143 research outputs found
Calibration of Computational Mössbauer Spectroscopy to Unravel Active Sites in FeNC-Catalysts for the Oxygen Reduction Reaction
No full textElectrosynthetic Screening and Modern Optimization Strategies for Electrosynthesis of Highly Value‐added Products
Full text linkUnlike common analytical techniques such as cyclic voltammetry, statistics-based optimization tools are not yet often in the toolbox of preparative organic electrochemists. In general, experimental effort is not optimally utilized because the selection of experimental conditions is based on the one-variable-at-a-time principle. We will summarize statistically motivated optimization approaches already used in the context of electroorganic synthesis. We discuss the central ideas of these optimization methods which originate from other fields of chemistry in relation to electrosynthetic applications
Electrosynthetic Screening and Modern Optimization Strategies for Electrosynthesis of Highly Value‐added Products
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Quantitative Structure–Reactivity Relationships for Synthesis Planning: The Benzhydrylium Case
No full text60th Symposium on Theoretical Chemistry: Next-Generation Theoretical Chemistry
Full text linkThis year’s Symposium for Theoretical Chemistry (STC 2024) took place in Braunschweig at the beginning of September 2024. The Symposium for Theoretical Chemistry is the most important international conference for theoretical chemistry in the German-speaking countries and takes place annually at different locations in Germany, Austria or Switzerland. This year, almost 400 participants came to TU Braunschweig for this event. The conference was organized by Christoph Jacob and Jonny Proppe (TU Braunschweig). This year’s conference was held under the motto “Next-Generation Theoretical Chemistry”. Accordingly, STC 2024 celebrated both established and emerging fields in theoretical chemistry, including electronic structure theory, molecular dynamics, quantum computing, artificial intelligence, and uncertainty quantification. In doing so, it particularly featured young scientist working on diverse topics in theoretical chemistry
Theoretical Studies of the Acid–Base Equilibria in a Model Active Site of the Human 20S Proteasome
No full textregAL: Python package for active learning of regression problems
Full text linkIncreasingly more research areas rely on machine learning methods to accelerate discovery while saving resources. Machine learning models, however, usually require large datasets of experimental or computational results, which in certain fields—such as (bio)chemistry, materials science, or medicine—are rarely given and often prohibitively expensive to obtain. To bypass that obstacle, active learning methods are employed to develop machine learning models with a desired performance while requiring the least possible number of computational or experimental results from the domain of application. For this purpose, the model’s knowledge about certain regions of the application domain is estimated to guide the choice of the model’s training set. Although active learning is widely studied for classification problems (discrete outcomes), comparatively few works handle this method for regression problems (continuous outcomes). In this work, we present our Python package regAL , which allows users to evaluate different active learning strategies for regression problems. With a minimal input of just the dataset in question, but many additional customization and insight options, this package is intended for anyone who aims to perform and understand active learning in their problem-specific scope. Program summary Program title: regAL ^1 Program source: https://doi.org/10.5281/zenodo.15309124 , https://git.rz.tu-bs.de/proppe-group/active-learning/regAL Programming language: Python 3+ Program dependencies: numpy, scikit-learn, matplotlib, panda
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