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Found 7 papers, 5 papers with code
Machine Learning of Accurate Energy-conserving Molecular Force Fields
Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories.
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules.
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model.
CEREBRUM‐7T: Fast and Fully Volumetric Brain Segmentation of 7 Tesla MR Volumes
An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures.
Improvements in Remote Cardiopulmonary Measurement Using a Five Band Digital Camera
Remote measurement of the blood volume pulse via photoplethysmography (PPG) using digital cameras and ambient light has great potential for healthcare and affective computing.
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models
Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models training in reasonable time as well as distributing the storage requirements.
TF-Label: a Topological-Folding Labeling Scheme for Reachability Querying in a Large Graph
We propose TF-label, an efficient and scalable labeling scheme for processing reachability queries.
