Machine learning-assisted low-dimensional electrocatalysts design for hydrogen evolution reaction
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …
Nevertheless, the conventional" trial and error" method for producing advanced …
Fast predictions of reaction barrier heights: toward coupled-cluster accuracy
KA Spiekermann, L Pattanaik… - The Journal of Physical …, 2022 - ACS Publications
Quantitative estimates of reaction barriers are essential for developing kinetic mechanisms
and predicting reaction outcomes. However, the lack of experimental data and the steep …
and predicting reaction outcomes. However, the lack of experimental data and the steep …
Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
Predicting protein–ligand binding and unbinding kinetics with biased MD simulations and coarse-graining of dynamics: Current state and challenges
S Wolf - Journal of Chemical Information and Modeling, 2023 - ACS Publications
The prediction of drug–target binding and unbinding kinetics that occur on time scales
between milliseconds and several hours is a prime challenge for biased molecular …
between milliseconds and several hours is a prime challenge for biased molecular …
High-energy and long-lasting organic electrode for a rechargeable aqueous battery
Redox-active organic materials (ROMs) hold great promise as potential electrode materials
for eco-friendly, cost-effective, and sustainable batteries; however, the poor cycle stability …
for eco-friendly, cost-effective, and sustainable batteries; however, the poor cycle stability …
Comment on 'physics-based representations for machine learning properties of chemical reactions'
In a recent article in this journal, van Gerwen et al (2022 Mach. Learn.: Sci. Technol. 3
045005) presented a kernel ridge regression model to predict reaction barrier heights. Here …
045005) presented a kernel ridge regression model to predict reaction barrier heights. Here …
Reinforcement learning for traversing chemical structure space: Optimizing transition states and minimum energy paths of molecules
R Barrett, J Westermayr - The Journal of Physical Chemistry …, 2024 - ACS Publications
In recent years, deep learning has made remarkable strides, surpassing human capabilities
in tasks, such as strategy games, and it has found applications in complex domains …
in tasks, such as strategy games, and it has found applications in complex domains …
[HTML][HTML] Integrating model-based design of experiments and computer-aided solvent design
L Gui, Y Yu, TO Oliyide, E Siougkrou… - Computers & Chemical …, 2023 - Elsevier
Computer-aided molecular design (CAMD) methods can be used to generate promising
solvents with enhanced reaction kinetics, given a reliable model of solvent effects on …
solvents with enhanced reaction kinetics, given a reliable model of solvent effects on …
A Machine Learning Approach for Rate Constants. III. Application to the Cl(2P) + CH4 → CH3 + HCl Reaction
The temperature dependence of the thermal rate constant for the reaction Cl (3P)+ CH4→
HCl+ CH3 is calculated using a Gaussian Process machine learning (ML) approach to train …
HCl+ CH3 is calculated using a Gaussian Process machine learning (ML) approach to train …
[PDF][PDF] Machine Learning Validation via Rational Dataset Sampling with astartes
Machine Learning (ML) has become an increasingly popular tool to accelerate traditional
workflows. Critical to the use of ML is the process of splitting datasets into training …
workflows. Critical to the use of ML is the process of splitting datasets into training …