Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: 14(20)

Published: Feb. 14, 2024

Abstract Machine learning (ML) exhibits substantial potential for predicting the properties of solid‐state electrolytes (SSEs). By integrating experimental or/and simulation data within ML frameworks, discovery and development advanced SSEs can be accelerated, ultimately facilitating their application in high‐end energy storage systems. This review commences with an introduction to background SSEs, including explicit definition, comprehensive classification, intrinsic physical/chemical properties, underlying mechanisms governing conductivity, challenges, future developments. An in‐depth explanation methodology is also elucidated. Subsequently, key factors that influence performance are summarized, thermal expansion, modulus, diffusivity, ionic reaction energy, migration barrier, band gap, activation energy. Finally, it offered perspectives on design prerequisites upcoming generations focusing real‐time property prediction, multi‐property optimization, multiscale modeling, transfer learning, automation high‐throughput experimentation, synergistic optimization full battery, all which crucial accelerating progress SSEs. aims guide novel SSE materials practical realization efficient reliable technologies.

Language: Английский

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Navigating the landscape of enzyme design: from molecular simulations to machine learning

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: 53(16), P. 8202 - 8239

Published: Jan. 1, 2024

Global environmental issues and sustainable development call for new technologies fine chemical synthesis waste valorization. Biocatalysis has attracted great attention as the alternative to traditional organic synthesis. However, it is challenging navigate vast sequence space identify those proteins with admirable biocatalytic functions. The recent of deep-learning based structure prediction methods such AlphaFold2 reinforced by different computational simulations or multiscale calculations largely expanded 3D databases enabled structure-based design. While approaches shed light on site-specific enzyme engineering, they are not suitable large-scale screening potential biocatalysts. Effective utilization big data using machine learning techniques opens up a era accelerated predictions. Here, we review applications machine-learning guided We also provide our view challenges perspectives effectively employing design integrating molecular learning, importance database construction algorithm in attaining predictive ML models explore fitness landscape

Language: Английский

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Machine learning applications for electrospun nanofibers: a review

Journal of Materials Science, Journal Year: 2024, Volume and Issue: 59(31), P. 14095 - 14140

Published: July 30, 2024

Abstract Electrospun nanofibers have gained prominence as a versatile material, with applications spanning tissue engineering, drug delivery, energy storage, filtration, sensors, and textiles. Their unique properties, including high surface area, permeability, tunable porosity, low basic weight, mechanical flexibility, alongside adjustable fiber diameter distribution modifiable wettability, make them highly desirable across diverse fields. However, optimizing the properties of electrospun to meet specific requirements has proven be challenging endeavor. The electrospinning process is inherently complex influenced by numerous variables, applied voltage, polymer concentration, solution flow rate, molecular weight polymer, needle-to-collector distance. This complexity often results in variations nanofibers, making it difficult achieve desired characteristics consistently. Traditional trial-and-error approaches parameter optimization been time-consuming costly, they lack precision necessary address these challenges effectively. In recent years, convergence materials science machine learning (ML) offered transformative approach electrospinning. By harnessing power ML algorithms, scientists researchers can navigate intricate space more efficiently, bypassing need for extensive experimentation. holds potential significantly reduce time resources invested producing wide range applications. Herein, we provide an in-depth analysis current work that leverages obtain target nanofibers. examining work, explore intersection ML, shedding light on advancements, challenges, future directions. comprehensive not only highlights processes but also provides valuable insights into evolving landscape, paving way innovative precisely engineered various Graphical abstract

Language: Английский

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Machine Learning-Assisted Design of Advanced Polymeric Materials

Accounts of Materials Research, Journal Year: 2024, Volume and Issue: 5(5), P. 571 - 584

Published: April 16, 2024

ConspectusPolymeric material research is encountering a new paradigm driven by machine learning (ML) and big data. The ML-assisted design has proven to be successful approach for designing novel high-performance polymeric materials. This goal mainly achieved through the following procedure: structure representation database construction, establishment of ML-based property prediction model, virtual high-throughput screening. key this lies in training ML models that delineate structure–property relationships based on available polymer data (e.g., structure, component, data), enabling screening promising polymers satisfy targeted requirements. However, relative scarcity high-quality complex multiscale pose challenges method, such as modeling challenges.In Account, we summarize state-of-the-art advancements concerning Regarding digital representations are predominant methods cheminformatics along with some newly developed integrate characteristics. When establishing choosing optimizing attain high-precision predictions across vast chemical space. Advanced algorithms, transfer multitask learning, have been utilized address challenges. During process, defining combining genes, candidates generated, subsequently, their properties predicted screened using models. Finally, identified verified computer simulations experiments.We provide an overview our recent efforts toward developing approaches discovering advanced materials emphasize intricate nature structural design. To well describe structures polymers, methods, fingerprint cross-linking descriptors, were developed. Moreover, multifidelity method was proposed leverage multisource isomerous from experiments simulations. Additionally, graph neural networks Bayesian optimization applied predicting compositions.Finally, identify current point out development directions emerging field. It highly desirable establish materials, particularly when constructing large language. Through seek stimulate further interest foster active collaborations realizing innovation

Language: Английский

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Applying statistical modeling strategies to sparse datasets in synthetic chemistry

Science Advances, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 1, 2025

The application of statistical modeling in organic chemistry is emerging as a standard practice for probing structure-activity relationships and predictive tool many optimization objectives. This review aimed tutorial those entering the area chemistry. We provide case studies to highlight considerations approaches that can be used successfully analyze datasets low data regimes, common situation encountered given experimental demands Statistical hinges on (what being modeled), descriptors (how are represented), algorithms modeled). Herein, we focus how various reaction outputs (e.g., yield, rate, selectivity, solubility, stability, turnover number) structures binned, heavily skewed, distributed) influence choice algorithm constructing chemically insightful models.

Language: Английский

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Integrated Molecular Modeling and Machine Learning for Drug Design

Journal of Chemical Theory and Computation, Journal Year: 2023, Volume and Issue: 19(21), P. 7478 - 7495

Published: Oct. 26, 2023

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations usually required to bring a new drug the market. Computational approaches have increasingly become an indispensable part of helping reduce time cost research drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling machine learning develop computational tools for modulator design, including pocket-guided rational design approach based AlphaSpace target protein-protein interactions, delta scoring functions protein-ligand docking as well virtual screening, state-of-the-art deep models predict calculated experimental properties mechanics optimized geometries. Meanwhile, discuss remaining challenges promising directions further use retrospective example FDA approved kinase inhibitor Erlotinib demonstrate these newly developed tools.

Language: Английский

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Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight

International Journal of Emerging Multidisciplinaries Computer Science & Artificial Intelligence, Journal Year: 2023, Volume and Issue: 2(1)

Published: Nov. 25, 2023

The fascination with understanding student academic performance has drawn widespread attention from various stakeholders, including parents, policymakers, and businesses. 'Students Performance in Exams' dataset, available on platforms like Kaggle, stands as a treasure trove. It extends beyond test scores, encompassing diverse attributes ethnicity, gender, parental education, preparation, even lunch type. In our tech-driven age, predicting success become compelling pursuit. This study aims to delve deep into this utilizing data mining methods robust classification algorithms Logistic Regression Random Forest Jupyter Notebook environment. Rigorous model training, testing, fine-tuning strive for the utmost predictive accuracy. Data cleaning preprocessing play crucial role establishing reliable dataset accurate predictions. Beyond numbers, project emphasizes visualization's impact, transforming raw comprehensible insights effective communication. Model exhibits an impressive 87.6% accuracy, highlighting its potential performance. Moreover, excels remarkable 100% accuracy forecasting grades, showcasing effectiveness domain.

Language: Английский

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The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease

Drug Metabolism and Personalized Therapy, Journal Year: 2024, Volume and Issue: 39(2), P. 47 - 58

Published: June 1, 2024

The completion of the Human Genome Project in 2003 marked beginning a transformative era medicine. This milestone laid foundation for personalized medicine, an innovative approach that customizes healthcare treatments.

Language: Английский

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Data Generation for Machine Learning Interatomic Potentials and Beyond

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(24), P. 13681 - 13714

Published: Nov. 21, 2024

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides ML-based interatomic potentials have paved the way accurate modeling diverse chemical structural at atomic level. key determinant defining MLIP reliability remains quality training data. A paramount challenge lies constructing sets that capture specific domains vast space. This Review navigates intricate landscape essential components integrity data ensure extensibility transferability resulting models. We delve into details active learning, discussing its various facets implementations. outline different types uncertainty quantification applied to atomistic acquisition correlations between estimated true error. role samplers generating informative structures highlighted. Furthermore, we discuss via modified surrogate potential energy surfaces as innovative approach diversify also provides a list publicly available cover

Language: Английский

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Practical Machine Learning-Assisted Design Protocol for Protein Engineering: Transaminase Engineering for the Conversion of Bulky Substrates

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(9), P. 6462 - 6469

Published: April 12, 2024

Protein engineering is essential for improving the catalytic performance of enzymes applications in biocatalysis, which machine learning provides an emerging approach variant design. Transaminases are powerful biocatalysts stereoselective synthesis chiral amines but one major challenge their limited substrate scope. We present a general and practical design protocol protein to combine advantages three strategies, including directed evolution, rational design, learning, demonstrate application transaminases with higher activity toward bulky substrates. A high-quality data set was obtained by selected key positions, then applied create model transaminase activity. This data-assisted optimized variants, showed improved (up 3-fold over parent) substrates, maintaining enantioselectivity starting enzyme scaffold as well enantiomeric excess >99%ee).

Language: Английский

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