25 citations · 95 across the 16 of their papers we have counts for
16 papers
Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling
Jiaxin Zhang, Sirui Bi, Victor Fung
In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a…
Inverse design of two-dimensional materials with invertible neural networks
Victor Fung, Jiaxin Zhang, Guoxiang Hu +2
The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sam…
A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models
Jiaxin Zhang, Sirui Bi, Guannan Zhang
Bayesian experimental design (BED) aims at designing an experiment to maximize the information gathering from the collected data. The optimal design is usually achieved by maximizi…
A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Models
Jiaxin Zhang, Sirui Bi, Guannan Zhang
Bayesian experimental design (BED) is to answer the question that how to choose designs that maximize the information gathering. For implicit models, where the likelihood is intrac…
Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials
Sirui Bi, Jiaxin Zhang, Guannan Zhang
Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the ap…
Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics
Jiaxin Zhang, Congjie Wei, Chenglin Wu
For multilayer materials in thin substrate systems, interfacial failure is one of the most challenges. The traction-separation relations (TSR) quantitatively describe the mechanica…
Modern Monte Carlo Methods for Efficient Uncertainty Quantification and Propagation: A Survey
Jiaxin Zhang
Uncertainty quantification (UQ) includes the characterization, integration, and propagation of uncertainties that result from stochastic variations and a lack of knowledge or data…
A directional Gaussian smoothing optimization method for computational inverse design in nanophotonics
Jiaxin Zhang, Sirui Bi, Guannan Zhang
Local-gradient-based optimization approaches lack nonlocal exploration ability required for escaping from local minima in non-convex landscapes. A directional Gaussian smoothing (D…
A Novel Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization
Jiaxin Zhang, Hoang Tran, Dan Lu +1
We propose an improved evolution strategy (ES) using a novel nonlocal gradient operator for high-dimensional black-box optimization. Standard ES methods with -dimensional Gaussi…
Machine Learning the Effective Hamiltonian in High Entropy Alloys
Xianglin Liu, Jiaxin Zhang, Markus Eisenbach +1
The development of machine learning sheds new light on the problem of statistical thermodynamics in multicomponent alloys. However, a data-driven approach to construct the effectiv…
Fast and stable deep-learning predictions of material properties for solid solution alloys
Massimiliano Lupo Pasini, Ying Wai Li, Junqi Yin +3
We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The m…
Robust data-driven approach for predicting the configurational energy of high entropy alloys
Jiaxin Zhang, Xianglin Liu, Sirui Bi +3
High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially charact…
Chemical complexity in high entropy alloys: A pair-interaction perspective
Xianglin Liu, Jiaxin Zhang, Sirui Bi +3
The recently proposed pair-interaction model is applied to study a series of refractory high entropy alloys. The results demonstrate the simplicity, robustness, and high accuracy o…
Machine learning modeling of high entropy alloy: the role of short-range order
Xianglin Liu, Jiaxin Zhang, Markus Eisenbach +1
The development of machine learning sheds new light on the traditionally complicated problem of thermodynamics in multicomponent alloys. Successful application of such a method, ho…
On the quantification and efficient propagation of imprecise probabilities with copula dependence
Jiaxin Zhang, Michael D. Shields
This paper addresses the problem of quantification and propagation of uncertainties associated with dependence modeling when data for characterizing probability models are limited.…
Probability measure changes in Monte Carlo simulation
Jiaxin Zhang, Michael D. Shields
The objective of Bayesian inference is often to infer, from data, a probability measure for a random variable that can be used as input for Monte Carlo simulation. When datasets fo…