High-Rate Quinone Cathodes and Nafion Conditioning for Improved Stability in Aqueous Zinc-Ion Batteries DOI
Pedaballi Sireesha,

Kaylie A. McCracken,

William T. McLeod

et al.

ACS Applied Materials & Interfaces, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

The growing need for fast and reliable energy delivery in various applications ranging from electric vehicles portable electronics to grid-scale storage demands high-performance systems capable of operating at high charge/discharge rates (C-rates). Aqueous zinc-ion batteries (AZIBs) offer a promising alternative conventional lithium-ion primarily due their inherent safety, environmental friendliness, low cost, theoretical capacity. Quinone-based cathodes, with redox kinetics capacities, are particularly suitable high-rate applications. However, practical application AZIBs is limited by solubility aqueous electrolytes, leading significant capacity fading poor long-term cycling stability, especially elevated C-rates. To address these challenges, this study investigates the use Nafion membranes as ion-selective barriers stabilize quinone cathodes prevent dissolution active materials. evaluates four quinone-based cathodes─2,3,5,6-tetrachloro-1,4-benzoquinone (TCBQ), 1,4-naphthoquinone (NQ), anthraquinone (AQ), poly(2-chloro-3,5,6-trisulfide-1,4-benzoquinone) (PCTBQ)─in AZIBs, focusing on effect membrane conditioning 1 M ZnSO4 electrolyte. results demonstrate that optimized significantly enhances stability performance reducing dissolution, improving cyclability, maintaining stable retention under conditions, i.e., 35C. These findings emphasize importance its potential advance development durable, rapid

Language: Английский

Computer-aided multi-objective optimization in small molecule discovery DOI Creative Commons
Jenna C. Fromer,

Connor W. Coley

Patterns, Journal Year: 2023, Volume and Issue: 4(2), P. 100678 - 100678

Published: Feb. 1, 2023

Molecular discovery is a multi-objective optimization problem that requires identifying molecule or set of molecules balance multiple, often competing, properties. Multi-objective molecular design commonly addressed by combining properties interest into single objective function using scalarization, which imposes assumptions about relative importance and uncovers little the trade-offs between objectives. In contrast to Pareto does not require knowledge reveals However, it introduces additional considerations in algorithm design. this review, we describe pool-based de novo generative approaches with focus on algorithms. We show how relatively direct extension Bayesian plethora different models extend from single-objective similar ways non-dominated sorting reward (reinforcement learning) select for retraining (distribution propagation (genetic algorithms). Finally, discuss some remaining challenges opportunities field, emphasizing opportunity adopt techniques

Language: Английский

Citations

77
Self-Driving Laboratories for Chemistry and Materials Science DOI Creative Commons
Gary Tom, Stefan P. Schmid, Sterling G. Baird

et al.

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

Citations

56
Metal-organic frameworks-based materials: A feasible path for redox flow battery DOI

Tangju Yuan,

Shaotian Qi,

Lingzhi Ye

et al.

Coordination Chemistry Reviews, Journal Year: 2025, Volume and Issue: 531, P. 216503 - 216503

Published: Feb. 10, 2025

Language: Английский

Citations

3
Guided diffusion for inverse molecular design DOI
Tomer Weiss, Eduardo Mayo Yanes, Sabyasachi Chakraborty

et al.

Nature Computational Science, Journal Year: 2023, Volume and Issue: 3(10), P. 873 - 882

Published: Oct. 5, 2023

Language: Английский

Citations

37
Self-play reinforcement learning guides protein engineering DOI
Yi Wang, Hui Tang, Lichao Huang

et al.

Nature Machine Intelligence, Journal Year: 2023, Volume and Issue: 5(8), P. 845 - 860

Published: July 20, 2023

Language: Английский

Citations

36
Deep learning metal complex properties with natural quantum graphs DOI Creative Commons
Hannes Kneiding, Ruslan Lukin, Lucas Lang

et al.

Digital Discovery, Journal Year: 2023, Volume and Issue: 2(3), P. 618 - 633

Published: Jan. 1, 2023

Deep graph learning based on electronic structure can contribute to the accelerated discovery of transition metal complexes.

Language: Английский

Citations

27
Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge DOI
Felix Strieth‐Kalthoff, Sara Szymkuć, Karol Molga

et al.

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: unknown

Published: April 10, 2024

Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, development AI synthesis planners trained solely on reaction-example-data has stagnated and is not par with performance "hybrid" algorithms combining expert knowledge. This Perspective examines possible causes these shortcomings, extending beyond established reasoning insufficient quantities reaction data. Drawing attention to intricacies data biases that are specific domain synthetic we advocate augmenting unique capabilities knowledge base strategies experts. By actively involving chemists, who end users any software, into process, envision bridge gap between computer intricate nature synthesis.

Language: Английский

Citations

16
Directional multiobjective optimization of metal complexes at the billion-system scale DOI
Hannes Kneiding, Ainara Nova, David Balcells

et al.

Nature Computational Science, Journal Year: 2024, Volume and Issue: 4(4), P. 263 - 273

Published: March 29, 2024

Language: Английский

Citations

15
HydrogelFinder: A Foundation Model for Efficient Self‐Assembling Peptide Discovery Guided by Non‐Peptidal Small Molecules DOI Creative Commons

Xuanbai Ren,

Jiaying Wei,

Xiaoli Luo

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: 11(26)

Published: May 5, 2024

Abstract Self‐assembling peptides have numerous applications in medicine, food chemistry, and nanotechnology. However, their discovery has traditionally been serendipitous rather than driven by rational design. Here, HydrogelFinder, a foundation model is developed for the design of self‐assembling from scratch. This explores self‐assembly properties molecular structure, leveraging 1,377 non‐peptidal small molecules to navigate chemical space improve structural diversity. Utilizing 111 peptide candidates are generated synthesized 17 peptides, subsequently experimentally validating biophysical characteristics nine ranging 1–10 amino acids—all achieved within 19‐day workflow. Notably, two de novo‐designed demonstrated low cytotoxicity biocompatibility, as confirmed live/dead assays. work highlights capacity HydrogelFinder diversify through molecules, offering powerful toolkit paradigm future endeavors.

Language: Английский

Citations

15
Accelerating Computation of Acidity Constants and Redox Potentials for Aqueous Organic Redox Flow Batteries by Machine Learning Potential-Based Molecular Dynamics DOI
Feng Wang,

Ze-Bing Ma,

Jun Cheng

et al.

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(21), P. 14566 - 14575

Published: April 24, 2024

Due to the increased concern about energy and environmental issues, significant attention has been paid development of large-scale storage devices facilitate utilization clean sources. The redox flow battery (RFB) is one most promising systems. Recently, high cost transition-metal complex-based RFB promoted aqueous RFBs with redox-active organic molecules. To expand working voltage, computational chemistry applied search for molecules lower or higher potentials. However, potential computation based on implicit solvation models would be challenging due difficulty in parametrization when considering complex supporting electrolytes. Besides, although ab initio molecular dynamics (AIMD) describes electrolytes same level electronic structure theory as couple, application impeded by costs. machine learning (MLMD) illustrated accelerate AIMD several orders magnitude without sacrificing accuracy. It established that potentials can computed MLMD two separated (MLPs) reactant product states, which redundant inefficient. In this work, an automated workflow developed construct a universal MLP both compute acidity constants more efficiently. Furthermore, predicted evaluated at hybrid functional much costs, design RFBs.

Language: Английский

Citations

12