A field guide to flow chemistry for synthetic organic chemists

Chemical Science, Journal Year: 2023, Volume and Issue: 14(16), P. 4230 - 4247

Published: Jan. 1, 2023

This review explores the benefits of flow chemistry and dispels notion that it is a mysterious “black box”, demonstrating how can push boundaries organic synthesis through understanding its governing principles.

Language: Английский

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Machine learning-guided realization of full-color high-quantum-yield carbon quantum dots

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: June 6, 2024

Abstract Carbon quantum dots (CQDs) have versatile applications in luminescence, whereas identifying optimal synthesis conditions has been challenging due to numerous parameters and multiple desired outcomes, creating an enormous search space. In this study, we present a novel multi-objective optimization strategy utilizing machine learning (ML) algorithm intelligently guide the hydrothermal of CQDs. Our closed-loop approach learns from limited sparse data, greatly reducing research cycle surpassing traditional trial-and-error methods. Moreover, it also reveals intricate links between target properties unifies objective function optimize like full-color photoluminescence (PL) wavelength high PL yields (PLQY). With only 63 experiments, achieve fluorescent CQDs with PLQY exceeding 60% across all colors. study represents significant advancement ML-guided synthesis, setting stage for developing new materials properties.

Language: Английский

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Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning

npj Computational Materials, Journal Year: 2024, Volume and Issue: 10(1)

Published: Jan. 10, 2024

Abstract Data-driven materials science has realized a new paradigm by integrating domain knowledge and machine-learning (ML) techniques. However, ML-based research often overlooked the inherent limitation in predicting unknown data: extrapolative performance, especially when dealing with small-scale experimental datasets. Here, we present comprehensive benchmark for assessing performance across 12 organic molecular properties. Our large-scale reveals that conventional ML models exhibit remarkable degradation beyond training distribution of property range structures, particularly small-data To address this challenge, introduce quantum-mechanical (QM) descriptor dataset, called QMex, an interactive linear regression (ILR), which incorporates interaction terms between QM descriptors categorical information pertaining to structures. The QMex-based ILR achieved state-of-the-art while preserving its interpretability. results, QMex proposed model serve as valuable assets improving predictions small datasets discovery novel materials/molecules surpass existing candidates.

Language: Английский

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The green chemistry paradigm in modern organic synthesis

Russian Chemical Reviews, Journal Year: 2023, Volume and Issue: 92(12), P. RCR5104 - RCR5104

Published: Dec. 1, 2023

After the appearance of green chemistry concept, which was introduced in vocabulary early 1990s, its main statements have been continuously developed and modified. Currently, there are 10–12 cornerstones that should form basis for an ideal chemical process. This review analyzes accumulated experience achievements towards design products processes reduce or eliminate use generation hazardous substances. The presents views leading Russian scientists specializing various fields this subject, including homogeneous heterogeneous catalysis, fine basic organic synthesis, electrochemistry, polymer chemistry, based on bio-renewable feedstocks energetic compounds materials. A new approach to quantitative evaluation environmental friendliness by authors is described. <br> bibliography includes 1761.

Language: Английский

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Chemical Reaction Networks Explain Gas Evolution Mechanisms in Mg-Ion Batteries

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(22), P. 12181 - 12192

Published: May 26, 2023

Out-of-equilibrium electrochemical reaction mechanisms are notoriously difficult to characterize. However, such reactions critical for a range of technological applications. For instance, in metal-ion batteries, spontaneous electrolyte degradation controls electrode passivation and battery cycle life. Here, improve our ability elucidate reactivity, we the first time combine computational chemical network (CRN) analysis based on density functional theory (DFT) differential mass spectroscopy (DEMS) study gas evolution from model Mg-ion electrolyte─magnesium bistriflimide (Mg(TFSI)2) dissolved diglyme (G2). Automated CRN allows facile interpretation DEMS data, revealing H2O, C2H4, CH3OH as major products G2 decomposition. These findings further explained by identifying elementary using DFT. While TFSI- is reactive at Mg electrodes, find that it does not meaningfully contribute evolution. The combined theoretical-experimental approach developed here provides means effectively predict decomposition pathways when initially unknown.

Language: Английский

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Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 30, 2024

Abstract Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous platform implements adaptive, closed-loop workflow mechanistic investigation molecular electrochemistry. As a proof-of-concept, this autonomously identifies investigates EC mechanism, interfacial electron transfer ( E step) followed by solution reaction C step), cobalt tetraphenylporphyrin exposed library organohalide electrophiles. The generally applicable accurately discerns mechanism’s presence amid negative controls outliers, adaptively designs desired conditions, quantitatively extracts kinetic information step spanning over 7 orders magnitude, from which insights into oxidative addition pathways gained. This work opens opportunities discoveries self-driving electrochemistry laboratories without manual intervention.

Language: Английский

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AI for organic and polymer synthesis

Science China Chemistry, Journal Year: 2024, Volume and Issue: 67(8), P. 2461 - 2496

Published: June 26, 2024

Language: Английский

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Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion

Applied Physics Reviews, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 6, 2025

Electrochemical reactions are pivotal for energy conversion and storage to achieve a carbon-neutral sustainable society, optimal electrocatalysts essential their industrial applications. Theoretical modeling methodologies, such as density functional theory (DFT) molecular dynamics (MD), efficiently assess electrochemical reaction mechanisms electrocatalyst performance at atomic levels. However, its intrinsic algorithm limitations high computational costs large-scale systems generate gaps between experimental observations calculation simulation, restricting the accuracy efficiency of design. Combining machine learning (ML) is promising strategy accelerate development electrocatalysts. The ML-DFT frameworks establish accurate property–structure–performance relations predict verify novel electrocatalysts' properties performance, providing deep understanding mechanisms. ML-based methods also solution MD DFT. Moreover, integrating ML experiment characterization techniques represents cutting-edge approach insights into structural, electronic, chemical changes under working conditions. This review will summarize DFT current application status design in various conversions. underlying physical fundaments, advancements, challenges be summarized. Finally, future research directions prospects proposed guide revolution.

Language: Английский

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Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit

Journal of Chemical Information and Modeling, Journal Year: 2023, Volume and Issue: 63(12), P. 3659 - 3668

Published: June 14, 2023

Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount reaction data is used train these models, which in stark contrast how expert chemists discover and develop new reactions by leveraging information from a small number relevant transformations. Transfer active two strategies that can operate low-data situations, may help fill this gap promote the use machine for tackling real-world challenges synthesis. This Perspective introduces transfer connects potential opportunities directions further research, especially area prospective development

Language: Английский

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A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target

Chemical Science, Journal Year: 2024, Volume and Issue: 15(10), P. 3640 - 3660

Published: Jan. 1, 2024

A genetic optimization strategy to discover asymmetric organocatalysts with high activity and enantioselectivity across a broad substrate scope.

Language: Английский

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