Search Results for author:
Found 12 papers, 9 papers with code
Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules
Discovery of high-performance materials and molecules requires identifying extremes with property values that fall outside the known distribution.
Univariate Conditional Variational Autoencoder for Morphogenic Patterns Design in Frontal Polymerization-Based Manufacturing
Under some initial and boundary conditions, the rapid reaction-thermal diffusion process taking place during frontal polymerization (FP) destabilizes the planar mode of front propagation, leading to spatially varying, complex hierarchical patterns in thermoset polymeric materials.
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables
Generating a data set that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine learned interatomic potentials (MLIP).
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
We benchmark a collection of state-of-the-art (SOTA) ML FF models and illustrate, in particular, how the commonly benchmarked force accuracy is not well aligned with relevant simulation metrics.
Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential
We use our approach to predict the thermal half-lives of 19, 000 azobenzene derivatives.
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program.
Chemistry-informed Macromolecule Graph Representation for Similarity Computation and Supervised Learning
Macromolecules are large, complex molecules composed of covalently bonded monomer units, existing in different stereochemical configurations and topologies.
Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties
Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology.
Molecular machine learning with conformer ensembles
Here we investigate how the 3D information of multiple conformers, traditionally known as 4D information in the cheminformatics community, can improve molecular property prediction in deep learning models.
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
GEOM: Energy-annotated molecular conformations for property prediction and molecular generation
The Geometric Ensemble Of Molecules (GEOM) dataset contains conformers for 133, 000 species from QM9, and 317, 000 species with experimental data related to biophysics, physiology, and physical chemistry.
Graph similarity drives zeolite diffusionless transformations and intergrowth
Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis.
Graph Similarity
Materials Science
