Nature computational science最新文献_第2页

Probabilistic photonic computing for AI. 人工智能的概率光子计算。

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Nature computational science Pub Date : 2025-05-01 Epub Date: 2025-05-23 DOI: 10.1038/s43588-025-00800-1

Frank Brückerhoff-Plückelmann, Anna P Ovvyan, Akhil Varri, Hendrik Borras, Bernhard Klein, Lennart Meyer, C David Wright, Harish Bhaskaran, Ghazi Sarwat Syed, Abu Sebastian, Holger Fröning, Wolfram Pernice

{"title":"Probabilistic photonic computing for AI.","authors":"Frank Brückerhoff-Plückelmann, Anna P Ovvyan, Akhil Varri, Hendrik Borras, Bernhard Klein, Lennart Meyer, C David Wright, Harish Bhaskaran, Ghazi Sarwat Syed, Abu Sebastian, Holger Fröning, Wolfram Pernice","doi":"10.1038/s43588-025-00800-1","DOIUrl":"10.1038/s43588-025-00800-1","url":null,"abstract":"Probabilistic computing excels in approximating combinatorial problems and modeling uncertainty. However, using conventional deterministic hardware for probabilistic models is challenging: (pseudo) random number generation introduces computational overhead and additional data shuffling. Therefore, there is a pressing need for different probabilistic computing architectures that achieve low latencies with reasonable energy consumption. Physical computing offers a promising solution, as these systems do not rely on an abstract deterministic representation of data but directly encode the information in physical quantities, enabling inherent probabilistic architectures utilizing entropy sources. Photonic computing is a prominent variant of physical computing due to the large available bandwidth, several orthogonal degrees of freedom for data encoding and optimal properties for in-memory computing and parallel data transfer. Here, we highlight key developments in physical photonic computing and photonic random number generation. We further provide insights into the realization of probabilistic photonic processors and their impact on artificial intelligence systems and future challenges.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":"377-387"},"PeriodicalIF":12.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144133377","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 quest toward comprehensive benchmarking of quantum computing software. 对量子计算软件的全面基准测试的探索。

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Nature computational science Pub Date : 2025-05-01 DOI: 10.1038/s43588-025-00803-y

Chenghong Zhu, Lei Zhang, Xin Wang

引用次数: 0

Making the most of presubmission enquiries. 充分利用提交前查询。

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Nature computational science Pub Date : 2025-05-01 DOI: 10.1038/s43588-025-00817-6

引用次数: 0

DNA data storage for biomedical images using HELIX. 利用HELIX存储生物医学图像的DNA数据。

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Nature computational science Pub Date : 2025-05-01 Epub Date: 2025-05-13 DOI: 10.1038/s43588-025-00793-x

Guanjin Qu, Zihui Yan, Xin Chen, Huaming Wu

引用次数: 0

Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials. 用化学驱动的方法扩展原子分解和多体表示到机器学习潜能。

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Nature computational science Pub Date : 2025-05-01 Epub Date: 2025-04-14 DOI: 10.1038/s43588-025-00790-0

Qi Yu, Ruitao Ma, Chen Qu, Riccardo Conte, Apurba Nandi, Priyanka Pandey, Paul L Houston, Dong H Zhang, Joel M Bowman

{"title":"Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials.","authors":"Qi Yu, Ruitao Ma, Chen Qu, Riccardo Conte, Apurba Nandi, Priyanka Pandey, Paul L Houston, Dong H Zhang, Joel M Bowman","doi":"10.1038/s43588-025-00790-0","DOIUrl":"10.1038/s43588-025-00790-0","url":null,"abstract":"Most widely used machine learning potentials for condensed-phase applications rely on many-body permutationally invariant polynomial or atom-centered neural networks. However, these approaches face challenges in achieving chemical interpretability in atomistic energy decomposition and fully matching the computational efficiency of traditional force fields. Here we present a method that combines aspects of both approaches and balances accuracy and force-field-level speed. This method utilizes a monomer-centered representation, where the potential energy is decomposed into the sum of chemically meaningful monomeric energies. The structural descriptors of monomers are described by one-body and two-body effective interactions, enforced by appropriate sets of permutationally invariant polynomials as inputs to the feed-forward neural networks. Systematic assessments of models for gas-phase water trimer, liquid water, methane-water cluster and liquid carbon dioxide are performed. The improved accuracy, efficiency and flexibility of this method have promise for constructing accurate machine learning potentials and enabling large-scale quantum and classical simulations for complex molecular systems.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":"418-426"},"PeriodicalIF":12.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065335","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

Evaluating and mitigating bias in AI-based medical text generation. 评估和减轻基于人工智能的医学文本生成中的偏见。

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Nature computational science Pub Date : 2025-05-01 Epub Date: 2025-04-23 DOI: 10.1038/s43588-025-00789-7

Xiuying Chen, Tairan Wang, Juexiao Zhou, Zirui Song, Xin Gao, Xiangliang Zhang

引用次数: 0

Toward fair AI-driven medical text generation. 朝着公平的人工智能驱动的医学文本生成。

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Nature computational science Pub Date : 2025-05-01 DOI: 10.1038/s43588-025-00807-8

Yumeng Zhang, Jiangning Song

引用次数: 0

Gender and racial diversity socialization in science. 科学中的性别和种族多样性社会化。

IF 12

Nature computational science Pub Date : 2025-04-17 DOI: 10.1038/s43588-025-00795-9

Weihua Li, Hongwei Zheng, Jennie E Brand, Aaron Clauset

{"title":"Gender and racial diversity socialization in science.","authors":"Weihua Li, Hongwei Zheng, Jennie E Brand, Aaron Clauset","doi":"10.1038/s43588-025-00795-9","DOIUrl":"https://doi.org/10.1038/s43588-025-00795-9","url":null,"abstract":"Scientific collaboration networks are a form of unequally distributed social capital that shapes both researcher job placement and long-term research productivity and prominence. However, the role of collaboration networks in shaping the gender and racial diversity of the scientific workforce remains unclear. Here we propose a computational null model to investigate the degree to which early-career scientific collaborators with representationally diverse cohorts of scholars are associated with forming or participating in more diverse research groups as established researchers. When testing this hypothesis using two large-scale, longitudinal datasets on scientific collaborations, we find that the gender and racial diversity in a researcher's early-career collaboration environment is strongly associated with the diversity of their collaborators in their established period. This diversity-association effect is particularly prominent for men. Coupled with gender and racial homophily between advisors and advisees, collaborator diversity represents a generational effect that partly explains why changes in representation within the scientific workforce tend to happen very slowly.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060512","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

Integrating statistical physics and machine learning for combinatorial optimization. 结合统计物理和机器学习进行组合优化。

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Nature computational science Pub Date : 2025-04-01 DOI: 10.1038/s43588-025-00794-w

引用次数: 0

Multimodal learning for mapping genotype-phenotype dynamics. 绘制基因型-表型动力学的多模态学习。

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Nature computational science Pub Date : 2025-04-01 Epub Date: 2025-01-28 DOI: 10.1038/s43588-024-00765-7

Farhan Khodaee, Rohola Zandie, Elazer R Edelman

{"title":"Multimodal learning for mapping genotype-phenotype dynamics.","authors":"Farhan Khodaee, Rohola Zandie, Elazer R Edelman","doi":"10.1038/s43588-024-00765-7","DOIUrl":"10.1038/s43588-024-00765-7","url":null,"abstract":"How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Integrating high-content genotyping approaches such as single-cell RNA sequencing and advanced learning methods such as language models offers an opportunity for dissecting this complex relationship. Here we present a computational integrated genetics framework designed to analyze and interpret the high-dimensional landscape of genotypes and their associated phenotypes simultaneously. We applied this approach to develop a multimodal foundation model to explore the genotype-phenotype relationship manifold for human transcriptomics at the cellular level. Analyzing this joint manifold showed a refined resolution of cellular heterogeneity, uncovered potential cross-tissue biomarkers and provided contextualized embeddings to investigate the polyfunctionality of genes shown for the von Willebrand factor (VWF) gene in endothelial cells. Overall, this study advances our understanding of the dynamic interplay between gene expression and phenotypic manifestation and demonstrates the potential of integrated genetics in uncovering new dimensions of cellular function and complexity.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":"333-344"},"PeriodicalIF":12.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143061684","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