Nature computational science最新文献_第3页

Large language models act as if they are part of a group 大型语言模型的行为就好像它们是一个群体的一部分。

IF 12

Nature computational science Pub Date : 2025-01-02 DOI: 10.1038/s43588-024-00750-0

Germans Savcisens

引用次数: 0

Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning 基于多任务学习的分子电子结构耦合聚类精度逼近。

IF 12

Nature computational science Pub Date : 2024-12-27 DOI: 10.1038/s43588-024-00747-9

Hao Tang, Brian Xiao, Wenhao He, Pero Subasic, Avetik R. Harutyunyan, Yao Wang, Fang Liu, Haowei Xu, Ju Li

{"title":"Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning","authors":"Hao Tang, Brian Xiao, Wenhao He, Pero Subasic, Avetik R. Harutyunyan, Yao Wang, Fang Liu, Haowei Xu, Ju Li","doi":"10.1038/s43588-024-00747-9","DOIUrl":"10.1038/s43588-024-00747-9","url":null,"abstract":"Machine learning plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules; however, most existing machine learning models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work we developed a unified machine learning method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with several widely used hybrid and double-hybrid functionals in terms of both computational cost and prediction accuracy of various quantum chemical properties. We apply the model to aromatic compounds and semiconducting polymers, evaluating both ground- and excited-state properties. The results demonstrate the model’s accuracy and generalization capability to complex systems that cannot be calculated using CCSD(T)-level methods due to scaling. A multi-task deep learning method for molecular electronic structures, called MEHnet, is developed to predict various molecular properties in a unified framework, approaching chemical accuracy while exhibiting local DFT-level computational costs.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 2","pages":"144-154"},"PeriodicalIF":12.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900898","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 programmable environment for shape optimization and shapeshifting problems 形状优化和变形问题的可编程环境。

IF 12

Nature computational science Pub Date : 2024-12-27 DOI: 10.1038/s43588-024-00749-7

Chaitanya Joshi, Daniel Hellstein, Cole Wennerholm, Eoghan Downey, Emmett Hamilton, Samuel Hocking, Anca S. Andrei, James H. Adler, Timothy J. Atherton

{"title":"A programmable environment for shape optimization and shapeshifting problems","authors":"Chaitanya Joshi, Daniel Hellstein, Cole Wennerholm, Eoghan Downey, Emmett Hamilton, Samuel Hocking, Anca S. Andrei, James H. Adler, Timothy J. Atherton","doi":"10.1038/s43588-024-00749-7","DOIUrl":"10.1038/s43588-024-00749-7","url":null,"abstract":"Soft materials underpin many domains of science and engineering, including soft robotics, structured fluids, and biological and particulate media. In response to applied mechanical, electromagnetic or chemical stimuli, such materials typically change shape, often dramatically. Predicting their structure is of great interest to facilitate design and mechanistic understanding, and can be cast as an optimization problem where a given energy function describing the physics of the material is minimized with respect to the shape of the domain and additional fields. However, shape-optimization problems are very challenging to solve, and there is a lack of suitable simulation tools that are both readily accessible and general in purpose. Here we present an open-source programmable environment, Morpho, and demonstrate its versatility by showcasing a range of applications from different areas of soft-matter physics: swelling hydrogels, complex fluids that form aspherical droplets, soap films and membranes, and filaments. This study introduces an extensible framework—Morpho—for shape optimization, enabling researchers to predict the structure of soft materials, such as complex fluids, gels, particulate and biological materials.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 2","pages":"170-183"},"PeriodicalIF":12.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900892","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

Leveraging pharmacovigilance data to predict population-scale toxicity profiles of checkpoint inhibitor immunotherapy. 利用药物警戒数据来预测检查点抑制剂免疫疗法的人群规模毒性概况。

IF 12

Nature computational science Pub Date : 2024-12-23 DOI: 10.1038/s43588-024-00748-8

Dongxue Yan, Siqi Bao, Zicheng Zhang, Jie Sun, Meng Zhou

引用次数: 0

Deep Bayesian active learning using in-memory computing hardware 使用内存计算硬件的深度贝叶斯主动学习。

IF 12

Nature computational science Pub Date : 2024-12-23 DOI: 10.1038/s43588-024-00744-y

Yudeng Lin, Bin Gao, Jianshi Tang, Qingtian Zhang, He Qian, Huaqiang Wu

{"title":"Deep Bayesian active learning using in-memory computing hardware","authors":"Yudeng Lin, Bin Gao, Jianshi Tang, Qingtian Zhang, He Qian, Huaqiang Wu","doi":"10.1038/s43588-024-00744-y","DOIUrl":"10.1038/s43588-024-00744-y","url":null,"abstract":"Labeling data is a time-consuming, labor-intensive and costly procedure for many artificial intelligence tasks. Deep Bayesian active learning (DBAL) boosts labeling efficiency exponentially, substantially reducing costs. However, DBAL demands high-bandwidth data transfer and probabilistic computing, posing great challenges for conventional deterministic hardware. Here we propose a memristor stochastic gradient Langevin dynamics in situ learning method that uses the stochastic of memristor modulation to learn efficiency, enabling DBAL within the computation-in-memory (CIM) framework. To prove the feasibility and effectiveness of the proposed method, we implemented in-memory DBAL on a memristor-based stochastic CIM system and successfully demonstrated a robot’s skill learning task. The inherent stochastic characteristics of memristors allow a four-layer memristor Bayesian deep neural network to efficiently identify and learn from uncertain samples. Compared with cutting-edge conventional complementary metal-oxide-semiconductor-based hardware implementation, the stochastic CIM system achieves a remarkable 44% boost in speed and could conserve 153 times more energy. This study introduces an in-memory deep Bayesian active learning framework that uses the stochastic properties of memristors for in situ probabilistic computations. This framework can greatly improve the efficiency and speed of artificial intelligence learning tasks, as demonstrated with a robot skill-learning task.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"27-36"},"PeriodicalIF":12.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883834","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

Mapping the gene space at single-cell resolution with gene signal pattern analysis 利用基因信号模式分析绘制单细胞分辨率的基因空间图谱

IF 12

Nature computational science Pub Date : 2024-12-20 DOI: 10.1038/s43588-024-00734-0

Aarthi Venkat, Sam Leone, Scott E. Youlten, Eric Fagerberg, John Attanasio, Nikhil S. Joshi, Michael Perlmutter, Smita Krishnaswamy

{"title":"Mapping the gene space at single-cell resolution with gene signal pattern analysis","authors":"Aarthi Venkat, Sam Leone, Scott E. Youlten, Eric Fagerberg, John Attanasio, Nikhil S. Joshi, Michael Perlmutter, Smita Krishnaswamy","doi":"10.1038/s43588-024-00734-0","DOIUrl":"10.1038/s43588-024-00734-0","url":null,"abstract":"In single-cell sequencing analysis, several computational methods have been developed to map the cellular state space, but little has been done to map or create embeddings of the gene space. Here we formulate the gene embedding problem, design tasks with simulated single-cell data to evaluate representations, and establish ten relevant baselines. We then present a graph signal processing approach, called gene signal pattern analysis (GSPA), that learns rich gene representations from single-cell data using a dictionary of diffusion wavelets on the cell–cell graph. GSPA enables characterization of genes based on their patterning and localization on the cellular manifold. We motivate and demonstrate the efficacy of GSPA as a framework for diverse biological tasks, such as capturing gene co-expression modules, condition-specific enrichment and perturbation-specific gene–gene interactions. Then we showcase the broad utility of gene representations derived from GSPA, including for cell–cell communication (GSPA-LR), spatial transcriptomics (GSPA-multimodal) and patient response (GSPA-Pt) analysis. This work presents a graph signal processing method, gene signal pattern analysis, to embed gene signals from single-cell sequencing data. In diverse experimental set-ups and case studies, GSPA establishes a gene-based framework for single-cell analysis.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"955-977"},"PeriodicalIF":12.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862464","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 spatiotemporal style transfer algorithm for dynamic visual stimulus generation 一种动态视觉刺激生成的时空风格转移算法。

IF 12

Nature computational science Pub Date : 2024-12-20 DOI: 10.1038/s43588-024-00746-w

Antonino Greco, Markus Siegel

{"title":"A spatiotemporal style transfer algorithm for dynamic visual stimulus generation","authors":"Antonino Greco, Markus Siegel","doi":"10.1038/s43588-024-00746-w","DOIUrl":"10.1038/s43588-024-00746-w","url":null,"abstract":"Understanding how visual information is encoded in biological and artificial systems often requires the generation of appropriate stimuli to test specific hypotheses, but available methods for video generation are scarce. Here we introduce the spatiotemporal style transfer (STST) algorithm, a dynamic visual stimulus generation framework that allows the manipulation and synthesis of video stimuli for vision research. We show how stimuli can be generated that match the low-level spatiotemporal features of their natural counterparts, but lack their high-level semantic features, providing a useful tool to study object recognition. We used these stimuli to probe PredNet, a predictive coding deep network, and found that its next-frame predictions were not disrupted by the omission of high-level information, with human observers also confirming the preservation of low-level features and lack of high-level information in the generated stimuli. We also introduce a procedure for the independent spatiotemporal factorization of dynamic stimuli. Testing such factorized stimuli on humans and deep vision models suggests a spatial bias in how humans and deep vision models encode dynamic visual information. These results showcase potential applications of the STST algorithm as a versatile tool for dynamic stimulus generation in vision science. The spatiotemporal style transfer (STST) algorithm enables video generation by selectively manipulating the spatial and temporal features of natural videos, fostering vision science research in both biological and artificial systems.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 2","pages":"155-169"},"PeriodicalIF":12.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00746-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873616","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

Cover runners-up of 2024 2024 年封面亚军

IF 12

Nature computational science Pub Date : 2024-12-20 DOI: 10.1038/s43588-024-00758-6

引用次数: 0

Simulation and assimilation of the digital human brain 数字人脑的模拟和同化

IF 12

Nature computational science Pub Date : 2024-12-19 DOI: 10.1038/s43588-024-00731-3

Wenlian Lu, Xin Du, Jiexiang Wang, Longbin Zeng, Leijun Ye, Shitong Xiang, Qibao Zheng, Jie Zhang, Ningsheng Xu, Jianfeng Feng, the DTB Consortium

{"title":"Simulation and assimilation of the digital human brain","authors":"Wenlian Lu, Xin Du, Jiexiang Wang, Longbin Zeng, Leijun Ye, Shitong Xiang, Qibao Zheng, Jie Zhang, Ningsheng Xu, Jianfeng Feng, the DTB Consortium","doi":"10.1038/s43588-024-00731-3","DOIUrl":"10.1038/s43588-024-00731-3","url":null,"abstract":"Here we present the Digital Brain (DB)—a platform for simulating spiking neuronal networks at the large neuron scale of the human brain on the basis of personalized magnetic resonance imaging data and biological constraints. The DB aims to reproduce both the resting state and certain aspects of the action of the human brain. An architecture with up to 86 billion neurons and 14,012 GPUs—including a two-level routing scheme between GPUs to accelerate spike transmission in up to 47.8 trillion neuronal synapses—was implemented as part of the simulations. We show that the DB can reproduce blood-oxygen-level-dependent signals of the resting state of the human brain with a high correlation coefficient, as well as interact with its perceptual input, as demonstrated in a visual task. These results indicate the feasibility of implementing a digital representation of the human brain, which can open the door to a broad range of potential applications. The Digital Brain platform is capable of simulating spiking neuronal networks at the neuronal scale of the human brain. The platform is used to reproduce blood-oxygen-level-dependent signals in both the resting state and action, thereby predicting the visual evaluation scores.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"890-898"},"PeriodicalIF":12.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862421","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

On the path toward brain-scale simulations 迈向大脑尺度模拟之路

IF 12

Nature computational science Pub Date : 2024-12-19 DOI: 10.1038/s43588-024-00743-z

Felix Wang, James B. Aimone

引用次数: 0