: Английский

---

Self-Driving Laboratories for Chemistry and Materials Science

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(16), P. 9633 - 9732

Published: Aug. 13, 2024

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through automation experimental workflows, along with autonomous planning, SDLs hold potential to greatly accelerate research in chemistry and materials discovery. This review provides in-depth analysis state-of-the-art SDL technology, its applications across various disciplines, implications for industry. additionally overview enabling technologies SDLs, including their hardware, software, integration laboratory infrastructure. Most importantly, this explores diverse range domains where have made significant contributions, from drug discovery science genomics chemistry. We provide a comprehensive existing real-world examples different levels automation, challenges limitations associated each domain.

Language: Английский

---

Recent Advances in Diverse MXenes‐Based Structures for Photocatalytic CO₂ Reduction into Renewable Hydrocarbon Fuels

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: 34(19)

Published: Jan. 17, 2024

Abstract Photocatalytic CO 2 reduction into renewable hydrocarbon fuels is a green solution to address emission and energy issues simultaneously. However, the fast recombination of photogenerated charge carriers sluggish surface reaction kinetics restrict efficiency photocatalytic reduction. The emergence 2D MXenes has potential in improving reduction, owing their high electrical conductivity, flexible structural properties, abundant active sites. Hence, this review will concisely summarize highlight recent advances MXenes‐based photocatalysts used First, synthesis properties briefly introduced. Second, mechanism photoreduction along with roles are summarized, including promoting adsorption , enhancing separation photo‐induced carriers, acting as robust support, photothermal effect. Third, different kinds such MXenes/metal oxides, MXenes/nitrides, MXenes/LDH, MXenes/perovskite, MXene‐derived for classified via type semiconductors. Finally, challenges perspectives also presented, exploring suitable machine learning, uncovering structure‐activity relationship by situ, time‐ space‐resolved characterization techniques, anti‐oxidization ability, scale‐up applications.

Language: Английский

---

Image and data mining in reticular chemistry powered by GPT-4V

Digital Discovery, Journal Year: 2024, Volume and Issue: 3(3), P. 491 - 501

Published: Jan. 1, 2024

The integration of artificial intelligence into scientific research opens new avenues with the advent GPT-4V, a large language model equipped vision capabilities.

Language: Английский

---

Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 25, 2024

The design and synthesis of MOFs have evolved from traditional large-scale approaches to function-oriented modifications, recently AI predictions, which save time, reduce costs, enhance the efficiency achieving target functions.

Language: Английский

---

Large language models for reticular chemistry

Nature Reviews Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Language: Английский

---

Metal–organic framework-based smart stimuli-responsive drug delivery systems for cancer therapy: advances, challenges, and future perspectives

Journal of Nanobiotechnology, Journal Year: 2025, Volume and Issue: 23(1)

Published: Feb. 28, 2025

Cancer treatment is currently one of the most critical healthcare issues globally. A well-designed drug delivery system can precisely target tumor tissues, improve efficacy, and reduce damage to normal tissues. Stimuli-responsive systems (SRDDSs) have shown promising application prospects. Intelligent nano responsive endogenous stimuli such as weak acidity, complex redox characteristics, hypoxia, active energy metabolism, well exogenous like high temperature, light, pressure, magnetic fields are increasingly being applied in chemotherapy, radiotherapy, photothermal therapy, photodynamic various other anticancer approaches. Metal–organic frameworks (MOFs) become candidate materials for constructing SRDDSs due their large surface area, tunable porosity structure, ease synthesis modification, good biocompatibility. This paper reviews MOF-based modes cancer therapy. It summarizes key aspects, including classification, synthesis, modifications, loading modes, stimuli-responsive mechanisms, roles different modalities. Furthermore, we address current challenges summarize potential applications artificial intelligence MOF synthesis. Finally, propose strategies enhance efficacy safety SRDDSs, ultimately aiming at facilitating clinical translation.

Language: Английский

---

The Road Ahead for Metal–Organic Frameworks: Current Landscape, Challenges and Future Prospects

ACS Nano, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

This perspective highlights the transformative potential of Metal-Organic Frameworks (MOFs) in environmental and healthcare sectors. It discusses work that has advanced beyond technology readiness levels >4 including applications capture, storage, conversion gases to value added products. showcases efforts most salient MOFs which have been performed at a great cadence, enabled by federal government, large companies, startups commercialize these technologies despite facing significant challenges. article also forecasts role nanoscale healthcare, strides toward personalized medicine, advocating for their use custom-tailored drug delivery systems. Finally we underscore acceleration MOF research development through integration machine learning AI, positioning as versatile tools poised address global sustainability health

Language: Английский

---

Computational and AI‐Driven Design of Hydrogels for Bioelectronic Applications

Advanced Electronic Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 14, 2025

Abstract As hydrogel research progresses, hydrogels are becoming essential tools in bioelectronics and biotechnology. This review explores the diverse range of natural synthetic gel materials tailored for specific bioelectronic applications, with a focus on their integration electronic components to create responsive, multifunctional systems. The role Artificial Intelligence (AI) advancing design functionality from optimizing material properties enabling precise, predictive modeling is investigated. Furthermore, recent innovations that harness synergy between hydrogels, electronics, AI discussed, emphasizing potential these drive future advances biomedical technologies. AI‐driven approaches transforming development applications wound healing, biosensing, drug delivery, tissue engineering.

Language: Английский

---

A Review of Large Language Models and Autonomous Agents in Chemistry

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

---