Search Results for author:
Found 6 papers, 2 papers with code
Allegro-Legato: Scalable, Fast, and Robust Neural-Network Quantum Molecular Dynamics via Sharpness-Aware Minimization
Specifically, Allegro-Legato exhibits much weaker dependence of timei-to-failure on the problem size, $t_{\textrm{failure}} \propto N^{-0. 14}$ ($N$ is the number of atoms) compared to the SOTA Allegro model $\left(t_{\textrm{failure}} \propto N^{-0. 29}\right)$, i. e., systematically delayed time-to-failure, thus allowing much larger and longer NNQMD simulations without failure.
Multiscale Graph Neural Networks for Protein Residue Contact Map Prediction
Machine learning (ML) is revolutionizing protein structural analysis, including an important subproblem of predicting protein residue contact maps, i. e., which amino-acid residues are in close spatial proximity given the amino-acid sequence of a protein.
Graph Signal Recovery Using Restricted Boltzmann Machines
We propose a model-agnostic pipeline to recover graph signals from an expert system by exploiting the content addressable memory property of restricted Boltzmann machine and the representational ability of a neural network.
Physics-informed Neural-Network Software for Molecular Dynamics Applications
We have developed a novel differential equation solver software called PND based on the physics-informed neural network for molecular dynamics simulators.
Predictive Synthesis of Quantum Materials by Probabilistic Reinforcement Learning
Predictive materials synthesis is the primary bottleneck in realizing new functional and quantum materials.
Anisotropic structural dynamics of monolayer crystals revealed by femtosecond surface x-ray scattering
X-ray scattering is one of the primary tools to determine crystallographic configuration with atomic accuracy.
Materials Science
