[HTML][HTML] SELFIES and the future of molecular string representations
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …
applications to challenging tasks in chemistry and materials science. Examples include the …
Machine intelligence for chemical reaction space
Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …
accessible chemical space are critical drivers for major technological advances and more …
[HTML][HTML] Inverse design of 3d molecular structures with conditional generative neural networks
The rational design of molecules with desired properties is a long-standing challenge in
chemistry. Generative neural networks have emerged as a powerful approach to sample …
chemistry. Generative neural networks have emerged as a powerful approach to sample …
A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
Geomol: Torsional geometric generation of molecular 3d conformer ensembles
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key
role in areas of cheminformatics and drug discovery. Existing generative models have …
role in areas of cheminformatics and drug discovery. Existing generative models have …
[HTML][HTML] 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 …
Machine learning for design principles for single atom catalysts towards electrochemical reactions
Machine learning (ML) integrated density functional theory (DFT) calculations have recently
been used to accelerate the design and discovery of heterogeneous catalysts such as single …
been used to accelerate the design and discovery of heterogeneous catalysts such as single …
Modulating the microenvironment of single atom catalysts with tailored activity to benchmark the CO2 reduction
Extreme fossil fuel consumption results in increasing the emanation of carbon dioxide (CO
2) in the atmosphere and fosters ecocrisis. The CO 2 electrocatalytic reduction has together …
2) in the atmosphere and fosters ecocrisis. The CO 2 electrocatalytic reduction has together …
[HTML][HTML] Prospective de novo drug design with deep interactome learning
De novo drug design aims to generate molecules from scratch that possess specific
chemical and pharmacological properties. We present a computational approach utilizing …
chemical and pharmacological properties. We present a computational approach utilizing …
Efficient parametrization of the atomic cluster expansion
The atomic cluster expansion (ACE) provides a general, local, and complete representation
of atomic energies. Here we present an efficient framework for parametrization of ACE …
of atomic energies. Here we present an efficient framework for parametrization of ACE …