Machine learning-assisted low-dimensional electrocatalysts design for hydrogen evolution reaction
J Li, N Wu, J Zhang, HH Wu, K Pan, Y Wang, G Liu… - Nano-Micro Letters, 2023 - Springer
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 …
AI for organic and polymer synthesis
Recent years have witnessed the transformative impact from the integration of artificial
intelligence with organic and polymer synthesis. This synergy offers innovative and …
intelligence with organic and polymer synthesis. This synergy offers innovative and …
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 …