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A machine learning tool to efficiently calculate electron–phonon coupling 高效计算电子-声子耦合的机器学习工具。
IF 12
Nature computational science Pub Date : 2024-08-08 DOI: 10.1038/s43588-024-00680-x
{"title":"A machine learning tool to efficiently calculate electron–phonon coupling","authors":"","doi":"10.1038/s43588-024-00680-x","DOIUrl":"10.1038/s43588-024-00680-x","url":null,"abstract":"A machine learning framework that uses atomic orbital-based Hamiltonian matrices and gradients predicted by an equivariant graph neural network is established to calculate electron–phonon coupling (EPC). This approach accelerates the calculations by several orders of magnitude, enabling EPC-related properties to be predicted for complex systems using highly accurate functionals.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 8","pages":"565-566"},"PeriodicalIF":12.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
250 years of oxygen chemistry 150 年的氧气化学
IF 12
Nature computational science Pub Date : 2024-07-30 DOI: 10.1038/s43588-024-00670-z
{"title":"250 years of oxygen chemistry","authors":"","doi":"10.1038/s43588-024-00670-z","DOIUrl":"10.1038/s43588-024-00670-z","url":null,"abstract":"We look back on the discovery of oxygen in light of its upcoming milestone anniversary and highlight the computational contributions to oxygen reduction and evolution in chemistry.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 7","pages":"461-461"},"PeriodicalIF":12.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00670-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Publisher Correction: A perspective on brain-age estimation and its clinical promise 出版商更正：透视脑年龄估算及其临床前景。
IF 12
Nature computational science Pub Date : 2024-07-30 DOI: 10.1038/s43588-024-00681-w
Christian Gaser, Polona Kalc, James H. Cole
{"title":"Publisher Correction: A perspective on brain-age estimation and its clinical promise","authors":"Christian Gaser, Polona Kalc, James H. Cole","doi":"10.1038/s43588-024-00681-w","DOIUrl":"10.1038/s43588-024-00681-w","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"797-797"},"PeriodicalIF":12.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00681-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Computational electrochemistry of oxygen 250 years after Priestley 普利斯特里 150 年后的氧气计算电化学。
IF 12
Nature computational science Pub Date : 2024-07-30 DOI: 10.1038/s43588-024-00664-x
De-en Jiang
{"title":"Computational electrochemistry of oxygen 250 years after Priestley","authors":"De-en Jiang","doi":"10.1038/s43588-024-00664-x","DOIUrl":"10.1038/s43588-024-00664-x","url":null,"abstract":"Since the first isolation of oxygen, chemists have explored oxygen reduction and evolution reactions. Now, computational chemists are trying to understand and predict the best catalysts for them. Here, the importance of various considerations for such calculations, as well as their challenges and opportunities, are discussed.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 7","pages":"462-464"},"PeriodicalIF":12.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A perspective on brain-age estimation and its clinical promise 从脑年龄估计及其临床前景的角度看问题。
IF 12
Nature computational science Pub Date : 2024-07-24 DOI: 10.1038/s43588-024-00659-8
Christian Gaser, Polona Kalc, James H. Cole
{"title":"A perspective on brain-age estimation and its clinical promise","authors":"Christian Gaser, Polona Kalc, James H. Cole","doi":"10.1038/s43588-024-00659-8","DOIUrl":"10.1038/s43588-024-00659-8","url":null,"abstract":"Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings. Brain-age estimation is gaining attention as a biomarker for brain health as it provides a unique perspective on aging. This Perspective reviews current advancements and future directions to ensure deployment in hospital settings.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"744-751"},"PeriodicalIF":12.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multi-task learning for medical foundation models 医学基础模型的多任务学习。
IF 12
Nature computational science Pub Date : 2024-07-19 DOI: 10.1038/s43588-024-00658-9
Jiancheng Yang
{"title":"Multi-task learning for medical foundation models","authors":"Jiancheng Yang","doi":"10.1038/s43588-024-00658-9","DOIUrl":"10.1038/s43588-024-00658-9","url":null,"abstract":"To address the challenge of pretraining foundational models with large datasets, a multi-task approach is proposed, thus helping to overcome the data scarcity problem in biomedical imaging.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 7","pages":"473-474"},"PeriodicalIF":12.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A multi-task learning strategy to pretrain models for medical image analysis 用于医学图像分析模型预训练的多任务学习策略。
IF 12
Nature computational science Pub Date : 2024-07-19 DOI: 10.1038/s43588-024-00666-9
{"title":"A multi-task learning strategy to pretrain models for medical image analysis","authors":"","doi":"10.1038/s43588-024-00666-9","DOIUrl":"10.1038/s43588-024-00666-9","url":null,"abstract":"Pretraining powerful deep learning models requires large, comprehensive training datasets, which are often unavailable for medical imaging. In response, the universal biomedical pretrained (UMedPT) foundational model was developed based on multiple small and medium-sized datasets. This model reduced the amount of data required to learn new target tasks by at least 50%.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 7","pages":"479-480"},"PeriodicalIF":12.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Overcoming data scarcity in biomedical imaging with a foundational multi-task model 利用基础多任务模型克服生物医学成像中的数据匮乏问题。
IF 12
Nature computational science Pub Date : 2024-07-19 DOI: 10.1038/s43588-024-00662-z
Raphael Schäfer, Till Nicke, Henning Höfener, Annkristin Lange, Dorit Merhof, Friedrich Feuerhake, Volkmar Schulz, Johannes Lotz, Fabian Kiessling
{"title":"Overcoming data scarcity in biomedical imaging with a foundational multi-task model","authors":"Raphael Schäfer, Till Nicke, Henning Höfener, Annkristin Lange, Dorit Merhof, Friedrich Feuerhake, Volkmar Schulz, Johannes Lotz, Fabian Kiessling","doi":"10.1038/s43588-024-00662-z","DOIUrl":"10.1038/s43588-024-00662-z","url":null,"abstract":"Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more specialized datasets common in biomedical imaging. Here we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a universal biomedical pretrained model (UMedPT) on a multi-task database including tomographic, microscopic and X-ray images, with various labeling strategies such as classification, segmentation and object detection. The UMedPT foundational model outperformed ImageNet pretraining and previous state-of-the-art models. For classification tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required only 50% of the original training data. In an external independent validation, imaging features extracted using UMedPT proved to set a new standard for cross-center transferability. UMedPT, a foundational model for biomedical imaging, has been trained on a variety of medical tasks with different types of label. It has achieved high performance with less training data in various clinical applications.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 7","pages":"495-509"},"PeriodicalIF":12.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11288886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Free-form metamaterials design with isotropic materials 各向同性材料的自由形态超材料设计
IF 12
Nature computational science Pub Date : 2024-07-18 DOI: 10.1038/s43588-024-00663-y
Juan Manuel Restrepo-Flórez
{"title":"Free-form metamaterials design with isotropic materials","authors":"Juan Manuel Restrepo-Flórez","doi":"10.1038/s43588-024-00663-y","DOIUrl":"10.1038/s43588-024-00663-y","url":null,"abstract":"A recent study proposes a computational method for the design of free-form metamaterials systems. The method simplifies the design process by avoiding the use of anisotropic materials that are usually required for the conventional methods. The method can be applied in designing both two-dimensional and three-dimensional metamaterials that are subject to multiple physical fields.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 7","pages":"477-478"},"PeriodicalIF":12.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Boosting graph neural networks with virtual nodes to predict phonon properties 利用虚拟节点提升图神经网络，预测声子特性。
IF 12
Nature computational science Pub Date : 2024-07-16 DOI: 10.1038/s43588-024-00665-w
{"title":"Boosting graph neural networks with virtual nodes to predict phonon properties","authors":"","doi":"10.1038/s43588-024-00665-w","DOIUrl":"10.1038/s43588-024-00665-w","url":null,"abstract":"A graph neural network using virtual nodes is proposed to predict the properties of complex materials with variable dimensions or dimensions that depend on the input. The method is used to accurately and quickly predict phonon dispersion relations in complex solids and alloys.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 7","pages":"481-482"},"PeriodicalIF":12.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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