Nature computational science最新文献_第9页

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。

IF 18.3

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

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

{"title":"Evaluating and mitigating bias in AI-based medical text generation","authors":"Xiuying Chen, Tairan Wang, Juexiao Zhou, Zirui Song, Xin Gao, Xiangliang Zhang","doi":"10.1038/s43588-025-00789-7","DOIUrl":"10.1038/s43588-025-00789-7","url":null,"abstract":"Artificial intelligence (AI) systems, particularly those based on deep learning models, have increasingly achieved expert-level performance in medical applications. However, there is growing concern that such AI systems may reflect and amplify human bias, reducing the quality of their performance in historically underserved populations. The fairness issue has attracted considerable research interest in the medical imaging classification field, yet it remains understudied in the text-generation domain. In this study, we investigate the fairness problem in text generation within the medical field and observe substantial performance discrepancies across different races, sexes and age groups, including intersectional groups, various model scales and different evaluation metrics. To mitigate this fairness issue, we propose an algorithm that selectively optimizes those underserved groups to reduce bias. Our evaluations across multiple backbones, datasets and modalities demonstrate that our proposed algorithm enhances fairness in text generation without compromising overall performance. This study evaluates bias in AI-generated medical text, revealing disparities across race, sex and age. An optimization method is proposed to enhance fairness without compromising performance, offering a step toward more equitable AI in healthcare.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 5","pages":"388-396"},"PeriodicalIF":18.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058305","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

Xeric plants inspire adaptable liquid crystal elastomers for water collection 干旱植物激发了适应性强的液晶弹性体用于水收集。

IF 18.3

Nature computational science Pub Date : 2025-04-21 DOI: 10.1038/s43588-025-00801-0

引用次数: 0

Active twisting for adaptive droplet collection 主动扭转自适应液滴收集。

IF 18.3

Nature computational science Pub Date : 2025-04-21 DOI: 10.1038/s43588-025-00786-w

Yifan Yang, Zhijun Dai, Yuzhen Chen, Fan Xu

{"title":"Active twisting for adaptive droplet collection","authors":"Yifan Yang, Zhijun Dai, Yuzhen Chen, Fan Xu","doi":"10.1038/s43588-025-00786-w","DOIUrl":"10.1038/s43588-025-00786-w","url":null,"abstract":"Many xeric plant leaves exhibit bending and twisting morphology, which may contribute to their important biological and physical functions adapted to drought and desert conditions. Revealing the relationships between various morphologies and functionalities can inspire device designs for meeting increasingly stringent environmental requirements. Here, demonstrated on the biomimetic bilayer ribbons made of liquid crystal elastomers, we reveal that the stimulus-induced morphological evolution of bending, spiraling, twisting and various coupling states among them can be selectively achieved and precisely tuned by designing the director orientations in liquid crystal elastomer bilayers. The mathematical models and analytical solutions are developed to quantify the morphology selection and phase transition of these liquid crystal elastomer ribbons for material design, as confirmed by experiments. Moreover, we show that, under activation and control of external stimuli, the twisting configuration can be harnessed to effectively collect and guide the transportation of droplets, and enhance the structural stiffness for resisting wind blow and rainfall to achieve the optimal configuration for water collection. Our results reveal the interesting functions correlated with bending, spiraling and twisting morphologies widely present in the natural world, by providing fundamental insights into their shape transformation and controlling factors. This work also demonstrates a potential application with integrating morphogenesis–environment interactions into devices or equipments. Using theoretical and mechanical models, this study reveals that active twisting of most xeric plant leaves contributes to effective droplet collection and wind resistance in extreme environments. The twisting configuration is used to design a biomimetic plant cluster, enabling tunable water collection and transportation.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 4","pages":"313-321"},"PeriodicalIF":18.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12021652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052816","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

Benchmarking the performance of quantum computing software for quantum circuit creation, manipulation and compilation 对量子电路创建、操作和编译的量子计算软件的性能进行基准测试。

IF 18.3

Nature computational science Pub Date : 2025-04-18 DOI: 10.1038/s43588-025-00792-y

Paul D. Nation, Abdullah Ash Saki, Sebastian Brandhofer, Luciano Bello, Shelly Garion, Matthew Treinish, Ali Javadi-Abhari

{"title":"Benchmarking the performance of quantum computing software for quantum circuit creation, manipulation and compilation","authors":"Paul D. Nation, Abdullah Ash Saki, Sebastian Brandhofer, Luciano Bello, Shelly Garion, Matthew Treinish, Ali Javadi-Abhari","doi":"10.1038/s43588-025-00792-y","DOIUrl":"10.1038/s43588-025-00792-y","url":null,"abstract":"We present Benchpress, a benchmarking suite for evaluating the performance and range of functionality of multiple quantum computing software development kits. This suite consists of a collection of over 1,000 tests measuring key performance metrics for a wide variety of operations on quantum circuits composed of up to 930 qubits and $${mathcal{O}}(1{0}^{6})$$ two-qubit gates, as well as an execution framework for running the tests over multiple quantum software packages in a unified manner. Here we give a detailed overview of the benchmark suite and its methodology and generate representative results over seven different quantum software packages. The flexibility of the Benchpress framework enables benchmarking that not only keeps pace with quantum hardware improvements but also can preemptively gauge the quantum circuit processing costs of future device architectures. In this Resource, the authors present an open-source extensible benchmark tool, Benchpress, for evaluating the performance of mainstream quantum computing software. Benchpress was demonstrated to perform over 1,000 tests with up to 930 qubits to compare the performance of quantum software, providing insight into how to best use current programming stacks.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 5","pages":"427-435"},"PeriodicalIF":18.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058807","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

Generative molecular design and discovery on the rise 生成式分子设计和发现正在兴起。

IF 18.3

Nature computational science Pub Date : 2025-04-17 DOI: 10.1038/s43588-025-00802-z

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 18.3

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":"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. A computational null model is proposed to study the gender and racial diversity-association effects in academia. Researchers’ early training in diverse environments is strongly correlated with nurturing diverse groups in the established period.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 6","pages":"481-491"},"PeriodicalIF":18.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12187764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060512","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

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。

IF 18.3

Nature computational science Pub 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. A machine-learning-potential framework achieves an optimal balance of accuracy and efficiency through monomeric decomposition. Systematic evaluations highlight its potential in large-scale simulations of complex molecular systems.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 5","pages":"418-426"},"PeriodicalIF":18.3,"publicationDate":"2025-04-14","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

Towards efficient and adaptive simulations for continuum physics 面向连续介质物理的高效自适应模拟。

IF 18.3

Nature computational science Pub Date : 2025-04-14 DOI: 10.1038/s43588-025-00791-z

Nicolò Scapin

引用次数: 0

Toward a unified benchmark and framework for deep learning-based prediction of nuclear magnetic resonance chemical shifts 为基于深度学习的核磁共振化学位移预测建立统一的基准和框架。

IF 18.3

Nature computational science Pub Date : 2025-03-28 DOI: 10.1038/s43588-025-00783-z

Fanjie Xu, Wentao Guo, Feng Wang, Lin Yao, Hongshuai Wang, Fujie Tang, Zhifeng Gao, Linfeng Zhang, Weinan E, Zhong-Qun Tian, Jun Cheng

{"title":"Toward a unified benchmark and framework for deep learning-based prediction of nuclear magnetic resonance chemical shifts","authors":"Fanjie Xu, Wentao Guo, Feng Wang, Lin Yao, Hongshuai Wang, Fujie Tang, Zhifeng Gao, Linfeng Zhang, Weinan E, Zhong-Qun Tian, Jun Cheng","doi":"10.1038/s43588-025-00783-z","DOIUrl":"10.1038/s43588-025-00783-z","url":null,"abstract":"The study of structure–spectrum relationships is essential for spectral interpretation, impacting structural elucidation and material design. Predicting spectra from molecular structures is challenging due to their complex relationships. Here we introduce NMRNet, a deep learning framework using the SE(3) Transformer for atomic environment modeling, following a pretraining and fine-tuning paradigm. To support the evaluation of nuclear magnetic resonance chemical shift prediction models, we have established a comprehensive benchmark based on previous research and databases, covering diverse chemical systems. Applying NMRNet to these benchmark datasets, we achieve competitive performance in both liquid-state and solid-state nuclear magnetic resonance datasets, demonstrating its robustness and practical utility in real-world scenarios. Our work helps to advance deep learning applications in analytical and structural chemistry. A deep learning framework (NMRNet) is developed to model atomic environments for predicting NMR chemical shifts. A benchmark dataset, nmrshiftdb2-2024, is also established to provide a standardized source for evaluating NMR models.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 4","pages":"292-300"},"PeriodicalIF":18.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744553","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

Towards transparency and knowledge exchange in AI-assisted data analysis code generation 在人工智能辅助数据分析代码生成中实现透明度和知识交流。

IF 18.3

Nature computational science Pub Date : 2025-03-27 DOI: 10.1038/s43588-025-00781-1

Robert Haase

引用次数: 0