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data-driven-exploration-of-silver-nanoplate-formation-in-multidimensional-chemical-design-spaces
Oktober 2, 2024 In Unkategorisiert By Huat Thart Chiang

Data-driven exploration of silver nanoplate formation in multidimensional chemical design spaces

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00211C, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Huat Thart Chiang, Kiran Vaddi, Lilo PozzoWe present an autonomous data-driven framework that iteratively explores the experimental design space of silver nanoparticle synthesis to

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cost-informed-bayesian-reaction-optimization
Oktober 2, 2024 In Unkategorisiert By Alexandre Alain Schoepfer

Cost-Informed Bayesian Reaction Optimization

Digital Discovery, 2024, Accepted ManuscriptDOI: 10.1039/D4DD00225C, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Alexandre Alain Schoepfer, Jan Weinreich, Ruben Laplaza, Jerome Waser, Clemence CorminboeufBayesian optimization (BO) is an efficient method for solving complex optimization problems, including those

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machine-learning-assisted-analysis-of-dry-and-lubricated-tribological-properties-of-al–co–cr–fe–ni-high-entropy-alloy
Oktober 2, 2024 In Unkategorisiert By Saurabh Vashistha

Machine learning-assisted analysis of dry and lubricated tribological properties of Al–Co–Cr–Fe–Ni high entropy alloy

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00169A, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Saurabh Vashistha, Bashista Kumar Mahanta, Vivek Kumar Singh, Neha Sharma, Anjan Ray, Saurabh Dixit, Shailesh Kumar SinghThis study marks a notable advancement in tribology

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a-comprehensive-review-of-emerging-approaches-in-machine-learning-for-de-novo-protac-design
Oktober 2, 2024 In Unkategorisiert By Yossra Gharbi

A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design

Digital Discovery, 2024, Accepted ManuscriptDOI: 10.1039/D4DD00177J, Review Article Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Yossra Gharbi, Rocio MercadoTargeted protein degradation (TPD) is a rapidly growing field in modern drug discovery that aims to regulate the intracellular

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learning-material-synthesis–process–structure–property-relationship-by-data-fusion:-bayesian-co-regionalization-n-dimensional-piecewise-function-learning
Oktober 2, 2024 In Unkategorisiert By A. Gilad Kusne

Learning material synthesis–process–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00048J, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.A. Gilad Kusne, Austin McDannald, Brian DeCostAutonomous materials research labs require the ability to combine and learn from diverse data streams.To cite this article

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ampere:-automated-modular-platform-for-expedited-and-reproducible-electrochemical-testing
Oktober 2, 2024 In Unkategorisiert By Jehad Abed

AMPERE: Automated modular platform for expedited and reproducible electrochemical testing

Digital Discovery, 2024, Accepted ManuscriptDOI: 10.1039/D4DD00203B, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Jehad Abed, Yang Bai, Daniel Ethan Persaud, Jiheon Kim, Julia Witt, Jason Hattrick-Simpers, Edward H SargentThe rapid and reproducible electrochemical testing is a crucial

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composite-machine-learning-strategy-for-natural-products-taxonomical-classification-and-structural-insights
Oktober 2, 2024 In Unkategorisiert By Qisong Xu

Composite machine learning strategy for natural products taxonomical classification and structural insights

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00155A, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Qisong Xu, Alan K. X. Tan, Liangfeng Guo, Yee Hwee Lim, Dillon W. P. Tay, Shi Jun AngA composite machine learning model combining graph

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stability-and-transferability-of-machine-learning-force-fields-for-molecular-dynamics-applications
Oktober 2, 2024 In Unkategorisiert By Salatan Duangdangchote

Stability and Transferability of Machine Learning Force Fields for Molecular Dynamics Applications

Digital Discovery, 2024, Accepted ManuscriptDOI: 10.1039/D4DD00140K, Communication Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Salatan Duangdangchote, Dwight S. Seferos, Oleksandr VoznyyIn this study, we focus on simplifying the generation of Machine Learning Force Fields (MLFFs) for Molecular Dynamics

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transfer-learning-based-on-atomic-feature-extraction-for-the-prediction-of-experimental-13c-chemical-shifts
Oktober 2, 2024 In Unkategorisiert By Zarko Ivkovic

Transfer learning based on atomic feature extraction for the prediction of experimental 13C chemical shifts

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D4DD00168K, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Žarko Ivković, Jesús Jover, Jeremy HarveyAtomic feature extraction as a solution for low-data regimes in chemistry.To cite this article before page numbers are assigned,

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