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Found 17 papers, 13 papers with code
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability.
Learning Collective Variables with Synthetic Data Augmentation through Physics-inspired Geodesic Interpolation
In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs.
Machine-learning-accelerated simulations to enable automatic surface reconstruction
Here, we present a bi-faceted computational loop to predict surface phase diagrams of multi-component materials that accelerates both the energy scoring and statistical sampling methods.
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles
In this work, we examine multiple UQ schemes for improving the robustness of NN interatomic potentials (NNIPs) through active learning.
Automated patent extraction powers generative modeling in focused chemical spaces
Deep generative models have emerged as an exciting avenue for inverse molecular design, with progress coming from the interplay between training algorithms and molecular representations.
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
Coarse-graining (CG) accelerates molecular simulations of protein dynamics by simulating sets of atoms as singular beads.
Differentiable Simulations for Enhanced Sampling of Rare Events
Simulating rare events, such as the transformation of a reactant into a product in a chemical reaction typically requires enhanced sampling techniques that rely on heuristically chosen collective variables (CVs).
Learning Pair Potentials using Differentiable Simulations
We show that our methods can be used to simultaneously fit potentials for simulations at different compositions and temperatures to improve the transferability of the learned potentials.
Generative Coarse-Graining of Molecular Conformations
Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation.
Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential
For example, photoisomerization can allow a drug with a photo-switchable scaffold such as azobenzene to be activated with light.
GLAMOUR: Graph Learning over Macromolecule Representations
The near-infinite chemical diversity of natural and artificial macromolecules arises from the vast range of possible component monomers, linkages, and polymers topologies.
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.
Differentiable Molecular Simulations for Control and Learning
From the perspective of engineering, one wishes to control the Hamiltonian to achieve desired simulation outcomes and structures, as in self-assembly and optical control, to then realize systems with the desired Hamiltonian in the lab.
Generative Models for Automatic Chemical Design
Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care and many others.
Coarse-Graining Auto-Encoders for Molecular Dynamics
Autograin is trained to learn the optimal mapping between all-atom and reduced representation, using the reconstruction loss to facilitate the learning of coarse-grained variables.
Automatic chemical design using a data-driven continuous representation of molecules
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
We introduce a convolutional neural network that operates directly on graphs.
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