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ORCIDMachine Learning: Science and Technology, Volume 2
Volume 2, Number 1, March 2021
- Pascal Friederich
, Salvador León, José Darío Perea, Loïc M. Roch, Alán Aspuru-Guzik:
The influence of sorbitol doping on aggregation and electronic properties of PEDOT: PSS: a theoretical study. 01
- Tobias Haug, Wai-Keong Mok, Jia-Bin You, Wenzu Zhang, Ching Eng Png, Leong-Chuan Kwek:
Classifying global state preparation via deep reinforcement learning. 01 - Salman Ahmadi-Asl, Andrzej Cichocki, Anh Huy Phan, Maame G. Asante-Mensah, Mirfarid Musavian Ghazani, Toshihisa Tanaka, Ivan V. Oseledets:
Randomized algorithms for fast computation of low rank tensor ring model. 11001 - Elena Cuoco, Jade Powell, Marco Cavaglià, Kendall Ackley, Michal Bejger, Chayan Chatterjee, Michael Coughlin, Scott Coughlin, Paul Easter, Reed Essick, Hunter Gabbard, Timothy Gebhard, Shaon Ghosh, Leïla Haegel, Alberto Iess, David Keitel, Zsuzsa Márka, Szabolcs Márka, Filip Morawski, Tri Nguyen, Rich Ormiston, Michael Pürrer, Massimiliano Razzano, Kai Staats, Gabriele Vajente, Daniel Williams:
Enhancing gravitational-wave science with machine learning. 11002 - Wen Guan, Gabriel N. Perdue, Arthur Pesah, Maria Schuld, Koji Terashi, Sofia Vallecorsa, Jean-Roch Vlimant:
Quantum machine learning in high energy physics. 11003 - Jeffrey M. Ede:
Deep learning in electron microscopy. 11004 - Stuart I. Campbell, Daniel B. Allan, Andi M. Barbour, Daniel Olds, Maksim S. Rakitin, Reid Smith, Stuart B. Wilkins:
Outlook for artificial intelligence and machine learning at the NSLS-II. 13001 - Jennifer Ngadiuba, Vladimir Loncar, Maurizio Pierini, Sioni Summers, Giuseppe Di Guglielmo, Javier M. Duarte, Philip C. Harris, Dylan S. Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Sheila Sagear, Zhenbin Wu, Duc Hoang:
Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml. 15001 - João Caldeira, Brian Nord:
Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms. 15002 - Harbir Antil, Ratna Khatri, Rainald Löhner, Deepanshu Verma:
Fractional deep neural network via constrained optimization. 15003 - Reed Essick, Patrick Godwin, Chad Hanna, Lindy Blackburn, Erik Katsavounidis:
iDQ: Statistical inference of non-gaussian noise with auxiliary degrees of freedom in gravitational-wave detectors. 15004 - Arnab Bhadra, Kalidas Yeturu:
Site2Vec: a reference frame invariant algorithm for vector embedding of protein-ligand binding sites. 15005 - Dmitri Ivanov, Oleg E. Kalashev, Mikhail Yu Kuznetsov, Grigory I. Rubtsov, Takashi Sako, Yoshiki Tsunesada, Yana V. Zhezher:
Using deep learning to enhance event geometry reconstruction for the telescope array surface detector. 15006 - Xiao Liang, Dan Nguyen, Steve B. Jiang:
Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion. 15007 - Lucien Hardy, Adam G. M. Lewis:
Quantum computation with machine-learning-controlled quantum stuff. 15008 - Cory B. Scott, Eric Mjolsness:
Graph prolongation convolutional networks: explicitly multiscale machine learning on graphs with applications to modeling of cytoskeleton. 15009 - Sven Krippendorf, Marc Syvaeri:
Detecting symmetries with neural networks. 15010 - Lee J. O'Riordan, Myles Doyle, Fabio Baruffa, Venkatesh Kannan:
A hybrid classical-quantum workflow for natural language processing. 15011 - Marinus J. Lagerwerf, Allard A. Hendriksen, Jan-Willem Buurlage, Kees Joost Batenburg:
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D computed tomography. 15012 - Gal Gilad, Itay Sason, Roded Sharan:
An automated approach for determining the number of components in non-negative matrix factorization with application to mutational signature learning. 15013 - Zhe Zhang, Minghao Song, Xiaobiao Huang:
Online accelerator optimization with a machine learning-based stochastic algorithm. 15014 - Steff Farley, Jo E. A. Hodgkinson, Oliver M. Gordon, Joanna Turner, Andrea Soltoggio, Philip J. Moriarty, Eugénie Hunsicker:
Improving the segmentation of scanning probe microscope images using convolutional neural networks. 15015 - Philippe Schwaller, Alain C. Vaucher, Teodoro Laino, Jean-Louis Reymond:
Prediction of chemical reaction yields using deep learning. 15016 - Chao Wu, Dan Nguyen, Yixun Xing, Ana M. Barragan-Montero, Jan Schuemann, Haijiao Shang, Yuehu Pu, Steve B. Jiang:
Improving proton dose calculation accuracy by using deep learning. 15017 - Behnam Parsaeifard, Deb Sankar De, Anders S. Christensen, Felix A. Faber, Emir Kocer, Sandip De, Jörg Behler, O. Anatole von Lilienfeld, Stefan Goedecker:
An assessment of the structural resolution of various fingerprints commonly used in machine learning. 15018 - Esben Jannik Bjerrum, Amol Thakkar, Ola Engkvist:
Artificial applicability labels for improving policies in retrosynthesis prediction. 17001 - Pascal Pernot, Bing Huang, Andreas Savin:
Corrigendum: Impact of non-normal error distributions on the benchmarking and ranking of quantum machine learning models (2020 Mach. Learn.: Sci. Technol. 1 035011). 19501
Volume 2, Number 2, June 2021
- Tianchen Zhao, Giuseppe Carleo, James Stokes, Shravan K. Veerapaneni:
Natural evolution strategies and variational Monte Carlo. 02 - Kyle Sprague, Juan Carrasquilla, Stephen Whitelam, Isaac Tamblyn:
Watch and learn - a generalized approach for transferrable learning in deep neural networks via physical principles. 02 - Harold Erbin, Riccardo Finotello:
Inception neural network for complete intersection Calabi-Yau 3-folds. 02 - Jonathan Shlomi, Peter W. Battaglia, Jean-Roch Vlimant:
Graph neural networks in particle physics. 21001 - Mathieu Doucet, Anjana M. Samarakoon, Changwoo Do, William T. Heller, Richard Archibald, D. Alan Tennant, Thomas Proffen, Garrett E. Granroth:
Machine learning for neutron scattering at ORNL. 23001 - Nicholas Walker, Ka-Ming Tam:
InfoCGAN classification of 2D square Ising configurations. 25001 - Ivan S. Novikov, Konstantin Gubaev, Evgeny V. Podryabinkin, Alexander V. Shapeev:
The MLIP package: moment tensor potentials with MPI and active learning. 25002 - Magali Benoit, Jonathan Amodeo, Ségolène Combettes, Ibrahim Khaled, Aurélien Roux, Julien Lam:
Measuring transferability issues in machine-learning force fields: the example of gold-iron interactions with linearized potentials. 25003 - A. Rakotondrajoa, Martin Radtke:
Machine learning based quantification of synchrotron radiation-induced x-ray fluorescence measurements - a case study. 25004 - Ryan Sweke, Markus S. Kesselring, Evert P. L. van Nieuwenburg, Jens Eisert:
Reinforcement learning decoders for fault-tolerant quantum computation. 25005 - Andreu Glasmann, Alexandros Kyrtsos, Enrico Bellotti:
Machine learning for analyzing and characterizing InAsSb-based nBn photodetectors. 25006 - Muhammed Shuaibi, Saurabh Sivakumar, Rui Qi Chen, Zachary W. Ulissi:
Enabling robust offline active learning for machine learning potentials using simple physics-based priors. 25007 - Oleksandr Balabanov, Mats Granath:
Unsupervised interpretable learning of topological indices invariant under permutations of atomic bands. 25008 - Dmitrii Beloborodov, Alexander E. Ulanov, Jakob N. Foerster, Shimon Whiteson, A. I. Lvovsky:
Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization. 25009 - Sandeep Madireddy, Ji Hwan Park, Sunwoo Lee, Prasanna Balaprakash, Shinjae Yoo, Wei-keng Liao, Cory D. Hauck, M. Paul Laiu, Richard Archibald:
In situ compression artifact removal in scientific data using deep transfer learning and experience replay. 25010 - Jin-Guo Liu, Liang Mao, Pan Zhang, Lei Wang:
Solving quantum statistical mechanics with variational autoregressive networks and quantum circuits. 25011 - Haozhu Wang, Zeyu Zheng, Chengang Ji, L. Jay Guo:
Automated multi-layer optical design via deep reinforcement learning. 25013 - Alexandr Ignatenko, Dameli Assalauova, Sergey A. Bobkov, Luca Gelisio, Anton B. Teslyuk, Viacheslav A. Ilyin, Ivan A. Vartanyants:
Classification of diffraction patterns in single particle imaging experiments performed at x-ray free-electron lasers using a convolutional neural network. 25014 - Pake Melland, Jason Albright, Nathan M. Urban:
Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme. 25015 - Sharon Zhou, Jiequan Zhang, Hang Jiang, Torbjörn Lundh, Andrew Y. Ng:
Data augmentation with Mobius transformations. 25016 - Alice E. A. Allen, Geneviève Dusson, Christoph Ortner, Gábor Csányi:
Atomic permutationally invariant polynomials for fitting molecular force fields. 25017 - Francisco L. Giambelluca, Marcelo A. Cappelletti, Jorge Rafael Osio, Luis A. Giambelluca:
Novel automatic scorpion-detection and -recognition system based on machine-learning techniques. 25018 - Pranath Reddy, Aranya Bhuti Bhattacherjee:
A hybrid quantum regression model for the prediction of molecular atomization energies. 25019 - Jonas Paccolat, Stefano Spigler, Matthieu Wyart:
How isotropic kernels perform on simple invariants. 25020 - Pavel V. Kolesnichenko, Qianhui Zhang, Changxi Zheng, Michael S. Fuhrer, Jeffrey A. Davis:
Multidimensional analysis of excitonic spectra of monolayers of tungsten disulphide: toward computer-aided identification of structural and environmental perturbations of 2D materials. 25021 - Rohan Thavarajah, Xiang Zhai, Zheren Ma, David Castineira:
Fast modeling and understanding fluid dynamics systems with encoder-decoder networks. 25022 - Rocío Mercado, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, Esben Jannik Bjerrum:
Graph networks for molecular design. 25023 - Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, Nikita Janakarajan, Antonio Cardinale, Teodoro Laino, María Rodríguez Martínez:
Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2. 25024 - Phillip M. Maffettone, Joshua K. Lynch, Thomas A. Caswell, Clara E. Cook, Stuart I. Campbell, Daniel Olds:
Gaming the beamlines - employing reinforcement learning to maximize scientific outcomes at large-scale user facilities. 25025 - Alejandro Pozas-Kerstjens, Gorka Muñoz-Gil, Eloy Piñol, Miguel Ángel García-March, Antonio Acín, Maciej Lewenstein, Przemyslaw R. Grzybowski:
Efficient training of energy-based models via spin-glass control. 25026 - Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alán Aspuru-Guzik:
Scientific intuition inspired by machine learning-generated hypotheses. 25027 - Alexander Goscinski, Guillaume Fraux, Giulio Imbalzano, Michele Ceriotti:
The role of feature space in atomistic learning. 25028 - Suraj Pawar, Romit Maulik:
Distributed deep reinforcement learning for simulation control. 25029 - Heesoo Park, Adnan Ali, Raghvendra Mall, Halima Bensmail, Stefano Sanvito, Fedwa El-Mellouhi:
Data-driven enhancement of cubic phase stability in mixed-cation perovskites. 25030 - Singanallur V. Venkatakrishnan, Amirkoushyar Ziabari, Jacob D. Hinkle, Andrew W. Needham, Jeffrey M. Warren, Hassina Z. Bilheux:
Convolutional neural network based non-iterative reconstruction for accelerating neutron tomography. 25031 - Wenxiang Cong, Yan Xi, Bruno De Man, Ge Wang:
Monochromatic image reconstruction via machine learning. 25032 - Ti Bai, Biling Wang, Dan Nguyen, Steve B. Jiang:
Deep dose plugin: towards real-time Monte Carlo dose calculation through a deep learning-based denoising algorithm. 25033 - Juan Manuel Carmona Loaiza, Zamaan Raza:
Towards reflectivity profile inversion through artificial neural networks. 25034 - Sergei Gukov, James Halverson, Fabian Ruehle, Piotr Sulkowski:
Learning to unknot. 25035 - Howard Yanxon, David Zagaceta, Binh Tang, David S. Matteson, Qiang Zhu:
PyXtal_FF: a python library for automated force field generation. 27001
Volume 2, Number 3, September 2021
- Lisanne Knijff, Chao Zhang:
Machine learning inference of molecular dipole moment in liquid water. 03 - Cynthia Shen, Mario Krenn, Sagi Eppel, Alán Aspuru-Guzik:
Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations. 03 - Mathieu Doucet, Richard K. Archibald, William T. Heller:
Machine learning for neutron reflectometry data analysis of two-layer thin films. 35001 - James Halverson, Anindita Maiti, Keegan Stoner:
Neural networks and quantum field theory. 35002 - Chunpeng Wang, Feng Yu, Yiyang Liu, Xiaoyun Li, Jige Chen, Jeyan Thiyagalingam, Alessandro Sepe:
Deploying the Big Data Science Center at the Shanghai Synchrotron Radiation Facility: the first superfacility platform in China. 35003 - Frank Schäfer, Pavel Sekatski, Martin Koppenhöfer, Christoph Bruder, Michal Kloc:
Control of stochastic quantum dynamics by differentiable programming. 35004 - Jeffrey D. Krupa, Kelvin Lin, Maria Acosta Flechas, Jack Dinsmore, Javier M. Duarte, Philip C. Harris, Scott Hauck, Burt Holzman, Shih-Chieh Hsu, Thomas Klijnsma, Mia Liu, Kevin Pedro, Dylan S. Rankin, Natchanon Suaysom, Matt Trahms, Nhan Tran:
GPU coprocessors as a service for deep learning inference in high energy physics. 35005 - Daniil Mironov, James H. Durant, Rebecca Mackenzie, Joshaniel F. K. Cooper:
Towards automated analysis for neutron reflectivity. 35006 - Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe:
Improving epidemic testing and containment strategies using machine learning. 35007 - Maame G. Asante-Mensah, Salman Ahmadi-Asl, Andrzej Cichocki:
Matrix and tensor completion using tensor ring decomposition with sparse representation. 35008 - Viktor Zaverkin, Johannes Kästner:
Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design. 35009 - Amit Gupta, Sabyasachi Chakraborty, Raghunathan Ramakrishnan:
Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules. 35010 - Koji Hashimoto, Hong-Ye Hu, Yi-Zhuang You:
Neural ordinary differential equation and holographic quantum chromodynamics. 35011 - Vitaly Vanchurin:
Toward a theory of machine learning. 35012 - Azar Sadeghnejad-Barkousaraie, Gyanendra Bohara, Steve B. Jiang, Dan Nguyen:
A reinforcement learning application of a guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy. 35013 - Onur Danaci, Sanjaya Lohani, Brian T. Kirby, Ryan T. Glasser:
Machine learning pipeline for quantum state estimation with incomplete measurements. 35014 - Hao Tian, Xi Jiang, Peng Tao:
PASSer: prediction of allosteric sites server. 35015 - Zhiyue Ding, Lorin S. Matthews, Truell W. Hyde:
A machine learning based Bayesian optimization solution to non-linear responses in dusty plasmas. 35017 - Kentaro Mogushi, Ryan Quitzow-James, Marco Cavaglià, Sumeet Kulkarni, Fergus Hayes:
NNETFIX: an artificial neural network-based denoising engine for gravitational-wave signals. 35018 - Friederike Metz, Juan Polo, Natalya Weber, Thomas Busch:
Deep-learning-based quantum vortex detection in atomic Bose-Einstein condensates. 35019 - Shangjie Guo, Amilson R. Fritsch, Craig Greenberg, Ian B. Spielman, Justyna P. Zwolak:
Machine-learning enhanced dark soliton detection in Bose-Einstein condensates. 35020 - Annika Stuke, Patrick Rinke, Milica Todorovic:
Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization. 35022 - Matthew Praeger, Yunhui Xie, James A. Grant-Jacob, Robert W. Eason, Ben Mills:
Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments. 35024 - Vishrut Jetly, Bhaskar Chaudhury:
Extracting electron scattering cross sections from swarm data using deep neural networks. 35025 - Martin P. Bircher, Andreas Singraber, Christoph Dellago:
Improved description of atomic environments using low-cost polynomial functions with compact support. 35026 - Carlos Bravo-Prieto:
Quantum autoencoders with enhanced data encoding. 35028 - Lingxiao Wang, Tian Xu, Till Hannes Stoecker, Horst Stöcker, Yin Jiang, Kai Zhou:
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk. 35031 - Tommy Liu, Amanda S. Barnard:
Fast derivation of Shapley based feature importances through feature extraction methods for nanoinformatics. 35034 - Prakriti Kayastha, Raghunathan Ramakrishnan:
Machine learning modeling of materials with a group-subgroup structure. 35035 - Justin A. Reyes, E. Miles Stoudenmire:
Multi-scale tensor network architecture for machine learning. 35036 - Martín Leandro Paleico, Jörg Behler:
A bin and hash method for analyzing reference data and descriptors in machine learning potentials. 37001 - Nicolae C. Iovanac, Brett M. Savoie:
Erratum: Improving the generative performance of chemical autoencoders through transfer learning (2020 Mach. Learn.: Sci. Technol. 1 045010). 39601 - Stephen R. Green, Jonathan Gair:
Complete parameter inference for GW150914 using deep learning. 3 - April M. Miksch, Tobias Morawietz, Johannes Kästner, Alexander Urban, Nongnuch Artrith:
Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations. 31001 - Merjem Hoxha, Hiqmet Kamberaj:
Automation of some macromolecular properties using a machine learning approach. 35016 - Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Melodie Christensen, Elena Liles, Jason E. Hein, Alán Aspuru-Guzik:
Olympus: a benchmarking framework for noisy optimization and experiment planning. 35021 - Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton:
Data-driven discovery of Koopman eigenfunctions for control. 35023 - Vinicius Mikuni, Florencia Canelli:
Point cloud transformers applied to collider physics. 35027 - Francesca Mignacco, Pierfrancesco Urbani, Lenka Zdeborová:
Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem. 35029 - Lara Hoffmann, Ines Fortmeier, Clemens Elster:
Uncertainty quantification by ensemble learning for computational optical form measurements. 35030 - Raffaele Marino:
Learning from survey propagation: a neural network for MAX-E-3-SAT. 35032 - Viktor Ahlberg Gagner, Maja Jensen, Gergely Katona:
Estimating the probability of coincidental similarity between atomic displacement parameters with machine learning. 35033 - Niklas Käming, Anna Dawid, Korbinian Kottmann, Maciej Lewenstein, Klaus Sengstock, Alexandre Dauphin, Christof Weitenberg:
Unsupervised machine learning of topological phase transitions from experimental data. 35037 - Rose K. Cersonsky, Benjamin A. Helfrecht, Edgar A. Engel, Sergei Kliavinek, Michele Ceriotti:
Improving sample and feature selection with principal covariates regression. 35038