Nature computational science最新文献_第9页

Periodicity-aware deep learning for polymers 聚合物周期感知深度学习。

IF 18.3

Nature computational science Pub Date : 2025-11-20 DOI: 10.1038/s43588-025-00903-9

Yuhui Wu, Cong Wang, Xintian Shen, Tianyi Zhang, Peng Zhang, Jian Ji

{"title":"Periodicity-aware deep learning for polymers","authors":"Yuhui Wu, Cong Wang, Xintian Shen, Tianyi Zhang, Peng Zhang, Jian Ji","doi":"10.1038/s43588-025-00903-9","DOIUrl":"10.1038/s43588-025-00903-9","url":null,"abstract":"Deep learning has revolutionized chemical research by accelerating the discovery and understanding of complex chemical systems. However, polymer chemistry lacks a unified deep learning framework owing to the complexity of polymer structures. Existing self-supervised learning methods simplify polymers into repeating units and neglect their inherent periodicity, thereby limiting the models’ ability to generalize across tasks. To address this, we propose a periodicity-aware deep learning framework for polymers, PerioGT. In pre-training, a chemical knowledge-driven periodicity prior is constructed and incorporated into the model through contrastive learning. Then, periodicity prompts are learned in fine-tuning based on the prior. Additionally, a graph augmentation strategy is employed, which integrates additional conditions via virtual nodes to model complex chemical interactions. PerioGT achieves state-of-the-art performance on 16 downstream tasks. Wet-lab experiments highlight PerioGT’s potential in the real world, identifying two polymers with potent antimicrobial properties. Our results demonstrate that introducing the periodicity prior effectively enhances model performance. PerioGT is a self-supervised learning framework for polymer property prediction, integrating periodicity priors and additional conditions to enhance generalization under data scarcity and enable broad applicability.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 12","pages":"1214-1226"},"PeriodicalIF":18.3,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145566635","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

Thermal physics meets quantum computing 热物理与量子计算相结合

IF 18.3

Nature computational science Pub Date : 2025-11-20 DOI: 10.1038/s43588-025-00927-1

Jie Pan

引用次数: 0

Aligning brains into a shared space improves their alignment with large language models 将大脑整合到一个共享空间中可以提高它们与大型语言模型的一致性。

IF 18.3

Nature computational science Pub Date : 2025-11-18 DOI: 10.1038/s43588-025-00900-y

Arnab Bhattacharjee, Zaid Zada, Haocheng Wang, Bobbi Aubrey, Werner Doyle, Patricia Dugan, Daniel Friedman, Orrin Devinsky, Adeen Flinker, Peter J. Ramadge, Uri Hasson, Ariel Goldstein, Samuel A. Nastase

{"title":"Aligning brains into a shared space improves their alignment with large language models","authors":"Arnab Bhattacharjee, Zaid Zada, Haocheng Wang, Bobbi Aubrey, Werner Doyle, Patricia Dugan, Daniel Friedman, Orrin Devinsky, Adeen Flinker, Peter J. Ramadge, Uri Hasson, Ariel Goldstein, Samuel A. Nastase","doi":"10.1038/s43588-025-00900-y","DOIUrl":"10.1038/s43588-025-00900-y","url":null,"abstract":"Recent research demonstrates that large language models can predict neural activity recorded via electrocorticography during natural language processing. To predict word-by-word neural activity, most prior work evaluates encoding models within individual electrodes and participants, limiting generalizability. Here we analyze electrocorticography data from eight participants listening to the same 30-min podcast. Using a shared response model, we estimate a common information space across participants. This shared space substantially enhances large language model-based encoding performance and enables denoising of individual brain responses by projecting back into participant-specific electrode spaces—yielding a 37% average improvement in encoding accuracy (from r = 0.188 to r = 0.257). The greatest gains occur in brain areas specialized for language comprehension, particularly the superior temporal gyrus and inferior frontal gyrus. Our findings highlight that estimating a shared space allows us to construct encoding models that better generalize across individuals. Aligning electrocorticography data into a shared space improves how large language models predict brain activity during language comprehension, enhancing encoding accuracy, cross-participant generalization and denoising—especially in language-selective regions.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 2","pages":"169-178"},"PeriodicalIF":18.3,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145552206","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

Adaptive validation strategies for real-world clinical artificial intelligence 临床人工智能的自适应验证策略。

IF 18.3

Nature computational science Pub Date : 2025-11-17 DOI: 10.1038/s43588-025-00901-x

Fiona R. Kolbinger, Jakob Nikolas Kather

引用次数: 0

Efficient algorithms for the surface density of states in topological photonic and acoustic systems 拓扑光子和声学系统中态表面密度的有效算法。

IF 18.3

Nature computational science Pub Date : 2025-11-14 DOI: 10.1038/s43588-025-00898-3

Yi-Xin Sha, Ming-Yao Xia, Ling Lu, Yi Yang

{"title":"Efficient algorithms for the surface density of states in topological photonic and acoustic systems","authors":"Yi-Xin Sha, Ming-Yao Xia, Ling Lu, Yi Yang","doi":"10.1038/s43588-025-00898-3","DOIUrl":"10.1038/s43588-025-00898-3","url":null,"abstract":"Topological photonics and acoustics have attracted wide research interest for their ability to manipulate light and sound at surfaces. The supercell technique is the conventional standard approach used to calculate these boundary effects, but, as the supercell grows in size, this method requires increasingly large computational resources. Additionally, it falls short in differentiating the surface states at opposite boundaries and, due to finite-size effects, from bulk states. Here, to overcome these limitations, we provide two complementary efficient methods for obtaining the ideal topological surface states of semi-infinite systems of diverse surface configurations. The first is the cyclic reduction method, which is based on iteratively inverting the Hamiltonian for a single unit cell, and the other is the transfer matrix method, which relies on eigenanalysis of a transfer matrix for a pair of unit cells. Numerical benchmarks, including gyromagnetic photonic crystals, valley photonic crystals, spin-Hall acoustic crystals and quadrupole photonic crystals, jointly show that both methods can effectively sort out the boundary modes via the surface density of states, at reduced computational cost and increased speed. Our computational schemes enable direct comparisons with near-field scanning measurements, thereby expediting the exploration of topological artificial materials and the design of topological devices. This study reports two efficient methods—cyclic reduction and transfer matrix—to compute topological surface states in photonic and acoustic systems, cutting memory and time use by up to 100-fold and enabling the faster design of advanced topological devices.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 12","pages":"1192-1201"},"PeriodicalIF":18.3,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145524776","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

SynGFN: learning across chemical space with generative flow-based molecular discovery SynGFN：基于生成流的分子发现的跨化学空间学习。

IF 18.3

Nature computational science Pub Date : 2025-11-13 DOI: 10.1038/s43588-025-00902-w

Yuchen Zhu, Shuwang Li, Jihong Chen, Donghai Zhao, Xiaorui Wang, Yitong Li, Yifei Liu, Yue Kong, Beichen Zhang, Chang Liu, Tingjun Hou, Chang-Yu Hsieh

{"title":"SynGFN: learning across chemical space with generative flow-based molecular discovery","authors":"Yuchen Zhu, Shuwang Li, Jihong Chen, Donghai Zhao, Xiaorui Wang, Yitong Li, Yifei Liu, Yue Kong, Beichen Zhang, Chang Liu, Tingjun Hou, Chang-Yu Hsieh","doi":"10.1038/s43588-025-00902-w","DOIUrl":"10.1038/s43588-025-00902-w","url":null,"abstract":"In recent years, artificial intelligence has advanced the design–make–test–analyze cycle, transforming molecular discovery. Despite these advances, the compartmentalized approach to computer-aided molecular design and synthesis remains a critical bottleneck, limiting further optimization of the design–make–test–analyze cycle. Here, to this end, we introduce SynGFN, which models molecular design as a cascade of simulated chemical reactions, enabling the assembly of molecules from synthesizable building blocks. SynGFN features two key ingredients: (1) a hierarchically pretrained policy network that accelerates learning across diverse distributions of desirable molecules in chemical spaces, and (2) a multifidelity acquisition framework to alleviate the cost of reward evaluations. These technical developments collectively endow SynGFN with the capability to explore a chemical space up to an order of magnitude larger (measured in terms of #Circles) than that of other synthesis-aware generative models, while identifying the most diverse, synthesizable and high-performance molecules. We demonstrate SynGFN’s potential impacts by designing inhibitors for GluN1/GluN3A, a therapeutic target for neuropsychiatric disorders. A persistent gap from theoretical molecules to experimentally viable compounds has hindered the practical adoption of generative algorithms. This study proposes SynGFN as a bridge linking molecular design and synthesis, accelerating exploration and producing diverse, synthesizable, high-performance molecules.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 1","pages":"29-38"},"PeriodicalIF":18.3,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145514834","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

Larger language models better align with the reading brain 更大的语言模型更适合阅读大脑。

IF 18.3

Nature computational science Pub Date : 2025-11-12 DOI: 10.1038/s43588-025-00905-7

Samuel A. Nastase

引用次数: 0

Learning to decode logical circuits 学习解码逻辑电路。

IF 18.3

Nature computational science Pub Date : 2025-11-04 DOI: 10.1038/s43588-025-00897-4

Yiqing Zhou, Chao Wan, Yichen Xu, Jin Peng Zhou, Kilian Q. Weinberger, Eun-Ah Kim

{"title":"Learning to decode logical circuits","authors":"Yiqing Zhou, Chao Wan, Yichen Xu, Jin Peng Zhou, Kilian Q. Weinberger, Eun-Ah Kim","doi":"10.1038/s43588-025-00897-4","DOIUrl":"10.1038/s43588-025-00897-4","url":null,"abstract":"As quantum hardware advances toward enabling error-corrected quantum circuits in the near future, the absence of an efficient polynomial-time decoding algorithm for logical circuits presents a critical bottleneck. While quantum memory decoding has been well studied, inevitable correlated errors introduced by transversal entangling logical gates prevent the straightforward generalization of quantum memory decoders. Here we introduce a data-centric, modular decoder framework, the Multi-Core Circuit Decoder (MCCD), which consists of decoder modules corresponding to each logical operation supported by the quantum hardware. The MCCD handles both single-qubit and entangling gates within a unified framework. We train MCCD using mirror-symmetric random Clifford circuits, demonstrating its ability to effectively learn correlated decoding patterns. Through extensive testing on circuits substantially deeper than those used in training, we show that MCCD maintains high logical accuracy while exhibiting competitive polynomial decoding time across increasing circuit depths and code distances. When compared with conventional decoders such as minimum weight perfect matching (MWPM), most likely error (MLE) and belief propagation with ordered statistics post-processing (BP-OSD), MCCD achieves competitive accuracy with substantially better time efficiency, particularly for circuits with entangling gates. Our approach provides a noise-model-agnostic solution to the decoding challenge in deep logical quantum circuits. This study reports a machine learning decoder that efficiently corrects errors in quantum logical circuits with entangling gates. The Multi-Core Circuit Decoder achieves competitive accuracy while running much faster than conventional methods.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 12","pages":"1158-1167"},"PeriodicalIF":18.3,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s43588-025-00897-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145446737","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

Data-driven law firm rankings to reduce information asymmetry in legal disputes 数据驱动的律师事务所排名，减少法律纠纷中的信息不对称。

IF 18.3

Nature computational science Pub Date : 2025-10-31 DOI: 10.1038/s43588-025-00899-2

Alexandre Mojon, Robert Mahari, Sandro Claudio Lera

引用次数: 0

A computational science perspective on the legal system 法律体系的计算科学视角。

IF 18.3

Nature computational science Pub Date : 2025-10-31 DOI: 10.1038/s43588-025-00827-4

Aurelia Tamò-Larrieux, Clement Guitton, Simon Mayer

引用次数: 0