編譯｜未玖
Nature, 13 November 2025, Volume 647, Issue 8089
《自然》，2025年11月13日，第647卷，8089期
物理學Physics
Millisecond lifetimes and coherence times in 2D transmon qubits
二維透射子量子比特的毫秒壽命和相干時間
▲ 作者：Matthew P. Bland, Faranak Bahrami, Jeronimo G. C. Martinez, Paal H. Prestegaard, Basil M. Smitham, Atharv Joshi, et al.
▲鏈接：
https://www.nature.com/articles/s41586-025-09687-4
▲摘要：材料改進是減少超導量子比特損耗和退相干的有效途徑，因為這種改進可較易轉化為大規模處理器。最近的工作通過使用鉭作為基底層和藍寶石作為基底來改善透射子相干性。這些器件的損耗主要由兩級系統控制，表面電介質和體電介質的貢獻相當，這表明要實現技術水平的實質性改進，必須同時解決這兩個問題。
研究組展示了用高電阻率硅代替基底顯著降低了體基底損耗，在45個量子比特上實現了時間平均品質因子（Qavg）高達9.7×106的2D透射子。對于最佳量子比特，研究組實現了1.5×107的Qavg，最大Q值達到2.5×107，對應壽命（T1）高達1.68 ms。這種低損耗也使研究組能夠觀察到與約瑟夫森結相關的退相干效應，并使用改進的低污染結沉積來實現超過T1的哈恩回波相干時間（T2E）。
研究組在不修改量子比特架構的情況下實現了這些材料的改進，從而實現輕松集成標準量子控制門。他們展示了具有99.994%保真度的單量子比特門。硅上鉭平臺包括一個可在晶圓規模上制造的簡單材料堆疊結構，因此可便捷轉化為大規模量子處理器。
▲ Abstract： Materials improvement is a powerful approach to reducing loss and decoherence in superconducting qubits, because such improvements can be readily translated to large-scale processors. Recent work improved transmon coherence by using tantalum as a base layer and sapphire as a substrate. The losses in these devices are dominated by two-level systems with comparable contributions from both the surface and bulk dielectrics, indicating that both must be tackled to achieve substantial improvements in the state of the art. Here we show that replacing the substrate with high-resistivity silicon markedly decreases the bulk substrate loss, enabling 2D transmons with time-averaged quality factors (Qavg) of 9.7×106 across 45qubits. For our best qubit, we achieve a Qavg of 1.5×107, reaching a maximum Q of 2.5×107, corresponding to a lifetime (T1) up to 1.68 ms. This low loss also allows us to observe decoherence effects related to the Josephson junction, and we use an improved, low-contamination junction deposition to achieve Hahn echo coherence times (T2E) exceeding T1. We achieve these materials improvements without any modifications to the qubit architecture, allowing us to readily incorporate standard quantum control gates. We demonstrate single-qubit gates with 99.994% fidelity. The tantalum-on-silicon platform comprises a simple material stack that can potentially be fabricated at the wafer scale and therefore can be readily translated to large-scale quantum processors.
人工智能Artificial Intelligence
Aligning machine and human visual representations across abstraction levels
跨抽象層級對齊機器和人類的視覺表征
▲ 作者：Lukas Muttenthaler, Klaus Greff, Frieda Born, Bernhard Spitzer, Simon Kornblith, Michael C. Mozer, et al.
▲ 鏈接： https://www.nature.com/articles/s41586-025-09631-6
▲摘要：深度神經網絡已在廣泛應用中取得了成功，包括作為人類行為模型和視覺任務中的神經表征。然而，神經網絡訓練和人類學習存在根本差異，神經網絡通常不能像人類那樣穩健泛化，這就引發了人們對其底層表征相似性的質疑。人們需要確定，現代學習系統要表現出更符合人類的行為，還欠缺什么？
研究組強調了視覺模型和人類之間的一個關鍵差異：盡管人類概念知識是從精細到粗粒度來層級化組織的，但模型表征并不能準確捕獲所有這些抽象層級。為了解決這種偏差，研究組首先訓練一個教師模型來模仿人類的判斷，再通過微調，將其表征中與人類對齊的結構遷移至最先進的預訓練視覺基礎模型，從而優化其表征。
這些人類對齊模型在廣泛的相似性任務中更準確地逼近人類行為和不確定性，包括跨越多層級語義抽象的人類判斷數據集。它們在各種機器學習任務上也表現得更好，提高了泛化和分布外魯棒性。
因此，向神經網絡注入額外人類知識會產生一個兩全其美的表征，既更符合人類的認知判斷，又更實用，這為更強大、可解釋和與人類一致的人工智能系統鋪平了道路。
▲ Abstract： Deep neural networks have achieved success across a wide range of applications, including as models of human behaviour and neural representations in vision tasks. However, neural network training and human learning differ in fundamental ways, and neural networks often fail to generalize as robustly as humans do, raising questions regarding the similarity of their underlying representations. We need to determine what is missing for modern learning systems to exhibit more human-aligned behaviour. Here we highlight a key misalignment between vision models and humans: whereas human conceptual knowledge is hierarchically organized from fine- to coarse-scale distinctions, model representations do not accurately capture all these levels of abstraction. To address this misalignment, we first train a teacher model to imitate human judgements, then transfer human-aligned structure from its representations to refine the representations of pretrained state-of-the-art vision foundation models via fine-tuning. These human-aligned models more accurately approximate human behaviour and uncertainty across a wide range of similarity tasks, including a dataset of human judgements spanning multiple levels of semantic abstractions. They also perform better on a diverse set of machine learning tasks, increasing generalization and out-of-distribution robustness. Thus, infusing neural networks with additional human knowledge yields a best-of-both-worlds representation that is both more consistent with human cognitive judgements and more practically useful, paving the way towards more robust, interpretable and human-aligned artificial intelligence systems.
材料科學Materials Science
Atomically resolved edges and defects in lead halide perovskites
鹵化鉛鈣鈦礦的原子分辨邊緣和缺陷分析
▲ 作者：Biao Yuan, Zeyu Wang, Shuchen Zhang, Christoph Hofer, Chuang Gao, Tamazouzt Chennit, et al.
▲鏈接：
https://www.nature.com/articles/s41586-025-09693-6
▲摘要：雖然邊緣和缺陷只構成晶體材料的一小部分，但它們對材料的性能產生巨大的影響。有機—無機鹵化物鈣鈦礦是頗具前景的下一代半導體材料，具有優越的成本效益和有趣的光電性能，但因其極端敏感性，獲得邊緣的清晰圖像仍頗具挑戰。
研究組采用真正高速超低劑量四維掃描透射電子顯微鏡技術，結合劑量分割法，實現了迄今所知最低劑量的原子分辨率疊層成像，不僅揭示了鹵化物鈣鈦礦邊緣的精細原子結構，還揭示了邊緣結構動力學。
研究組在甲基銨鉛碘（MAPbI3）中觀察到大多數甲基銨（MA）和碘（I）在邊緣的末端結構，并發現其邊緣和內部缺陷的損壞率取決于存在空位的濃度和類型，特別是碘空位的優勢與更高的損壞率相關。
▲ Abstract： Although edges and defects constitute only a small fraction of crystalline materials, they exert an outsized impact on a material′s properties. Organic–inorganic halide perovskites are promising next-generation semiconductor materials with superior cost effectiveness and interesting optoelectronic properties. However, clear images of their edges have remained challenging to obtain owing to their extreme sensitivity. Using truly high-speed ultralow-dose four-dimensional scanning transmission electron microscopy with dose fractionation, we perform ptychography at, to our knowledge, the lowest-dose atomic resolution to date, revealing not only the detailed atomic structure of the edges of a halide perovskite but also their structural dynamics. A majority methylammonium (MA) and iodine (I) edge termination is observed in methylammonium lead iodide (MAPbI3), and the damage rate of its edges and internal defects is found to depend on the concentration and type of vacancies present, with a preponderance of I vacancies in particular correlating with higher rates of damage.
Silicon solar cells with hybrid back contacts
雜化背接觸結構硅太陽能電池
▲ 作者：Genshun Wang, Mingzhe Yu, Hua Wu, Yunpeng Li, Lei Xie, Junzhe Wei, et al.
▲鏈接：
https://www.nature.com/articles/s41586-025-09681-w
▲摘要：硅太陽能電池對可持續能源至關重要，但仍受到效率損失的限制，特別是在填充因子方面。
研究組開發了一種雜化背接觸結構太陽能電池，其結合了先進的全表面鈍化和激光加工隧穿接觸。該策略實現了27.81%的功率轉換效率，接近理論極限的95%。通過整合高低溫工藝，研究組抑制了復合，提高了接觸性能，實現了87.55%（接近理論極限98%）的填充因子。
該模型將理想因子與載流子損失機制聯系起來，闡明了在體相和表面的載流子復合，以及由復合引起的關鍵填充因子損失。這些創新為可擴展、高效的硅光伏電池提供了實驗和理論上的進展。
▲ Abstract： Silicon solar cells are essential for sustainable energy but remain limited by efficiency losses, particularly in the fill factor. Here we develop a hybrid interdigitated back-contact solar cell that combines advanced all-surface passivation with laser-treated tunnelling contacts. This approach achieves a power conversion efficiency of 27.81%, approaching 95% of the theoretical limit. By integrating high- and low-temperature processes, we suppress recombination and enhance contact performance, achieving a fill factor of 87.55%—nearly 98% of the theoretical limit. A model links the ideality factor to carrier loss mechanisms, elucidating carrier recombination in both the bulk and the surface and clarifies key fill factor losses owing to recombination. These innovations provide both experimental and theoretical advances towards scalable, high-efficiency silicon photovoltaics.
化學Chemistry
A molecularly impermeable polymer from two-dimensional polyaramids
二維聚芳酰胺制備分子不滲透性聚合物
▲ 作者：Cody L. Ritt, Michelle Quien, Zitang Wei, Hagen Gress, Mohan T. Dronadula, Kaan Altmisdort, et al.
▲鏈接：
https://www.nature.com/articles/s41586-025-09674-9
▲摘要：所有聚合物都因纏結聚合物鏈的自由體積而表現出氣體滲透性。相比之下，包括石墨烯在內的二維（2D）材料密堆可表現出分子不滲透性。溶液合成的2D聚合物通過縮聚反應表現出分子不滲透性是一個長期目標。
研究組展示了自支撐、自旋涂覆的2D聚芳酰胺納米膜，其氮氣滲透率低于3.1×10-9 Barrer，比每種已知聚合物低近4個數量級，對其他測試氣體（氦氣、氬氣、氧氣、甲烷和六氟化硫）亦是如此。在納米膜涂層微孔的加壓過程中，光學干涉技術可實現對機械敏感邊緣開合狀態的測量，并形成穩定期超過三年的鼓泡結構。
這一發現使2D聚合物諧振器具有高諧振頻率（約8 MHz）和高達537的品質因數，類似于石墨烯。60 nm氣敏鈣鈦礦涂層使晶格退化率降低了14倍，氧氣滲透率為3.3×10-8 Barrer。分子不滲透性聚合物有望實現下一代屏障，具有合成可加工性、化學適應性，并以最少的材料來最大化分子排斥率，最終實現可持續發展目標。
▲ Abstract： All polymers exhibit gas permeability through the free volume of entangled polymer chains. By contrast, two-dimensional (2D) materials including graphene stack densely and can exhibit molecular impermeability. Solution-synthesized 2D polymers that exhibit the latter by poly-condensation have been a longstanding goal. Herein, we demonstrate self-supporting, spin-coated 2D polyaramid nanofilms that exhibit nitrogen permeability below 3.1 × 10-9Barrer, nearly four orders of magnitude lower than every class of existing polymer, and similar for other gases tested (helium, argon, oxygen, methane and sulfur hexafluoride). Optical interference during the pressurization of nanofilm-coated microwells allows measurement of mechanosensitive rim opening and sealing, creating gas-filled bulges that are stable exceeding three years. This discovery enables 2D polymer resonators with high resonance frequencies (about 8 MHz) and quality factors up to 537, similar to graphene. A 60-nm coating of air-sensitive perovskites reduces the lattice degradation rate 14-fold with an oxygen permeability of 3.3 × 10-8 Barrer. Molecularly impermeable polymers promise the next generation of barriers that are synthetically processable, chemically amenable and maximize molecular rejection with minimal material, ultimately advancing sustainability goals.
A multimodal robotic platform for multi-element electrocatalyst discovery
多模態機器人平臺助力多元素電催化劑發現
▲ 作者：Zhen Zhang, Zhichu Ren, Chia-Wei Hsu, Weibin Chen, Zhang-Wei Hong, Chi-Feng Lee, et al.
▲鏈接：
https://www.nature.com/articles/s41586-025-09640-5
▲摘要：“科學人工智能”的目標之一是通過現實世界的實驗發現定制材料。人們已在計算預測和材料合成自動化方面取得了開創性進展。然而，大多數材料實驗仍局限于使用單模態主動學習方法，依賴于單一數據流。人工智能解釋實驗復雜性的潛力在很大程度上尚未得到開發。
研究組介紹了真實世界實驗科學家助駕（CRESt），這是一個將大型多模態模型（包括化學成分、文本嵌入和微觀結構圖像）與知識輔助貝葉斯優化和機器人自動化集成為一體的平臺。CRESt采用基于知識嵌入的搜索空間縮減和自適應探索開發策略來加速材料設計、高通量合成和表征以及電化學性能優化。
CRESt可通過攝像頭進行監控，并生成視覺語言模型驅動的假設，以診斷和糾正實驗異常。CRESt應用于甲酸鹽電化學氧化，在3個月內分析了900多種催化劑化學性質，進行了3500次電化學測試，最終在八元化學空間（Pd-Pt-Cu-Au-Ir-Ce-Nb-Cr）中確定了一種最先進的催化劑，其成本效益提高了9.3倍。
▲ Abstract： One of the goals of ‘AI for Science’ is to discover customized materials through real-world experiments. Pioneering advances have been made in computational predictions and the automation of materials synthesis. Yet most materials experimentation remains constrained to using unimodal active learning approaches, relying on a single data stream. The potential of artificial intelligence to interpret experimental complexity remains largely untapped. Here we present Copilot for Real-world Experimental Scientists (CRESt), a platform that integrates large multimodal models (incorporating chemical compositions, text embeddings and microstructural images) with knowledge-assisted Bayesian optimization and robotic automation. CRESt uses knowledge-embedding-based search space reduction and adaptive exploration–exploitation strategy to accelerate materials design, high-throughput synthesis and characterization, and electrochemical performance optimization. CRESt enables monitoring with cameras and the generation of vision-language-model-driven hypotheses to diagnose and correct experimental anomalies. Applied to electrochemical formate oxidation, CRESt explored more than 900 catalyst chemistries and 3,500 electrochemical tests within 3 months, identifying a state-of-the-art catalyst in the octonary chemical space (Pd–Pt–Cu–Au–Ir–Ce–Nb–Cr) that exhibits a 9.3-fold improvement in cost-specific performance.
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