《自然》（20231207出版）一周論文導讀

原文地址：http://news.sciencenet.cn/htmlnews/2023/12/513926.shtm

編譯 | 馮維維

Nature, Volume 624 Issue 7990, 7 December 2023

《自然》 第624卷，7990期，2023年12月7日

  ?

物理學Physics

Self-assembled photonic cavities with atomic-scale confinement

具有原子尺度約束的自組裝光子腔

▲ 作者：Ali Nawaz Babar, Thor August Schimmell Weis, Konstantinos Tsoukalas, Shima Kadkhodazadeh, Guillermo Arregui, Babak Vosoughi Lahijani & S?ren Stobbe

▲ 鏈接：

https://www.nature.com/articles/s41586-023-06736-8

▲ 摘要：

盡管自組裝納米技術的研究取得了巨大的進展，如大分子、納米線和二維材料，但從納米尺度到宏觀尺度的合成自組裝方法仍然不可擴展，不如生物自組裝。

相比之下，平面半導體技術由于其固有的可擴展性而產生了巨大的技術影響，但它似乎無法達到自組裝的原子尺寸。研究者使用表面力，包括卡西米爾-范德華相互作用，來確定自組裝和自對準懸浮硅納米結構，盡管只使用傳統光刻和蝕刻，其空洞特征遠低于傳統光刻和蝕刻的長度尺度。

該方法具有顯著的魯棒性，自組裝閾值單調依賴于數千個被測器件的所有控制參數。研究者通過制造任何其他已知方法都無法制造的納米結構來說明這些概念的潛力：波導耦合高Q硅光子腔，將電信光子限制在2納米的氣隙中，寬高比為100，對應于比衍射極限低100倍以上的模式體積。

掃描透射電子顯微鏡測量證實了制造亞納米尺寸設備的能力。研究者表示該技術將自組裝的原子尺寸與平面半導體的可擴展性相結合，是邁向新一代制造技術的第一步。

▲ Abstract：

Despite tremendous progress in research on self-assembled nanotechnological building blocks, such as macromolecules, nanowires and two-dimensional materials, synthetic self-assembly methods that bridge the nanoscopic to macroscopic dimensions remain unscalable and inferior to biological self-assembly. By contrast, planar semiconductor technology has had an immense technological impact, owing to its inherent scalability, yet it seems unable to reach the atomic dimensions enabled by self-assembly. Here, we use surface forces, including Casimir–van der Waals interactions, to deterministically self-assemble and self-align suspended silicon nanostructures with void features well below the length scales possible with conventional lithography and etching, despite using only conventional lithography and etching. The method is remarkably robust and the threshold for self-assembly depends monotonically on all the governing parameters across thousands of measured devices. We illustrate the potential of these concepts by fabricating nanostructures that are impossible to make with any other known method: waveguide-coupled high-Q silicon photonic cavities that confine telecom photons to 2?nm air gaps with an aspect ratio of 100, corresponding to mode volumes more than 100 times below the diffraction limit. Scanning transmission electron microscopy measurements confirm the ability to build devices with sub-nanometre dimensions. Our work constitutes the first steps towards a new generation of fabrication technology that combines the atomic dimensions enabled by self-assembly with the scalability of planar semiconductors.

Single-molecule electron spin resonance by means of atomic force microscopy

原子力顯微鏡下的單分子電子自旋共振

▲ 作者：Lisanne Sellies, Raffael Spachtholz, Sonja Bleher, Jakob Eckrich, Philipp Scheuerer & Jascha Repp

▲ 鏈接：

https://www.nature.com/articles/s41586-023-06754-6

▲ 摘要：

理解和控制開放量子系統中的退相干是科學研究的基礎，而實現長相干時間對于量子信息處理至關重要。

盡管單個系統已經取得了很大的進展，并且單自旋的電子自旋共振（ESR）已經被證明具有納米級分辨率，但在許多復雜的固態量子系統中，對退相干的理解最終需要將環境控制到原子尺度，這可能通過掃描探針顯微鏡及其原子和分子表征和操作能力來實現。

因此，最近在掃描隧道顯微鏡中實現的ESR是實現這一目標的一個里程碑，并很快被相干振蕩的演示和真實空間原子分辨率的核自旋所遵循。原子操縱甚至激發了實現第一個人工原子尺度量子器件的雄心。然而，這種方法固有的基于電流的傳感限制了相干時間。

研究者展示了泵探針ESR原子力顯微鏡（AFM）檢測電子自旋躍遷之間的非平衡態的單個并五苯分子。這些躍遷的光譜表現出亞納米電子伏特的光譜分辨率，允許局部區分分子，只是在它們的同位素配置不同。

此外，電子自旋可以在數十微秒內進行相干操縱。我們預計單分子ESR-AFM可以與原子操作和表征相結合，從而為了解原子定義良好的量子元素中退相干的原子起源和基礎量子傳感實驗鋪平道路。

▲ Abstract：

Understanding and controlling decoherence in open quantum systems is of fundamental interest in science, whereas achieving long coherence times is critical for quantum information processing. Although great progress was made for individual systems, and electron spin resonance (ESR) of single spins with nanoscale resolution has been demonstrated, the understanding of decoherence in many complex solid-state quantum systems requires ultimately controlling the environment down to atomic scales, as potentially enabled by scanning probe microscopy with its atomic and molecular characterization and manipulation capabilities. Consequently, the recent implementation of ESR in scanning tunnelling microscopy represents a milestone towards this goal and was quickly followed by the demonstration of coherent oscillations and access to nuclear spins with real-space atomic resolution. Atomic manipulation even fuelled the ambition to realize the first artificial atomic-scale quantum devices. However, the current-based sensing inherent to this method limits coherence times. Here we demonstrate pump–probe ESR atomic force microscopy (AFM) detection of electron spin transitions between non-equilibrium triplet states of individual pentacene molecules. Spectra of these transitions exhibit sub-nanoelectronvolt spectral resolution, allowing local discrimination of molecules that only differ in their isotopic configuration. Furthermore, the electron spins can be coherently manipulated over tens of microseconds. We anticipate that single-molecule ESR-AFM can be combined with atomic manipulation and characterization and thereby paves the way to learn about the atomistic origins of decoherence in atomically well-defined quantum elements and for fundamental quantum-sensing experiments.

化學Chemistry

Scaling deep learning for materials discovery

擴展深度學習用于材料發現

▲ 作者：Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon & Ekin Dogus Cubuk

▲ 鏈接：

https://www.nature.com/articles/s41586-023-06735-9

▲ 摘要：

新型功能材料使從清潔能源到信息處理等技術應用取得根本性突破。從微芯片到電池和光伏，無機晶體的發現一直受到昂貴的試錯方法的阻礙。同時，隨著數據和計算量的增加，語言、視覺和生物學的深度學習模型也顯示出了新興的預測能力。

研究者展示了大規模訓練的圖網絡可以達到前所未有的泛化水平，將材料發現的效率提高了一個數量級。在持續研究中發現的4.8萬個穩定晶體的基礎上，效率的提高使人們能夠在目前的凸殼下發現220萬個結構，其中許多結構超出了人類以前的化學直覺。

這項研究代表了人類已知的穩定物質的一個數量級的擴展。最終凸包上的穩定發現將用于篩選技術應用，正如作者對分層材料和固體電解質候選物的演示一樣。

在穩定結構中，736個已經獨立實驗實現。數以億計的第一性原理計算的規模和多樣性也為下游應用解鎖了建模能力，特別是導致高度精確和強大的學習原子間勢，可用于凝聚態分子動力學模擬和高保真離子電導率零射預測。

▲ Abstract：

Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by expensive trial-and-error approaches. Concurrently, deep-learning models for language, vision and biology have showcased emergent predictive capabilities with increasing data and computation. Here we show that graph networks trained at scale can reach unprecedented levels of generalization, improving the efficiency of materials discovery by an order of magnitude. Building on 48,000 stable crystals identified in continuing studies, improved efficiency enables the discovery of 2.2 million structures below the current convex hull, many of which escaped previous human chemical intuition. Our work represents an order-of-magnitude expansion in stable materials known to humanity. Stable discoveries that are on the final convex hull will be made available to screen for technological applications, as we demonstrate for layered materials and solid-electrolyte candidates. Of the stable structures, 736 have already been independently experimentally realized. The scale and diversity of hundreds of millions of first-principles calculations also unlock modelling capabilities for downstream applications, leading in particular to highly accurate and robust learned interatomic potentials that can be used in condensed-phase molecular-dynamics simulations and high-fidelity zero-shot prediction of ionic conductivity.

An autonomous laboratory for the accelerated synthesis of novel materials

一個加速合成新材料的自主實驗室

▲ 作者：Nathan J. Szymanski, Bernardus Rendy, Yuxing Fei, Rishi E. Kumar, Tanjin He, David Milsted, Matthew J. McDermott, Max Gallant, Ekin Dogus Cubuk, Amil Merchant, Haegyeom Kim, Anubhav Jain, Christopher J. Bartel, Kristin Persson, Yan Zeng & Gerbrand Ceder

▲ 鏈接：

https://www.nature.com/articles/s41586-023-06734-w

▲ 摘要：

為了縮小新材料的計算篩選和實驗實現之間的差距，研究者引入了自主實驗室A-Lab，用于無機粉末的固態合成。該平臺使用計算、文獻中的歷史數據、機器學習（ML）和主動學習來計劃和解釋使用機器人進行的實驗結果。

在17天的連續運行中，A- Lab從58個目標中實現了41種新化合物，包括各種氧化物和磷酸鹽，這些目標是使用材料項目和谷歌深度思維的大規模從頭算相穩定性數據確定的。

合成配方由基于文獻的自然語言模型提出，并使用基于熱力學的主動學習方法進行優化。分析失敗的合成為改進現有的材料篩選和合成設計技術提供了直接和可行的建議。

高成功率證明了人工智能驅動平臺在自主材料發現方面的有效性，并激勵了計算、歷史知識和機器人技術的進一步整合。

▲ Abstract：

To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17?days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

氣候和生態Climate & Ecology

Aligning climate scenarios to emissions inventories shifts global benchmarks

將氣候情景與排放清單相結合會改變全球基準

▲ 作者：Matthew J. Gidden, Thomas Gasser, Giacomo Grassi, Nicklas Forsell, Iris Janssens, William F. Lamb, Jan Minx, Zebedee Nicholls, Jan Steinhauser & Keywan Riahi

▲ 鏈接：

ttps://www.nature.com/articles/s41586-023-06724-y

▲ 摘要：

評估在實現《巴黎協定》方面取得的全球進展，需要始終如一地衡量各國針對模擬緩解途徑采取的總體行動和承諾。

然而，國家溫室氣體清單（NGHGIs）和人為排放的科學評估遵循不同的陸地碳通量核算慣例，導致目前的排放估計值存在很大差異，這一差距將隨著時間的推移而擴大。研究者使用最先進的方法和土地碳循環模擬器，將政府間氣候變化專門委員會評估的緩解途徑與國家溫室氣體地理信息系統進行比較。

研究結果發現，當使用NGHGI公約計算時，關鍵的全球緩解基準變得更難實現，這既需要更早的二氧化碳凈零排放時間，也需要更低的累積排放量。

此外，減弱自然碳清除過程，如碳施肥，可以掩蓋人為的陸地清除努力，其結果是，到2100年，全球溫室氣體地理區域的陸地碳通量最終可能成為排放源。研究結果對全球盤點很重要，表明各國需要提高各自氣候目標的集體雄心，以保持與全球溫度目標的一致。

▲ Abstract：

Taking stock of global progress towards achieving the Paris Agreement requires consistently measuring aggregate national actions and pledges against modelled mitigation pathways. However, national greenhouse gas inventories (NGHGIs) and scientific assessments of anthropogenic emissions follow different accounting conventions for land-based carbon fluxes resulting in a large difference in the present emission estimates, a gap that will evolve over time. Using state-of-the-art methodologies and a land carbon-cycle emulator, we align the Intergovernmental Panel on Climate Change (IPCC)-assessed mitigation pathways with the NGHGIs to make a comparison. We find that the key global mitigation benchmarks become harder to achieve when calculated using the NGHGI conventions, requiring both earlier net-zero CO2 timing and lower cumulative emissions. Furthermore, weakening natural carbon removal processes such as carbon fertilization can mask anthropogenic land-based removal efforts, with the result that land-based carbon fluxes in NGHGIs may ultimately become sources of emissions by 2100. Our results are important for the Global Stocktake6, suggesting that nations will need to increase the collective ambition of their climate targets to remain consistent with the global temperature goals.

Integrated global assessment of the natural forest carbon potential

天然林碳潛力的全球綜合評估

▲ 作者：Lidong Mo, Constantin M. Zohner, Peter B. Reich, Jingjing Liang, Sergio de Miguel, Gert-Jan Nabuurs, Susanne S. Renner, Johan van den Hoogen, Arnan Araza, Martin Herold, Leila Mirzagholi, Haozhi Ma, Colin Averill, Oliver L. Phillips, Javier G. P. Gamarra, Iris Hordijk, Devin Routh, Meinrad Abegg, Yves C. Adou Yao, Giorgio Alberti, Angelica M. Almeyda Zambrano, Braulio Vilchez Alvarado, Esteban Alvarez-Dávila, Patricia Alvarez-Loayza, …Thomas W. Crowther Show authors

▲ 鏈接：

https://www.nature.com/articles/s41586-023-06723-z

▲ 摘要：

森林是一個重要的陸地碳匯，但土地利用和氣候的人為變化大大縮小了這一系統的規模。用于量化全球森林碳損失的遙感估算具有相當大的不確定性，缺乏全面的地面評估來對這些估算進行基準測試。

研究者結合了幾種地面來源和衛星來源的方法來評估農業和城市土地以外的全球森林碳潛力的規模。盡管存在區域差異，但這些預測在全球范圍內顯示出顯著的一致性，地面來源和衛星估算值之間的差異僅為12%。

目前，全球森林碳儲量明顯低于自然潛力，低人類足跡地區總虧缺226 Gt（模型范圍為151 ~ 363 Gt）。這一潛力的大部分（61%，139億噸碳當量）位于有森林的地區，在這些地區，生態系統保護可以使森林恢復到成熟。其余39%（87億噸碳當量）的潛力存在于森林被砍伐或破碎的地區。

雖然森林不能替代減排，但研究結果支持這樣一種觀點，即保護、恢復和可持續管理多樣化的森林為實現全球氣候和生物多樣性目標做出了寶貴的貢獻。

▲ Abstract：