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Found 7 papers, 3 papers with code
Information theory unifies atomistic machine learning, uncertainty quantification, and materials thermodynamics
An accurate description of information is relevant for a range of problems in atomistic modeling, such as sampling methods, detecting rare events, analyzing datasets, or performing uncertainty quantification (UQ) in machine learning (ML)-driven simulations.
Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances
In combination with template design, this work can accelerate the exploration of the space of synthesis conditions for zeolites.
Data efficiency and extrapolation trends in neural network interatomic potentials
In this work, we show how architectural and optimization choices influence the generalization of NNIPs, revealing trends in molecular dynamics (MD) stability, data efficiency, and loss landscapes.
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data.
Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks
The potential of mean force is expressed as two jointly-trained neural network interatomic potentials that learn the coupled short-range and the many-body long range molecular interactions.
Computational Physics Materials Science
Generative Models for Automatic Chemical Design
Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care and many others.
Graph similarity drives zeolite diffusionless transformations and intergrowth
Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis.
Graph Similarity
Materials Science
