Compile |Feng Weiwei
nature, volume 624 issue 7990, 7 december 2023
Nature Vol. 624, No. 7990, December 7, 2023
Physics
self-assembled photonic c**ities with atomic-scale confinement
Self-assembled photonic cavities with atomic-scale constraints
Authors: Ali Nawaz Babar, Thor August Schhimmell Weis, Konstantinos Tsoukalas, Shima Kadkhodazadeh, Guillermo Arregui, Babak Vosoughi Lahijani & S?ren stobbe
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Summary:
Although great progress has been made in the research of self-assembled nanotechnologies, such as macromolecules, nanowires, and two-dimensional materials, synthetic self-assembly methods from the nanoscale to the macroscale are still not scalable and inferior to biological self-assembly.
In contrast, planar semiconductor technology has had a huge technological impact due to its inherent scalability, but it does not seem to be able to reach the size of a self-assembled atom. The researchers used surface forces, including Casimir-van der Waals interactions, to determine self-assembled and self-aligned suspended silicon nanostructures, although only conventional lithography and etching were used, and their voiding characteristics were much lower than the length scales of traditional lithography and etching.
The method is remarkably robust, and the self-assembly threshold is monotonically dependent on all control parameters of thousands of devices under test. The researchers illustrate the potential of these concepts by fabricating nanostructures that are not possible to be fabricated by any other known method: waveguide-coupled high-q silicon photonic cavities that confine telecommunication photons to an air gap of 2 nanometers with an aspect ratio of 100, corresponding to a mode volume more than 100 times lower than the diffraction limit.
Scanning transmission electron microscopy measurements confirm the ability to fabricate sub-nanometer size devices. The technology combines the size of self-assembled atoms with the scalability of planar semiconductors, and is the first step toward a new generation of manufacturing technologies, the researchers said.
▲ 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: w**eguide-coupled high-q silicon photonic c**ities 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
Single-molecule electron spin resonance under atomic force microscopy
Authors: Lisanne Sellies, Raffael Spachtholz, Sonja Bleher, Jakob Eckrich, Philipp Scheuerer & Jascha Repp
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Summary:
Understanding and controlling decoherence in open quantum systems is the foundation of scientific research, and achieving long coherence time is critical for quantum information processing.
Although much progress has been made in individual systems, and electron spin resonance (ESR) of single spins has been shown to have nanoscale resolution, in many complex solid-state quantum systems, the understanding of decoherence ultimately requires the environment to be controlled to the atomic scale, which may be achieved through scanning probe microscopy and its atomic and molecular characterization and manipulation capabilities.
Therefore, the recent implementation of ESR in scanning tunneling microscopy is a milestone in achieving this goal and is soon followed by the demonstration of coherent oscillations and nuclear spin at real space atomic resolution. Atomic manipulation has even sparked ambitions to achieve the first artificial atomic-scale quantum device. However, the inherent current-based sensing of this method limits the coherence time.
The researchers demonstrated the pump probe ESR atomic force microscopy (AFM) to detect a single pentabenzene molecule in a non-equilibrium state between electron spin transitions. The spectra of these transitions exhibit a spectral resolution of sub-nanometer electron volts, allowing local differentiation of molecules, only differing in their isotopic configuration.
In addition, the electron spin can be coherently manipulated in tens of microseconds. We anticipate that single-molecule ESR-AFM can be combined with atomic manipulation and characterization, paving the way for understanding atomic origins and fundamental quantum sensing experiments in decoherence in atomically well-defined quantum elements.
▲ 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 p**es the way to learn about the atomistic origins of decoherence in atomically well-defined quantum elements and for fundamental quantum-sensing experiments.
Chemistry
scaling deep learning for materials discovery
Extended Deep Learning Xi for material discovery
Authors: Amil Merchant, Simon Batzner, Samuel S schoenholz, muratahan aykol, gowoon cheon & ekin dogus cubuk
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Summary:
New functional materials enable fundamental breakthroughs in the application of technologies ranging from clean energy to information processing. From microchips to cells and photovoltaics, the discovery of inorganic crystals has been hampered by expensive trial-and-error methods. At the same time, with the increase of data and computation, the deep Xi models of language, vision, and biology have also shown emerging capabilities.
The researchers have shown that large-scale trained graph networks can reach unprecedented levels of generalization, increasing the efficiency of material discovery by an order of magnitude. 4On the basis of 80,000 stable crystals, the increase in efficiency has made it possible to discover 2.2 million structures beneath the current convex shell, many of which are beyond human previous chemical intuition.
This study represents an order of magnitude expansion of the stable substance known to man. The stable findings on the final convex hull will be used for screening applications, as demonstrated by the authors for layered materials and solid electrolyte candidates.
Of the stable structures, 736 have been experimentally implemented independently. The scale and diversity of hundreds of millions of first-principles calculations also unlocks modeling capabilities for downstream applications, particularly leading to highly accurate and robust interatomic potentials Xi can be used for condensed matter molecular dynamics simulations and high-fidelity ionic conductivity zero-shot**.
▲ 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 h**e 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 **ailable to screen for technological applications, as we demonstrate for layered materials and solid-electrolyte candidates. of the stable structures, 736 h**e 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
An autonomous laboratory to accelerate the synthesis of new materials
by Nathan J szymanski, bernardus rendy, yuxing fei, rishi e. kumar, tanjin he, d**id milsted, matthew j. mcdermott, max gallant, ekin dogus cubuk, amil merchant, haegyeom kim, anubh** jain, christopher j. bartel, kristin persson, yan zeng & gerbrand ceder
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Summary:
In order to bridge the gap between the computational screening and experimental realization of new materials, the researchers introduced an independent laboratory, A-Lab, for the solid-state synthesis of inorganic powders. The platform uses computation, historical data from the literature, machine Xi (ML), and active learning Xi to plan and interpret the results of experiments conducted with robots.
Over the course of 17 days of continuous operation, A-Lab achieved 41 new compounds, including a variety of oxides and phosphates, from 58 targets, which were determined using large-scale ab initio phase stability data from the Materials Project and Google Deep Thinking.
The synthetic recipe was proposed by a literature-based natural language model and optimized using a thermodynamics-based active Xi method. Analyzing failed synthesis provides direct and actionable recommendations for improving existing material screening and synthesis design techniques.
The high success rate demonstrates the effectiveness of AI-driven platforms for autonomous material discovery and inspires further integration of computing, historical knowledge, and robotics.
▲ 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.
Climate & ecology
aligning climate scenarios to emissions inventories shifts global benchmarks
Combining climate scenarios with emissions inventories can change global benchmarks
by Matthew J gidden, thomas gasser, giacomo grassi, nicklas forsell, iris janssens, william f. lamb, jan minx, zebedee nicholls, jan steinhauser & keywan riahi
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Summary:
Assessing global progress towards the Paris Agreement requires a consistent measure of countries' overall actions and commitments to the simulated mitigation pathways.
However, national greenhouse gas inventories (NHGGIS) and scientific assessments of anthropogenic emissions follow different terrestrial carbon flux accounting conventions, resulting in large differences in current emission estimates, and this gap will widen over time. Using state-of-the-art methods and land carbon cycle simulators, the researchers compared the mitigation pathways assessed by the Inter-Panel on Climate Change with the National Geographic Information System for Greenhouse Gases.
The findings found that key global mitigation baselines become more difficult to achieve when calculated using the NGHGI Convention, which requires both an earlier time to net-zero CO2 emissions and lower cumulative emissions.
In addition, attenuating natural carbon removal processes, such as carbon fertilization, can mask anthropogenic terrestrial removal efforts, with the result that terrestrial carbon fluxes in geographic regions of global greenhouse gases could eventually become sources of emissions by 2100. The findings are important for the global stocktake, suggesting that countries need to increase their collective ambition on their climate targets to stay aligned with global temperature targets.
▲ 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
A comprehensive global assessment of the carbon potential of natural forests
Authors: 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 **erill, oliver l. phillips, j**ier 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
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Summary:
Forests are an important terrestrial carbon sink, but anthropogenic changes in land use and climate have greatly reduced the size of this system. Remotely sensed estimates used to quantify global forest carbon loss have considerable uncertainties and lack comprehensive ground-based assessments to benchmark these estimates.
The researchers have combined several ground-based and satellite-based methods to assess the scale of global forest carbon potential beyond agricultural and urban land. Despite regional differences, these ** show significant agreement across the globe, with a difference of only 12% between terrestrial ** and satellite estimates.
At present, the global forest carbon stock is significantly lower than the natural potential, and the total deficit in low human footprint areas is 226 gt (the model range is 151 363 gt). The majority of this potential (61 percent, 13.9 billion tonnes of carbon equivalent) is located in forested areas where ecosystem conservation can bring forests back to maturity. The remaining 39% (8.7 billion tonnes of carbon equivalent) of the potential exists in areas where forests have been cut down or fragmented.
While forests are not a substitute for reducing emissions, the findings support the idea that protecting, restoring and sustainably managing diverse forests makes a valuable contribution to achieving global climate and biodiversity goals.
▲ abstract：
forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate h**e considerably reduced the scale of this system. remote-sensing estimates to quantify carbon losses from global forests are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. here we combine several ground-sourced6 and satellite-derived approaches to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. at present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 gt (model range = 151–363 gt) in areas with low human footprint. most (61%, 139 gt c) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. the remaining 39% (87 gt c) of potential lies in regions in which forests h**e been removed or fragmented. although forests cannot be a substitute for emissions reductions, our results support the idea that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.