Papers citing Machine learning on neutron and x-ray scattering and spectroscopies
Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction
Research Square · 2021
Eun-Ah Kim, Jordan Venderley, Michael Matty, Krishnanand Mallayya, Matthew Krogstad, Jacob Ruff, Geoff Pleiss, Varsha Kishore, David G. Mandrus, Daniel Phelan, Lekh Poudel, Andrew Gordon Wilson, Kilian Q. Weinberger, Puspa Upreti, Michael R. Norman, Stephan Rosenkranz, R. Osborn
Read original paper ↗ ·Synchrotron studies of functional interfaces and the state of the art: A perspective
Journal of Applied Physics · 2021
Xi Yan, Dillon D. Fong, Hua Zhou, Jessica L. McChesney
Read original paper ↗ ·Application of Machine Learning Methods to Neutron Transmission Spectroscopic Imaging for Solid–Liquid Phase Fraction Analysis
Applied Sciences · 2021
Takashi Kamiyama, Kazuma Hirano, Hirotaka Sato, Kanta Ono, Yuta Suzuki, Daisuke Ito, Yasushi Saitō
Read original paper ↗ ·Machine Learning-Assisted Analysis of Small Angle X-ray Scattering
Venue not supplied by source · 2021
Piotr Tomaszewski, Shun Yu, Markus Borg, Jerk Rönnols
Read original paper ↗ ·Comparing End-to-End Machine Learning Methods for Spectra Classification
Applied Sciences · 2021
Yue Sun, Sándor Brockhauser, Péter Hegedűs
Read original paper ↗ ·Beyond structural insight: a deep neural network for the prediction of Pt L 2/3 -edge X-ray absorption spectra
Physical Chemistry Chemical Physics · 2022
Luke Watson, Conor D. Rankine, Thomas J. Penfold
Read original paper ↗ ·Introduction
Springer theses · 2022
Nina Andrejevic
Read original paper ↗ ·Machine-learning and high-throughput studies for high-entropy materials
Materials Science and Engineering R Reports · 2022
E‐Wen Huang, Wen‐Jay Lee, Sudhanshu Shekhar Singh, Poresh Kumar, Chih‐Yu Lee, Tu‐Ngoc Lam, Hsu-Hsuan Chin, Bi‐Hsuan Lin, Peter Kaehuei Liaw
Read original paper ↗ ·Prediction of the photoelectrochemical performance of hematite electrodes using analytical data
The Analyst · 2022
Yuya Nagai, Kenji Katayama
Read original paper ↗ ·Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?
Biomacromolecules · 2022
Eleonora Gianti, Simona Percec
Read original paper ↗ ·Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network
The Journal of Chemical Physics · 2022
Conor D. Rankine, Thomas J. Penfold
Read original paper ↗ ·Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction
Proceedings of the National Academy of Sciences · 2022
Jordan Venderley, Krishnanand Mallayya, Michael Matty, Matthew Krogstad, Jacob P. C. Ruff, Geoff Pleiss, Varsha Kishore, David G. Mandrus, Daniel Phelan, Lekh Poudel, Andrew Gordon Wilson, Kilian Q. Weinberger, Puspa Upreti, Michael R. Norman, Stephan Rosenkranz, R. Osborn, Eun-Ah Kim
Read original paper ↗ ·Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions
Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms · 2022
Majdi I. Radaideh, Hoang Tran, Lianshan Lin, Hao Jiang, Drew E. Winder, Sarma B Gorti, Guannan Zhang, Justin C. Mach, Sarah M. Cousineau
Read original paper ↗ ·Extraction of interaction parameters for α−RuCl3 from neutron data using machine learning
Physical Review Research · 2022
Anjana Samarakoon, Pontus Laurell, Christian Balz, Arnab Banerjee, Paula Lampen-Kelley, David G. Mandrus, S. E. Nagler, Satoshi Okamoto, D. M. Tennant
Read original paper ↗ ·Z-Contrast Enhancement in Au–Pt Nanocatalysts by Correlative X-ray Absorption Spectroscopy and Electron Microscopy: Implications for Composition Determination
ACS Applied Nano Materials · 2022
Yang Liu, Maichong Xie, Nicholas Marcella, Alexandre C. Foucher, Eric A. Stach, Marc R. Knecht, Anatoly I. Frenkel
Read original paper ↗ ·Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering
Synchrotron Radiation News · 2022
Nathan C. Drucker, Tongtong Liu, Zhantao Chen, Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Thanh Nguyen, Yao Wang, Mingda Li
Read original paper ↗ ·Measurement Informatics in Synchrotron Radiation X-Ray Spectroscopy
Synchrotron Radiation News · 2022
Tetsuro Ueno, Hideaki Iwasawa
Read original paper ↗ ·Parameter inversion of a polydisperse system in small-angle scattering
Journal of Applied Crystallography · 2022
Kuangdai Leng, Stephen M. King, Tim Snow, Sarah E. Rogers, Anders J. Markvardsen, Satheesh Maheswaran, Jeyan Thiyagalingam
Read original paper ↗ ·The Phonon Theory of Liquids and Biological Fluids: Developments and Applications
The Journal of Physical Chemistry Letters · 2022
Dima Bolmatov
Read original paper ↗ ·A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering
Journal of Applied Crystallography · 2022
Tanny Chávez, Eric J. Roberts, Petrus H. Zwart, Alexander Hexemer
Read original paper ↗ ·A perspective on machine learning and data science for strongly correlated electron problems
Carbon Trends · 2022
Steven Johnston, Ehsan Khatami, Richard T. Scalettar
Read original paper ↗ ·Faster and lower-dose X-ray reflectivity measurements enabled by physics-informed modeling and artificial intelligence co-refinement
Journal of Applied Crystallography · 2022
David Mareček, Julian Oberreiter, Andrew R. J. Nelson, Stefan Kowarik
Read original paper ↗ ·Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure
Communications Materials · 2022
Anjana Samarakoon, D. M. Tennant, Feng Ye, Qiang Zhang, S. A. Grigera
Read original paper ↗ ·Artificial Intelligence and Advanced Materials
Advanced Materials · 2022
Cefe López
Read original paper ↗ ·Machine Learning Applications to the One-speed Neutron Transport Problems
Cumhuriyet Science Journal · 2022
R. Gökhan Türeci
Read original paper ↗ ·Convolutional neural network prediction of the photocurrent–voltage curve directly from scanning electron microscopy images
Journal of Materials Chemistry A · 2023
Yuta Hayashi, Yuya Nagai, Zhenhua Pan, Kenji Katayama
Read original paper ↗ ·Model-based evaluation and data requirements for parallel kinetic experimentation and data-driven reaction identification and optimization
Digital Discovery · 2023
Nathan Jiscoot, Evgeny A. Uslamin, Evgeny A. Pidko
Read original paper ↗ ·SIGMA: Spectral Interpretation Using Gaussian Mixtures and Autoencoder
Geochemistry Geophysics Geosystems · 2023
Po‐Yen Tung, Hassan Aftab Sheikh, Matthew Ball, Farhang Nabiei, R. J. Harrison
Read original paper ↗ ·Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data
Digital Discovery · 2023
Andy S. Anker, Keith T. Butler, Manh Duc Le, T. G. Perring, Jeyan Thiyagalingam
Read original paper ↗ ·Direct prediction of inelastic neutron scattering spectra from the crystal structure*
Machine Learning Science and Technology · 2023
Yongqiang Q. Cheng, Geoffrey K. Wu, Daniel M. Pajerowski, M. B. Stone, Andrei T. Savici, Mingda Li, Anibal Javier Ramirez-Cuesta
Read original paper ↗ ·Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis
Chemistry of Materials · 2023
Daniel Enrique Vizoso, Ghatu Subhash, Krishna Rajan, Rémi Dingreville
Read original paper ↗ ·Machine learning for scattering data: strategies, perspectives and applications to surface scattering
Journal of Applied Crystallography · 2023
Alexander Hinderhofer, Alessandro Greco, Vladimir Starostin, Valentin Munteanu, Linus Pithan, Alexander Gerlach, Frank Schreiber
Read original paper ↗ ·The Autonomous Formulation Laboratory: An Open Liquid Handling Platform for Formulation Discovery Using X-ray and Neutron Scattering
Chemistry of Materials · 2023
Peter A. Beaucage, Tyler B. Martin
Read original paper ↗ ·Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification
npj Computational Materials · 2023
Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng, Mouhamad Said Diallo, Haegyeom Kim, Gerbrand Ceder
Read original paper ↗ ·Designing materials acceleration platforms for heterogeneous CO2 photo(thermal)catalysis
Matter · 2023
Andrew Wang, Carlota Bozal‐Ginesta, Sai Govind Hari Kumar, Alán Aspuru‐Guzik, Geoffrey Alan Ozin
Read original paper ↗ ·Molecular-scale synchrotron X-ray investigations of solid-liquid interfaces in lithium-ion batteries
Elsevier eBooks · 2023
Chuntian Cao, Hans‐Georg Steinrück
Read original paper ↗ ·Data-driven forecasting of nonequilibrium solid-state dynamics
Physical review. B./Physical review. B · 2023
Stefan Meinecke, Felix Köster, Dominik Christiansen, Kathy Lüdge, Andreas Knorr, Malte Selig
Read original paper ↗ ·Direct recognition of crystal structures via three-dimensional convolutional neural networks with high accuracy and tolerance to random displacements and missing atoms
Scripta Materialia · 2023
Ziyuan Rao, Yue Li, Hongbin Zhang, Timoteo Colnaghi, Andreas Marek A, Markus Rampp, Baptiste Gault
Read original paper ↗ ·A comprehensive open‐access database of electron backscattering coefficients for energies ranging from 0.1 keV to 15 MeV
Medical Physics · 2023
Fatemeh Akbari
Read original paper ↗ ·Topological Superconductors from a Materials Perspective
Chemistry of Materials · 2023
Manasi Mandal, Nathan C. Drucker, Phum Siriviboon, Thanh Nguyen, Artittaya Boonkird, Tej N. Lamichhane, Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Mingda Li
Read original paper ↗ ·Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon
The Journal of Physical Chemistry C · 2023
Hyuna Kwon, Wenyu Sun, Tim Hsu, Wonseok Jeong, Fikret Aydin, Shubham Sharma, Fanchen Meng, Matthew R. Carbone, Xiao Chen, Deyu Lu, Liwen F. Wan, Michael H. Nielsen, Tuan Anh Pham
Read original paper ↗ ·Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments
Journal of Synchrotron Radiation · 2023
Linus Pithan, Vladimir Starostin, David Mareček, Lukas Petersdorf, Constantin Völter, Valentin Munteanu, Maciej Jankowski, Oleg Konovalov, Alexander Gerlach, Alexander Hinderhofer, Bridget Mary Murphy, Stefan Kowarik, Frank Schreiber
Read original paper ↗ ·Technology of using deep learning algorithms
Bulletin of the National Engineering Academy of the Republic of Kazakhstan · 2023
А.С. Баймаханова, Kamalbek Berkimbayev, Аinur Zhumadillayeva, Elmira Abdrashova
Read original paper ↗ ·Capturing dynamical correlations using implicit neural representations
Nature Communications · 2023
Sathya R. Chitturi, Zhurun Ji, Alexander N. Petsch, Cheng Peng, Zhantao Chen, Rajan Plumley, Mike Dunne, Sougata Mardanya, Sugata Chowdhury, Hongwei Chen, Arun Bansil, Adrian E. Feiguin, Alexander I. Kolesnikov, D. Prabhakaran, S. M. Hayden, Daniel F. Ratner, Chunjing Jia, Youssef S. G. Nashed, Joshua J. Turner
Read original paper ↗ ·High-sensitivity extreme-ultraviolet transient absorption spectroscopy enabled by machine learning
Optics Express · 2023
Tobias Gutberlet, Hung-Tzu Chang, Sergey Zayko, Murat Sivis, Claus Ropers
Read original paper ↗ ·Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra
Journal of the American Chemical Society · 2023
Amir Kotobi, Kanishka Singh, Daniel Höche, Sadia Bari, Robert H. Meißner, Annika Bande
Read original paper ↗ ·Automated analysis of grain morphology in TEM images using convolutional neural network with CHAC algorithm
Journal of Nuclear Materials · 2023
Xinyuan Xu, Zefeng Yu, Wei‐Ying Chen, Aiping Chen, Arthur T. Motta, Xing Wang
Read original paper ↗ ·POMFinder : identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning
Journal of Applied Crystallography · 2023
Andy S. Anker, Emil T. S. Kjær, Mikkel Juelsholt, Kirsten M. Ø. Jensen
Read original paper ↗ ·Formation energy prediction of crystalline compounds using deep convolutional network learning on voxel image representation
Communications Materials · 2023
Ali Davariashtiyani, Sara Kadkhodaei
Read original paper ↗ ·Application of Machine Learning to the Classification of Spectra-like Data
Venue not supplied by source · 2023
Yue Sun
Read original paper ↗ ·