55 citations · 180 across the 8 of their papers we have counts for
8 papers
Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Images
Howard Yanxon, Eric Roberts, Hannah Parraga +6
Scientific researchers frequently use the in situ synchrotron high-energy powder X-ray diffraction (XRD) technique to examine the crystallographic structures of materials in functi…
MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies
Zhuowen Zhao, Tanny Chavez, Elizabeth A. Holman +14
Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data p…
Artifact Identification in X-ray Diffraction Data using Machine Learning Methods
Howard Yanxon, James Weng, Hannah Parraga +3
The in situ synchrotron high-energy X-ray powder diffraction (XRD) technique is highly utilized by researchers to analyze the crystallographic structures of materials in functional…
Short-range order and its impacts on the BCC NbMoTaW multi-principal element alloy by the machine-learning potential
Pedro A. Santos-Florez, Shi-Cheng Dai, Yi Yao +5
We employ a machine-learning force field, trained by a neural network (NN) with bispectrum coefficients as descriptors, to investigate the short-range order (SRO) influences on the…
Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics
Pedro A. Santos-Florez, Howard Yanxon, Byungkyun Kang +2
In this work, we present an efficient framework that combines machine learning potential (MLP) and metadynamics to explore multi-dimensional free energy surfaces for investigating…
PyXtal FF: a Python Library for Automated Force Field Generation
Howard Yanxon, David Zagaceta, Binh Tang +2
We present PyXtal FF, a package based on Python programming language, for developing machine learning potentials (MLPs). The aim of PyXtal FF is to promote the application of atomi…
Spectral Neural Network Potentials for Binary Alloys
David Zagaceta, Howard Yanxon, Qiang Zhu
In this work, we present a numerical implementation to compute the atom centered descriptors introduced by Bartok et al (Phys. Rev. B, 87, 184115, 2013) based on the harmonic analy…
Neural Networks Potential from the Bispectrum Component: A Case Study on Crystalline Silicon
Howard Yanxon, David Zagaceta, Brandon C. Wood +1
In this article, we present a systematic study in developing machine learning force fields (MLFF) for crystalline silicon. While the main-stream approach of fitting a MLFF is to us…