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Recent advances in artificial intelligence boosting materials design for electrochemical energy storage
In the rapidly evolving landscape of electrochemical energy storage (EES), the advent of
artificial intelligence (AI) has emerged as a keystone for innovation in material design …
artificial intelligence (AI) has emerged as a keystone for innovation in material design …
Exploring chemical reaction space with machine learning models: Representation and feature perspective
Chemical reactions serve as foundational building blocks for organic chemistry and drug
design. In the era of large AI models, data-driven approaches have emerged to innovate the …
design. In the era of large AI models, data-driven approaches have emerged to innovate the …
Structure-based drug design with equivariant diffusion models
Abstract Structure-based drug design (SBDD) aims to design small-molecule ligands that
bind with high affinity and specificity to pre-determined protein targets. Generative SBDD …
bind with high affinity and specificity to pre-determined protein targets. Generative SBDD …
Designing membranes with specific binding sites for selective ion separations
A new class of membranes that can separate ions of similar size and charge is highly
desired for resource recovery, water reuse and energy storage technologies. These …
desired for resource recovery, water reuse and energy storage technologies. These …
A survey of geometric graph neural networks: Data structures, models and applications
Geometric graph is a special kind of graph with geometric features, which is vital to model
many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical …
many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical …
Diffusion-based generative AI for exploring transition states from 2D molecular graphs
The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction
mechanisms and modeling their kinetics. Recently, machine learning (ML) models have …
mechanisms and modeling their kinetics. Recently, machine learning (ML) models have …
Retrobridge: Modeling retrosynthesis with markov bridges
Retrosynthesis planning is a fundamental challenge in chemistry which aims at designing
reaction pathways from commercially available starting materials to a target molecule. Each …
reaction pathways from commercially available starting materials to a target molecule. Each …
Analytical ab initio hessian from a deep learning potential for transition state optimization
Identifying transition states—saddle points on the potential energy surface connecting
reactant and product minima—is central to predicting kinetic barriers and understanding …
reactant and product minima—is central to predicting kinetic barriers and understanding …
Reinforcement learning for traversing chemical structure space: Optimizing transition states and minimum energy paths of molecules
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 …
OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion
Organometallic complexes are ubiquitous in numerous technological applications, and in
particular in homogeneous catalysis. Optimization of such complexes for specific …
particular in homogeneous catalysis. Optimization of such complexes for specific …