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Machine Learning: Science and Technology, Volume 1
Volume 1, Number 1, February 2020
- Ergun Simsek
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Determining optical constants of 2D materials with neural networks from multi-angle reflectometry data. 01 - O. Anatole von Lilienfeld
Introducing Machine Learning: Science and Technology. 10201 - Oliver T. Unke
High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning. 13001 - Sergei Manzhos
Machine learning for the solution of the Schrödinger equation. 13002 - Oliver M. Gordon
Embedding human heuristics in machine-learning-enabled probe microscopy. 15001 - Francisco Albarrán-Arriagada
Reinforcement learning for semi-autonomous approximate quantum eigensolver. 15002 - Genyue Liu, Mo Chen
Repetitive readout enhanced by machine learning. 15003 - Cas van der Oord
Regularised atomic body-ordered permutation-invariant polynomials for the construction of interatomic potentials. 15004 - Marco Cavaglià
Improving the background of gravitational-wave searches for core collapse supernovae: a machine learning approach. 15005 - Vignesh Gopakumar
Image mapping the temporal evolution of edge characteristics in tokamaks using neural networks. 15006 - Adam J. Barker
Applying machine learning optimization methods to the production of a quantum gas. 15007 - G. Drera
Deep neural network for x-ray photoelectron spectroscopy data analysis. 15008 - Chang Min Hyun
Framelet pooling aided deep learning network: the method to process high dimensional medical data. 15009 - Cristiano Fanelli
DeepRICH: learning deeply Cherenkov detectors. 15010 - Jeffrey M. Ede
Adaptive learning rate clipping stabilizes learning. 15011 - Yasemin Bozkurt Varolgunes
Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders. 15012
Volume 1, Number 2, June 2020
- Hongming Shan
Synergizing medical imaging and radiotherapy with deep learning. 21001 - Oliver M. Gordon
Machine learning at the (sub)atomic scale: next generation scanning probe microscopy. 23001 - Sathya R. Chitturi
Perspective: new insights from loss function landscapes of neural networks. 23002 - Mohammad Rashidi, Jeremiah Croshaw
Deep learning-guided surface characterization for autonomous hydrogen lithography. 25001 - Stefan N. Heinen
Machine learning the computational cost of quantum chemistry. 25002 - Deepak Kamal
A charge density prediction model for hydrocarbons using deep neural networks. 25003 - Guillaume Lambard
SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors. 25004 - Sharon Zhou, Alexandra Luccioni
Establishing an evaluation metric to quantify climate change image realism. 25005 - Kevin Tran
Methods for comparing uncertainty quantifications for material property predictions. 25006 - Davoud Hejazi
Development of use-specific high-performance cyber-nanomaterial optical detectors by effective choice of machine learning algorithms. 25007 - Andrew E. Brereton
Predicting drug properties with parameter-free machine learning: pareto-optimal embedded modeling (POEM). 25008 - Julia Westermayr
Neural networks and kernel ridge regression for excited states dynamics of CH2NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models. 25009 - Yadong Wu
Modified independent component analysis for extracting Eigen-Modes of a quantum system. 25010 - Jesper Løve Hinrich
The probabilistic tensor decomposition toolbox. 25011 - N. Benjamin Erichson
Randomized CP tensor decomposition. 25012 - Gaurav Gupta
DAVI: Deep learning-based tool for alignment and single nucleotide variant identification. 25013 - Alberto Iess
Core-Collapse supernova gravitational-wave search and deep learning classification. 25014 - Ganesh Sivaraman
A machine learning workflow for molecular analysis: application to melting points. 25015 - Filip Morawski
Convolutional neural network classifier for the output of the time-domain $\mathcal{F}$-statistic all-sky search for continuous gravitational waves. 25016
Volume 1, Number 3, August 2020
- Onur Çaylak, O. Anatole von Lilienfeld, Björn Baumeier
Wasserstein metric for improved quantum machine learning with adjacency matrix representations. 03 - Brian DeCost
Scientific AI in materials science: a path to a sustainable and scalable paradigm. 33001 - Lucas Lamata
Quantum machine learning and quantum biomimetics: A perspective. 33002 - Frank Schindler
Algorithms for tensor network contraction ordering. 35001 - Andrew McCluskey
A general approach to maximise information density in neutron reflectometry analysis. 35002 - Javier Alcazar, Vicente Leyton-Ortega, Alejandro Perdomo-Ortiz
Classical versus quantum models in machine learning: insights from a finance application. 35003 - Tom H. E. Oakes
A deep learning functional estimator of optimal dynamics for sampling large deviations. 35004 - Ari Frankel, Kousuke Tachida, Reese E. Jones
Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model. 35005 - Sanjaya Lohani
Coherent optical communications enhanced by machine intelligence. 35006 - Sanjaya Lohani
Machine learning assisted quantum state estimation. 35007 - Tai-Danae Bradley, E. Miles Stoudenmire, John Terilla
Modeling sequences with quantum states: a look under the hood. 35008 - Frank Schäfer
A differentiable programming method for quantum control. 35009 - Tyler C. McCandless
Enhancing wildfire spread modelling by building a gridded fuel moisture content product with machine learning. 35010 - Pascal Pernot
Impact of non-normal error distributions on the benchmarking and ranking of quantum machine learning models. 35011 - A deep neural network to search for new long-lived particles decaying to jets. 35012
- Anton Vladyka
Unsupervised classification of single-molecule data with autoencoders and transfer learning. 35013 - Héctor J. Hortúa, Luigi Malagò, Riccardo Volpi
Constraining the Reionization History using Bayesian Normalizing Flows. 35014 - Victor Venturi
Machine learning enabled discovery of application dependent design principles for two-dimensional materials. 35015
Volume 1, Number 4, November 2020
- M. P. Oxley, Junqi Yin, N. Borodinov, Suhas Somnath, Maxim A. Ziatdinov, Andrew R. Lupini
Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening. 04 - Julia Westermayr
Machine learning and excited-state molecular dynamics. 43001 - Chao Fang
Machine learning in physics: the pitfalls of poisoned training sets. 45001 - Jaehoon Cha, Kyeong Soo Kim
Hierarchical Auxiliary Learning. 45002 - Jeffrey M. Ede
Warwick electron microscopy datasets. 45003 - Hassan Abdallah
Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images. 45004 - Max Hodapp
In operando active learning of interatomic interaction during large-scale simulations. 45005 - Giles Chatham Strong
On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case. 45006 - Thomas Stielow
Fast reconstruction of single-shot wide-angle diffraction images through deep learning. 45007 - Stefano Mangini
Quantum computing model of an artificial neuron with continuously valued input data. 45008 - Isaac C. D. Lenton
Machine learning reveals complex behaviours in optically trapped particles. 45009 - Nicolae C. Iovanac, Brett M. Savoie
Improving the generative performance of chemical autoencoders through transfer learning. 45010 - Jan M. Pawlowski, Julian M. Urban
Reducing autocorrelation times in lattice simulations with generative adversarial networks. 45011 - Amit Mishra
Baryon density extraction and isotropy analysis of cosmic microwave background using deep learning. 45012 - Chang Sun, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
Deep reinforcement learning for optical systems: A case study of mode-locked lasers. 45013 - Marc Stieffenhofer, Michael Wand, Tristan Bereau
Adversarial reverse mapping of equilibrated condensed-phase molecular structures. 45014 - Charles N. Melton
K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy. 45015 - Shusen Liu
Uncovering interpretable relationships in high-dimensional scientific data through function preserving projections. 45016 - Duc Phan Minh Truong
Determination of latent dimensionality in international trade flow. 45017 - Anders S. Christensen
On the role of gradients for machine learning of molecular energies and forces. 45018 - Woo-Seok Lee
Deep learning of chaos classification. 45019 - Derek DeSantis
Coarse-grain cluster analysis of tensors with application to climate biome identification. 45020 - Benjamin A. Helfrecht
Structure-property maps with Kernel principal covariates regression. 45021 - Jack Griffiths
Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks. 45022 - Christina Gao
i- flow: High-dimensional integration and sampling with normalizing flows. 45023 - Mario Krenn
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation. 45024 - Alexandre Alves
Towards recognizing the light facet of the Higgs boson. 45025 - Guido Falk von Rudorff
Thousands of reactants and transition states for competing E2 and S$_\mathrm{N}$2 reactions. 45026 - Hiroki Kawai
Predicting excited states from ground state wavefunction by supervised quantum machine learning. 45027 - Walter Winci, Lorenzo Buffoni
A path towards quantum advantage in training deep generative models with quantum annealers. 45028
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