Machine Learning Science and Technology最新文献_第7页

Retrieving past quantum features with deep hybrid classical-quantum reservoir computing 利用深度混合经典-量子存储计算检索过去的量子特征

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-18 DOI: 10.1088/2632-2153/ad5f12

Johannes Nokkala, Gian Luca Giorgi and Roberta Zambrini

引用次数: 0

Ultrafast jet classification at the HL-LHC 超高速大型强子对撞机的超快射流分类

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-17 DOI: 10.1088/2632-2153/ad5f10

Patrick Odagiu, Zhiqiang Que, Javier Duarte, Johannes Haller, Gregor Kasieczka, Artur Lobanov, Vladimir Loncar, Wayne Luk, Jennifer Ngadiuba, Maurizio Pierini, Philipp Rincke, Arpita Seksaria, Sioni Summers, Andre Sznajder, Alexander Tapper and Thea K Årrestad

引用次数: 0

Quantum extreme learning of molecular potential energy surfaces and force fields 分子势能面和力场的量子极端学习

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-17 DOI: 10.1088/2632-2153/ad6120

Gabriele Lo Monaco, Marco Bertini, Salvatore Lorenzo and G Massimo Palma

{"title":"Quantum extreme learning of molecular potential energy surfaces and force fields","authors":"Gabriele Lo Monaco, Marco Bertini, Salvatore Lorenzo and G Massimo Palma","doi":"10.1088/2632-2153/ad6120","DOIUrl":"https://doi.org/10.1088/2632-2153/ad6120","url":null,"abstract":"Quantum machine learning algorithms are expected to play a pivotal role in quantum chemistry simulations in the immediate future. One such key application is the training of a quantum neural network to learn the potential energy surface and force field of molecular systems. We address this task by using the quantum extreme learning machine paradigm. This particular supervised learning routine allows for resource-efficient training, consisting of a simple linear regression performed on a classical computer. We have tested a setup that can be used to study molecules of any dimension and is optimized for immediate use on NISQ devices with a limited number of native gates. We have applied this setup to three case studies: lithium hydride, water, and formamide, carrying out both noiseless simulations and actual implementation on IBM quantum hardware. Compared to other supervised learning routines, the proposed setup requires minimal quantum resources, making it feasible for direct implementation on quantum platforms, while still achieving a high level of predictive accuracy compared to simulations. Our encouraging results pave the way towards the future application to more complex molecules, being the proposed setup scalable.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"19 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solving deep-learning density functional theory via variational autoencoders 通过变分自动编码器解决深度学习密度泛函理论问题

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-17 DOI: 10.1088/2632-2153/ad611f

Emanuele Costa, Giuseppe Scriva and Sebastiano Pilati

{"title":"Solving deep-learning density functional theory via variational autoencoders","authors":"Emanuele Costa, Giuseppe Scriva and Sebastiano Pilati","doi":"10.1088/2632-2153/ad611f","DOIUrl":"https://doi.org/10.1088/2632-2153/ad611f","url":null,"abstract":"In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data. However, problematic instabilities have been shown to occur in the search of ground-state density profiles via energy minimization. Indeed, any small noise can lead astray from realistic profiles, causing the failure of the learned functional and, hence, strong violations of the variational property. In this article, we employ variational autoencoders (VAEs) to build a compressed, flexible, and regular representation of the ground-state density profiles of various quantum models. Performing energy minimization in this compressed space allows us to avoid both numerical instabilities and variational biases due to excessive constraints. Our tests are performed on one-dimensional single-particle models from the literature in the field and, notably, on a three-dimensional disordered potential. In all cases, the ground-state energies are estimated with errors below the chemical accuracy and the density profiles are accurately reproduced without numerical artifacts. Furthermore, we show that it is possible to perform transfer learning, applying pre-trained VAEs to different potentials.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"286 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-supervised representations and node embedding graph neural networks for accurate and multi-scale analysis of materials 自监督表征和节点嵌入图神经网络用于材料的精确和多尺度分析

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-17 DOI: 10.1088/2632-2153/ad612b

Jian-Gang Kong, Ke-Lin Zhao, Jian Li, Qing-Xu Li, Yu Liu, Rui Zhang, Jia-Ji Zhu and Kai Chang

{"title":"Self-supervised representations and node embedding graph neural networks for accurate and multi-scale analysis of materials","authors":"Jian-Gang Kong, Ke-Lin Zhao, Jian Li, Qing-Xu Li, Yu Liu, Rui Zhang, Jia-Ji Zhu and Kai Chang","doi":"10.1088/2632-2153/ad612b","DOIUrl":"https://doi.org/10.1088/2632-2153/ad612b","url":null,"abstract":"Supervised machine learning algorithms, such as graph neural networks (GNN), have successfully predicted material properties. However, the superior performance of GNN usually relies on end-to-end learning on large material datasets, which may lose the physical insight of multi-scale information about materials. And the process of labeling data consumes many resources and inevitably introduces errors, which constrains the accuracy of prediction. We propose to train the GNN model by self-supervised learning on the node and edge information of the crystal graph. Compared with the popular manually constructed material descriptors, the self-supervised atomic representation can reach better prediction performance on material properties. Furthermore, it may provide physical insights by tuning the range information. Applying the self-supervised atomic representation on the magnetic moment datasets, we show how they can extract rules and information from the magnetic materials. To incorporate rich physical information into the GNN model, we develop the node embedding graph neural networks (NEGNN) framework and show significant improvements in the prediction performance. The self-supervised material representation and the NEGNN framework may investigate in-depth information from materials and can be applied to small datasets with increased prediction accuracy.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"64 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces 针对金属表面活性氢动力学的机器学习原子间位势基准测试

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-14 DOI: 10.1088/2632-2153/ad5f11

Wojciech G Stark, Cas van der Oord, Ilyes Batatia, Yaolong Zhang, Bin Jiang, Gábor Csányi and Reinhard J Maurer

{"title":"Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces","authors":"Wojciech G Stark, Cas van der Oord, Ilyes Batatia, Yaolong Zhang, Bin Jiang, Gábor Csányi and Reinhard J Maurer","doi":"10.1088/2632-2153/ad5f11","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5f11","url":null,"abstract":"Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the same time, the energy landscapes need to be accurately mapped, as small errors in barriers can lead to large deviations in reaction probabilities. This brings a particularly interesting challenge for machine learning interatomic potentials, which are becoming well-established tools to accelerate molecular dynamics simulations. We compare state-of-the-art machine learning interatomic potentials with a particular focus on their inference performance on CPUs and suitability for high throughput simulation of reactive chemistry at surfaces. The considered models include polarizable atom interaction neural networks (PaiNN), recursively embedded atom neural networks (REANN), the MACE equivariant graph neural network, and atomic cluster expansion potentials (ACE). The models are applied to a dataset on reactive molecular hydrogen scattering on low-index surface facets of copper. All models are assessed for their accuracy, time-to-solution, and ability to simulate reactive sticking probabilities as a function of the rovibrational initial state and kinetic incidence energy of the molecule. REANN and MACE models provide the best balance between accuracy and time-to-solution and can be considered the current state-of-the-art in gas-surface dynamics. PaiNN models require many features for the best accuracy, which causes significant losses in computational efficiency. ACE models provide the fastest time-to-solution, however, models trained on the existing dataset were not able to achieve sufficiently accurate predictions in all cases.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"19 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141720016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep probabilistic direction prediction in 3D with applications to directional dark matter detectors 三维深度概率方向预测，应用于定向暗物质探测器

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-10 DOI: 10.1088/2632-2153/ad5f13

Majd Ghrear, Peter Sadowski and Sven E Vahsen

{"title":"Deep probabilistic direction prediction in 3D with applications to directional dark matter detectors","authors":"Majd Ghrear, Peter Sadowski and Sven E Vahsen","doi":"10.1088/2632-2153/ad5f13","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5f13","url":null,"abstract":"We present the first method to probabilistically predict 3D direction in a deep neural network model. The probabilistic predictions are modeled as a heteroscedastic von Mises-Fisher distribution on the sphere , giving a simple way to quantify aleatoric uncertainty. This approach generalizes the cosine distance loss which is a special case of our loss function when the uncertainty is assumed to be uniform across samples. We develop approximations required to make the likelihood function and gradient calculations stable. The method is applied to the task of predicting the 3D directions of electrons, the most complex signal in a class of experimental particle physics detectors designed to demonstrate the particle nature of dark matter and study solar neutrinos. Using simulated Monte Carlo data, the initial direction of recoiling electrons is inferred from their tortuous trajectories, as captured by the 3D detectors. For keV electrons in a 70% He 30% CO2 gas mixture at STP, the new approach achieves a mean cosine distance of 0.104 (26∘) compared to 0.556 (64∘) achieved by a non-machine learning algorithm. We show that the model is well-calibrated and accuracy can be increased further by removing samples with high predicted uncertainty. This advancement in probabilistic 3D directional learning could increase the sensitivity of directional dark matter detectors.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"24 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141586996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CResU-Net: a method for landslide mapping using deep learning CResU-Net：利用深度学习绘制滑坡图的方法

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-10 DOI: 10.1088/2632-2153/ad5f17

Thang M Pham, Nam Do, Ha T T Pham, Hanh T Bui, Thang T Do and Manh V Hoang

{"title":"CResU-Net: a method for landslide mapping using deep learning","authors":"Thang M Pham, Nam Do, Ha T T Pham, Hanh T Bui, Thang T Do and Manh V Hoang","doi":"10.1088/2632-2153/ad5f17","DOIUrl":"https://doi.org/10.1088/2632-2153/ad5f17","url":null,"abstract":"Landslides, which can occur due to earthquakes and heavy rainfall, pose significant challenges across large areas. To effectively manage these disasters, it is crucial to have fast and reliable automatic detection methods for mapping landslides. In recent years, deep learning methods, particularly convolutional neural and fully convolutional networks, have been successfully applied to various fields, including landslide detection, with remarkable accuracy and high reliability. However, most of these models achieved high detection performance based on high-resolution satellite images. In this research, we introduce a modified Residual U-Net combined with the Convolutional Block Attention Module, a deep learning method, for automatic landslide mapping. The proposed method is trained and assessed using freely available data sets acquired from Sentinel-2 sensors, digital elevation models, and slope data from ALOS PALSAR with a spatial resolution of 10 m. Compared to the original ResU-Net model, the proposed architecture achieved higher accuracy, with the F1-score improving by 9.1% for the landslide class. Additionally, it offers a lower computational cost, with 1.38 giga multiply-accumulate operations per second (GMACS) needed to execute the model compared to 2.68 GMACS in the original model. The source code is available at https://github.com/manhhv87/LandSlideMapping.git.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"235 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141588574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

End-to-end simulation of particle physics events with flow matching and generator oversampling 利用流量匹配和发生器超采样对粒子物理事件进行端到端模拟

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-09 DOI: 10.1088/2632-2153/ad563c

F Vaselli, F Cattafesta, P Asenov, A Rizzi

{"title":"End-to-end simulation of particle physics events with flow matching and generator oversampling","authors":"F Vaselli, F Cattafesta, P Asenov, A Rizzi","doi":"10.1088/2632-2153/ad563c","DOIUrl":"https://doi.org/10.1088/2632-2153/ad563c","url":null,"abstract":"The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomena not previously observed. We show that novel machine learning algorithms, specifically Normalizing Flows and Flow Matching, can be used to replicate accurate simulations from traditional approaches with several orders of magnitude of speed-up. The classical simulation chain starts from a physics process of interest, computes energy deposits of particles and electronics response, and finally employs the same reconstruction algorithms used for data. Eventually, the data are reduced to some high-level analysis format. Instead, we propose an end-to-end approach, simulating the final data format directly from physical generator inputs, skipping any intermediate steps. We use particle jets simulation as a benchmark for comparing both <italic toggle=\"yes\">discrete</italic> and <italic toggle=\"yes\">continuous</italic> Normalizing Flows models. The models are validated across a variety of metrics to identify the most accurate. We discuss the scaling of performance with the increase in training data, as well as the generalization power of these models on physical processes different from the training one. We investigate sampling multiple times from the same physical generator inputs, a procedure we name <italic toggle=\"yes\">oversampling</italic>, and we show that it can effectively reduce the statistical uncertainties of a dataset. This class of ML algorithms is found to be capable of learning the expected detector response independently of the physical input process. The speed and accuracy of the models, coupled with the stability of the training procedure, make them a compelling tool for the needs of current and future experiments.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"38 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty quantification by direct propagation of shallow ensembles 通过直接传播浅层集合量化不确定性

IF 6.8 2区物理与天体物理

Machine Learning Science and Technology Pub Date : 2024-07-04 DOI: 10.1088/2632-2153/ad594a

Matthias Kellner and Michele Ceriotti

{"title":"Uncertainty quantification by direct propagation of shallow ensembles","authors":"Matthias Kellner and Michele Ceriotti","doi":"10.1088/2632-2153/ad594a","DOIUrl":"https://doi.org/10.1088/2632-2153/ad594a","url":null,"abstract":"Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further source of error on top of the intrinsic limitations of the experimental or theoretical setup. Uncertainty estimation is essential to quantify this error, and to make application of data-centric approaches more trustworthy. To ensure that uncertainty quantification is used widely, one should aim for algorithms that are accurate, but also easy to implement and apply. In particular, including uncertainty quantification on top of an existing architecture should be straightforward, and add minimal computational overhead. Furthermore, it should be easy to manipulate or combine multiple machine-learning predictions, propagating uncertainty over further modeling steps. We compare several well-established uncertainty quantification frameworks against these requirements, and propose a practical approach, which we dub direct propagation of shallow ensembles, that provides a good compromise between ease of use and accuracy. We present benchmarks for generic datasets, and an in-depth study of applications to the field of atomistic machine learning for chemistry and materials. These examples underscore the importance of using a formulation that allows propagating errors without making strong assumptions on the correlations between different predictions of the model.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"13 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0