Publications (8)
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…
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…
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…
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…