158 citations · 261 across the 15 of their papers we have counts for
16 papers
Flux-Matched Poisson Iteration for Generated Jacobian Equations: Local Convergence and Spectral Galerkin Approximation
Haoxiang Zhang, Yixuan Zhang
We develop a flux-matched Poisson iteration for solving generated Jacobian equations with second boundary conditions. In each step, it solves a fixed shifted Poisson problem with a…
Physics-informed Bayesian Optimization for Quantitative High-Resolution Transmission Electron Microscopy
Xiankang Tang, Yixuan Zhang, Juri Barthel +4
Quantitative high-resolution transmission electron microscopy (HRTEM) provides an indispensable means to understand the structure-property relationships of a material in atomic dim…
Information-Theoretic Multi-Model Fusion for Target-Oriented Adaptive Sampling in Materials Design
Yixuan Zhang, Zhiyuan Li, Weijia He +4
Target-oriented discovery under limited evaluation budgets requires making reliable progress in high-dimensional, heterogeneous design spaces where each new measurement is costly,…
Accelerated Design of Mechanically Hard Magnetically Soft High-entropy Alloys via Multi-objective Bayesian Optimization
Mian Dai, Yixuan Zhang, Weijia He +7
Designing high-entropy alloys (HEAs) that are both mechanically hard and possess soft magnetic properties is inherently challenging, as a trade-off is needed for mechanical and mag…
SuperSalt: Equivariant Neural Network Force Fields for Multicomponent Molten Salts System
Chen Shen, Siamak Attarian, Yixuan Zhang +4
Molten salts are crucial for clean energy applications, yet exploring their thermophysical properties across diverse chemical space remains challenging. We present the development…
Stable diffusion for the inverse design of microstructures
Yixuan Zhang, Teng Long, Hongbin Zhang
In materials science, microstructures and their associated extrinsic properties are critical for engineering advanced structural and functional materials, yet their robust reconstr…
Generative deep learning for the inverse design of materials
Teng Long, Yixuan Zhang, Hongbin Zhang
In addition to the forward inference of materials properties using machine learning, generative deep learning techniques applied on materials science allow the inverse design of ma…
Kalman filter enhanced Adversarial Bayesian optimization for active sampling in inelastic neutron scattering
Nihad Abuawwad, Yixuan Zhang, Samir Lounis +1
Spin waves, or magnons, are fundamental excitations in magnetic materials that provide insights into their dynamic properties and interactions. Magnons are the building blocks of m…
Bayesian optimization with active learning of Ta-Nb-Hf-Zr-Ti system for spin transport properties
Ruiwen Xie, Yixuan Zhang, Fu Li +2
Designing materials with enhanced spin charge conversion, i.e., with high spin Hall conductivity (SHC) and low longitudinal electric conductivity (hence large spin Hall angle (SHA)…
Accelerated Screening of Ternary Chalcogenides for High-Performance Optoelectronic Materials
Chen Shen, Tianshu Li, Yixuan Zhang +7
Chalcogenides, which refer to chalcogen anions, have attracted considerable attention in multiple fields of applications, such as optoelectronics, thermoelectrics, transparent cont…
Autonomous atomic Hamiltonian construction and active sampling of x-ray absorption spectroscopy by adversarial Bayesian optimization
Yixuan Zhang, Ruiwen Xie, Teng Long +4
X-ray absorption spectroscopy (XAS) is a well-established method for in-depth characterization of the electronic structure due to its sensitivity to the local coordination and elec…
Novel Two-Dimensional Layered MSiN (M = Mo, W): New Promising Thermal Management Materials
Chen Shen, Lei Wang, Donghai Wei +4
With the miniaturization and integration of nanoelectronic devices, efficient heat removal becomes a key factor affecting the reliable operation of the nanoelectronic device. With…
Inverse design of crystal structures for multicomponent systems
Teng Long, Yixuan Zhang, Nuno M. Fortunato +3
We developed an inverse design framework enabling automated generation of stable multi-component crystal structures by optimizing the formation energies in the latent space based o…
Thermal conductivity of h-BN monolayers using machine learning interatomic potential
Yixuan Zhang, Chen Shen, Teng Long +1
Thermal management materials are of critical importance for engineering miniaturized electronic devices, where theoretical design of such materials demands the evaluation of therma…
Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures
Teng Long, Nuno M. Fortunato, Ingo Opahle +5
Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput scre…
An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature
T. Long, N. M. Fortunato, Yixuan Zhang +2
Magnetic materials have a plethora of applications ranging from informatics to energy harvesting and conversion. However, such functionalities are limited by the magnetic ordering…