Search Results for author:
Found 16 papers, 8 papers with code
MatterGen: a generative model for inorganic materials design
We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset.
Learning to See Physical Properties with Active Sensing Motor Policies
We show that the visual system trained with a small amount of real-world traversal data accurately predicts physical parameters.
Learning Interatomic Potentials at Multiple Scales
When running MD, the MTS integrator then evaluates the smaller model for every time step and the larger model less frequently, accelerating simulation.
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry.
Experimental quantum natural gradient optimization in photonics
Our work opens up a vista of utilizing QNG in photonics to implement practical near-term quantum applications.
Quantum generative adversarial learning in photonics
Quantum Generative Adversarial Networks (QGANs), an intersection of quantum computing and machine learning, have attracted widespread attention due to their potential advantages over classical analogs.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
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.
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks
Molecular dynamics (MD) simulation is essential for various scientific domains but computationally expensive.
Fragment-based Sequential Translation for Molecular Optimization
Equipped with the learned fragment vocabulary, we propose Fragment-based Sequential Translation (FaST), which learns a reinforcement learning (RL) policy to iteratively translate model-discovered molecules into increasingly novel molecules while satisfying desired properties.
Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Generating the periodic structure of stable materials is a long-standing challenge for the material design community.
Learning Task Informed Abstractions
Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features.
Model-based Reinforcement Learning
reinforcement-learning
+1
Hybrid Interest Modeling for Long-tailed Users
In this work, we propose the Hybrid Interest Modeling (HIM) network to hybrid both personalized interest and semi-personalized interest in learning long-tailed users' preferences in the recommendation.
Variational Quantum Circuits for Quantum State Tomography
We demonstrate our method by performing numerical simulations for the tomography of the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator.
Modeling and Analysis of Tagging Networks in Stack Exchange Communities
Large Question-and-Answer (Q&A) platforms support diverse knowledge curation on the Web.
Robust Image Segmentation Using Contour-Guided Color Palettes
The contour-guided color palette (CCP) is proposed for robust image segmentation.
