Nature Machine Intelligence最新文献_第2页

Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning 基于细胞间深度学习的不同老化条件下电池寿命预测

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-15 DOI: 10.1038/s42256-024-00972-x

Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian

{"title":"Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning","authors":"Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian","doi":"10.1038/s42256-024-00972-x","DOIUrl":"https://doi.org/10.1038/s42256-024-00972-x","url":null,"abstract":"Accurately predicting battery lifetime in early cycles holds tremendous value in real-world applications. However, this task poses significant challenges due to diverse factors influencing complex battery capacity degradation, such as cycling protocols, ambient temperatures and electrode materials. Moreover, cycling under specific conditions is both resource-intensive and time-consuming. Existing predictive models, primarily developed and validated within a restricted set of ageing conditions, thus raise doubts regarding their extensive applicability. Here we introduce BatLiNet, a deep learning framework tailored to predict battery lifetime reliably across a variety of ageing conditions. The distinctive design is integrating an inter-cell learning mechanism to predict the lifetime differences between two battery cells. This mechanism, when combined with conventional single-cell learning, enhances the stability of lifetime predictions for a target cell under varied ageing conditions. Our experimental results, derived from a broad spectrum of ageing conditions, demonstrate BatLiNet’s superior accuracy and robustness compared to existing models. BatLiNet also exhibits transferring capabilities across different battery chemistries, benefitting scenarios with limited resources. We expect this study could promote exploration of cross-cell insights and facilitate battery research across comprehensive ageing factors.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"24 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual cognition in multimodal large language models 多模态大语言模型中的视觉认知

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-15 DOI: 10.1038/s42256-024-00963-y

Luca M. Schulze Buschoff, Elif Akata, Matthias Bethge, Eric Schulz

{"title":"Visual cognition in multimodal large language models","authors":"Luca M. Schulze Buschoff, Elif Akata, Matthias Bethge, Eric Schulz","doi":"10.1038/s42256-024-00963-y","DOIUrl":"https://doi.org/10.1038/s42256-024-00963-y","url":null,"abstract":"A chief goal of artificial intelligence is to build machines that think like people. Yet it has been argued that deep neural network architectures fail to accomplish this. Researchers have asserted these models’ limitations in the domains of causal reasoning, intuitive physics and intuitive psychology. Yet recent advancements, namely the rise of large language models, particularly those designed for visual processing, have rekindled interest in the potential to emulate human-like cognitive abilities. This paper evaluates the current state of vision-based large language models in the domains of intuitive physics, causal reasoning and intuitive psychology. Through a series of controlled experiments, we investigate the extent to which these modern models grasp complex physical interactions, causal relationships and intuitive understanding of others’ preferences. Our findings reveal that, while some of these models demonstrate a notable proficiency in processing and interpreting visual data, they still fall short of human capabilities in these areas. Our results emphasize the need for integrating more robust mechanisms for understanding causality, physical dynamics and social cognition into modern-day, vision-based language models, and point out the importance of cognitively inspired benchmarks.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"74 2 Pt 2 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The design space of E(3)-equivariant atom-centred interatomic potentials E(3)-等变原子中心原子间势的设计空间

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-15 DOI: 10.1038/s42256-024-00956-x

Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi

{"title":"The design space of E(3)-equivariant atom-centred interatomic potentials","authors":"Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi","doi":"10.1038/s42256-024-00956-x","DOIUrl":"https://doi.org/10.1038/s42256-024-00956-x","url":null,"abstract":"Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"24 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causal chambers as a real-world physical testbed for AI methodology 因果室作为人工智能方法论的真实物理测试平台

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-15 DOI: 10.1038/s42256-024-00964-x

Juan L. Gamella, Jonas Peters, Peter Bühlmann

{"title":"Causal chambers as a real-world physical testbed for AI methodology","authors":"Juan L. Gamella, Jonas Peters, Peter Bühlmann","doi":"10.1038/s42256-024-00964-x","DOIUrl":"https://doi.org/10.1038/s42256-024-00964-x","url":null,"abstract":"In some fields of artificial intelligence, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers must often turn to simulated data, which yields limited information about the applicability of the proposed methods to real problems. As a step forward, we have constructed two devices that allow us to quickly and inexpensively produce large datasets from non-trivial but well-understood physical systems. The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and measure an array of variables from these physical systems, providing a rich testbed for algorithms from a variety of fields. We illustrate potential applications through a series of case studies in fields such as causal discovery, out-of-distribution generalization, change point detection, independent component analysis and symbolic regression. For applications to causal inference, the chambers allow us to carefully perform interventions. We also provide and empirically validate a causal model of each chamber, which can be used as ground truth for different tasks. The hardware and software are made open source, and the datasets are publicly available at causalchamber.org or through the Python package causalchamber.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"43 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modern maxims for an AI oracle AI神谕的现代格言

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-13 DOI: 10.1038/s42256-024-00970-z

M. J. Crockett

引用次数: 0

Exploring scalable medical image encoders beyond text supervision 探索文本监督之外的可扩展医学影像编码器

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-13 DOI: 10.1038/s42256-024-00965-w

Fernando Pérez-García, Harshita Sharma, Sam Bond-Taylor, Kenza Bouzid, Valentina Salvatelli, Maximilian Ilse, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Matthew P. Lungren, Maria Teodora Wetscherek, Noel Codella, Stephanie L. Hyland, Javier Alvarez-Valle, Ozan Oktay

{"title":"Exploring scalable medical image encoders beyond text supervision","authors":"Fernando Pérez-García, Harshita Sharma, Sam Bond-Taylor, Kenza Bouzid, Valentina Salvatelli, Maximilian Ilse, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Matthew P. Lungren, Maria Teodora Wetscherek, Noel Codella, Stephanie L. Hyland, Javier Alvarez-Valle, Ozan Oktay","doi":"10.1038/s42256-024-00965-w","DOIUrl":"https://doi.org/10.1038/s42256-024-00965-w","url":null,"abstract":"Language-supervised pretraining has proven to be a valuable method for extracting semantically meaningful features from images, serving as a foundational element in multimodal systems within the computer vision and medical imaging domains. However, the computed features are limited by the information contained in the text, which is particularly problematic in medical imaging, in which the findings described by radiologists focus on specific observations. This challenge is compounded by the scarcity of paired imaging–text data due to concerns over the leakage of personal health information. In this work, we fundamentally challenge the prevailing reliance on language supervision for learning general-purpose biomedical imaging encoders. We introduce RAD-DINO, a biomedical image encoder pretrained solely on unimodal biomedical imaging data that obtains similar or greater performance than state-of-the-art biomedical-language-supervised models on a diverse range of benchmarks. Specifically, the quality of learned representations is evaluated on standard imaging tasks (classification and semantic segmentation), and a vision–language alignment task (text report generation from images). To further demonstrate the drawback of language supervision, we show that features from RAD-DINO correlate with other medical records (for example, sex or age) better than language-supervised models, which are generally not mentioned in radiology reports. Finally, we conduct a series of ablations determining the factors in RAD-DINO’s performance. In particular, we observe that RAD-DINO’s downstream performance scales well with the quantity and diversity of training data, demonstrating that image-only supervision is a scalable approach for training a foundational biomedical image encoder.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"4 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating machine moral judgement through the Delphi experiment 通过德尔菲实验研究机器的道德判断

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-13 DOI: 10.1038/s42256-024-00969-6

Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny T. Liang, Sydney Levine, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jack Hessel, Jon Borchardt, Taylor Sorensen, Saadia Gabriel, Yulia Tsvetkov, Oren Etzioni, Maarten Sap, Regina Rini, Yejin Choi

{"title":"Investigating machine moral judgement through the Delphi experiment","authors":"Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny T. Liang, Sydney Levine, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jack Hessel, Jon Borchardt, Taylor Sorensen, Saadia Gabriel, Yulia Tsvetkov, Oren Etzioni, Maarten Sap, Regina Rini, Yejin Choi","doi":"10.1038/s42256-024-00969-6","DOIUrl":"https://doi.org/10.1038/s42256-024-00969-6","url":null,"abstract":"As our society adopts increasingly powerful artificial intelligence (AI) systems for pervasive use, there are growing concerns about machine morality—or lack thereof. Millions of users already rely on the outputs of AI systems, such as chatbots, as decision aids. Meanwhile, AI researchers continue to grapple with the challenge of aligning these systems with human morality and values. In response to this challenge, we build and test Delphi, an open-source AI system trained to predict the moral judgements of US participants. The computational framework of Delphi is grounded in the framework proposed by the prominent moral philosopher John Rawls. Our results speak to the promises and limits of teaching machines about human morality. Delphi demonstrates improved generalization capabilities over those exhibited by off-the-shelf neural language models. At the same time, Delphi’s failures also underscore important challenges in this arena. For instance, Delphi has limited cultural awareness and is susceptible to pervasive biases. Despite these shortcomings, we demonstrate several compelling use cases of Delphi, including its incorporation as a component within an ensemble of AI systems. Finally, we computationally demonstrate the potential of Rawls’s prospect of hybrid approaches for reliable moral reasoning, inspiring future research in computational morality.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"106 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards highly sensitive deep learning-based end-to-end database search for tandem mass spectrometry 迈向高度敏感的基于深度学习的端到端串联质谱数据库搜索

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-06 DOI: 10.1038/s42256-024-00960-1

Yonghan Yu, Ming Li

{"title":"Towards highly sensitive deep learning-based end-to-end database search for tandem mass spectrometry","authors":"Yonghan Yu, Ming Li","doi":"10.1038/s42256-024-00960-1","DOIUrl":"https://doi.org/10.1038/s42256-024-00960-1","url":null,"abstract":"Peptide identification in mass spectrometry-based proteomics is crucial for understanding protein function and dynamics. Traditional database search methods, though widely used, rely on heuristic scoring functions, and statistical estimations must be introduced to achieve a higher identification rate. Here we introduce DeepSearch, a deep learning-based end-to-end database search method for tandem mass spectrometry. DeepSearch leverages a modified transformer-based encoder–decoder architecture under the contrastive learning framework. Unlike conventional methods, which rely on ion-to-ion matching, DeepSearch adopts a data-driven approach to score peptide–spectrum matches. DeepSearch can also profile variable post-translational modifications in a zero-shot manner. We show that DeepSearch’s scoring scheme expresses less bias and does not require any statistical estimation. We validate DeepSearch’s accuracy and robustness across various datasets, including those from species with diverse protein compositions and a modification-enriched dataset. DeepSearch sheds new light on database search methods in tandem mass spectrometry.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"38 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reusability report: Deep learning-based analysis of images and spectroscopy data with AtomAI 可重用性报告：使用AtomAI进行基于深度学习的图像和光谱数据分析

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-01-02 DOI: 10.1038/s42256-024-00958-9

Pragalbh Vashishtha, Hitesh Gupta Kattamuri, Nikhil Thawari, Murugaiyan Amirthalingam, Rohit Batra

{"title":"Reusability report: Deep learning-based analysis of images and spectroscopy data with AtomAI","authors":"Pragalbh Vashishtha, Hitesh Gupta Kattamuri, Nikhil Thawari, Murugaiyan Amirthalingam, Rohit Batra","doi":"10.1038/s42256-024-00958-9","DOIUrl":"https://doi.org/10.1038/s42256-024-00958-9","url":null,"abstract":"Machine learning (ML) techniques are gaining traction for materials image processing applications. In this context, Ziatdinov et al. developed AtomAI, a user-friendly and comprehensive Python library designed for a wide range of materials imaging tasks, including image segmentation, denoising, image generation, image-to-spectrum mapping (and vice versa) and subsequent atomistic modelling of image-resolved structures. Given its broad applicability, this report aims to reproduce key aspects of the authors’ original work, extend its capabilities to new materials datasets and enhance certain features to improve model performance. We have not only successfully replicated parts of the original study, but also developed improved ML models for multiple datasets across different image processing tasks. The AtomAI library was found to be easy to use and extensible for custom applications. We believe that AtomAI holds significant potential for the microscopy and spectroscopy communities, and further development—such as semi-automated image segmentation—could broaden its utility and impact.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"72 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ARNLE model identifies prevalence potential of SARS-CoV-2 variants ARNLE模型确定了SARS-CoV-2变体的流行潜力

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-12-31 DOI: 10.1038/s42256-024-00919-2

Yuqi Liu, Jing Li, Peihan Li, Yehong Yang, Kaiying Wang, Jinhui Li, Lang Yang, Jiangfeng Liu, Leili Jia, Aiping Wu, Juntao Yang, Peng Li, Hongbin Song

{"title":"ARNLE model identifies prevalence potential of SARS-CoV-2 variants","authors":"Yuqi Liu, Jing Li, Peihan Li, Yehong Yang, Kaiying Wang, Jinhui Li, Lang Yang, Jiangfeng Liu, Leili Jia, Aiping Wu, Juntao Yang, Peng Li, Hongbin Song","doi":"10.1038/s42256-024-00919-2","DOIUrl":"https://doi.org/10.1038/s42256-024-00919-2","url":null,"abstract":"SARS-CoV-2 mutations accumulated during the COVID-19 pandemic, posing significant challenges for immune prevention. An optimistic perspective suggests that SARS-CoV-2 will become more tropic to humans with weaker virulence and stronger infectivity. However, tracing a quantified trajectory of this process remains difficult. Here we introduce an attentional recurrent network based on language embedding (ARNLE) framework to analyse the shift in SARS-CoV-2 host tropism towards humans. ARNLE incorporates a language model for self-supervised learning to capture the features of amino acid sequences, alongside a supervised bidirectional long-short-term-memory-based network to discern the relationship between mutations and host tropism among coronaviruses. We identified a shift in SARS-CoV-2 tropism from weak to strong, transitioning from an approximate Chiroptera coronavirus to a primate-tropic coronavirus. Delta variants were closer to other common primate coronaviruses than previous SARS-CoV-2 variants. A similar phenomenon was observed among the Omicron variants. We employed a Bayesian-based post hoc explanation method to analyse key mutations influencing the human tropism of SARS-CoV-2. ARNLE identified pivotal mutations in the spike proteins, including T478K, L452R, G142D and so on, as the top determinants of human tropism. Our findings suggest that language models like ARNLE will significantly facilitate the identification of potentially prevalent variants and provide important support for screening key mutations, aiding in timely update of vaccines to protect against future emerging SARS-CoV-2 variants.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"70 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0