Featured
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| Open AccessKnowledge-driven design of solid-electrolyte interphases on lithium metal via multiscale modelling
The application of Li metal electrodes in rechargeable batteries is limited by inherent high reactivity. Here, the authors provide model-based insights into the composition and formation mechanisms of the solid-electrolyte interphase on the µs-scale and suggest design strategies for the interphase.
- Janika Wagner-Henke
- , Dacheng Kuai
- & Ulrike Krewer
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Article
| Open AccessMaterial-agnostic machine learning approach enables high relative density in powder bed fusion products
Exploring laser powder bed fusion in manufacturing, the authors demonstrate a machine learning-based method to optimize processing conditions achieving materials with relative density greater than 98% and experimentally verify its generality for multiple distinct powder materials.
- Jaemin Wang
- , Sang Guk Jeong
- & Byeong-Joo Lee
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Article
| Open AccessMechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations
Electronic structure methods are vital, yet they are often too computationally expensive. Here, the authors develop machine learned density matrices to fully represent electronic structures in a computationally cheap and accurate way.
- Zezhu Zeng
- , Felix Wodaczek
- & Bingqing Cheng
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Article
| Open AccessEfficient magnetic switching in a correlated spin glass
GeTe is a ferroelectric semiconductor with broken inversion symmetry, which leads to a large spin-orbit interaction. When doped with small amounts of manganese, it becomes magnetoelectric. Here, Krempasky et al show that the ferrimagnetic ordering of Mn-doped GeTe can be switched with unusually small currents under specific resonant conditions, orders of magnitude smaller than typical for spin-orbit torque based switching.
- Juraj Krempaský
- , Gunther Springholz
- & J. Hugo Dil
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Article
| Open AccessEngineering the formation of spin-defects from first principles
Spin defects in semiconductors are promising for quantum technologies but understanding of defect formation processes in experiment remains incomplete. Here the authors present a computational protocol to study the formation of spin defects at the atomic scale and apply it to the divacancy defect in SiC.
- Cunzhi Zhang
- , Francois Gygi
- & Giulia Galli
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Article
| Open AccessTensorial stress-plastic strain fields in α - ω Zr mixture, transformation kinetics, and friction in diamond-anvil cell
Fields of stress and plastic strain tensors in a sample under high pressures in diamond-anvil cells are important, but measuring them is difficult. Here, the authors suggest a coupled experimental-analytical-computational approach to measure them before, during, and after α−ω transformation in Zr.
- Valery I. Levitas
- , Achyut Dhar
- & K. K. Pandey
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Article
| Open AccessManipulation of nonlinear optical responses in layered ferroelectric niobium oxide dihalides
This paper reports the intralayer ferroelectric-to-antiferroelectric and ferroelectric-toparaelectric phase transitions in layered NbOCl2 and NbOI2 under a small pressure, respectively, along with the strong manipulations of nonlinear optics.
- Liangting Ye
- , Wenju Zhou
- & Bing Huang
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Article
| Open AccessAccelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach
Despite the promise that machine learning (ML) can accelerate catalyst development, truly novel catalysts are challenging to find through ML approaches because of their inability to extrapolate. Here, the authors show an extrapolative ML approach to develop new multi-elemental catalysts.
- Gang Wang
- , Shinya Mine
- & Takashi Toyao
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Article
| Open AccessCapturing dynamical correlations using implicit neural representations
Analysis of experimental data in condensed matter is often challenging due to system complexity and slow character of physical simulations. The authors propose a framework that combines machine learning with theoretical calculations to enable real-time analysis for electron, neutron, and x-ray spectroscopies.
- Sathya R. Chitturi
- , Zhurun Ji
- & Joshua J. Turner
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Article
| Open AccessCharge density wave surface reconstruction in a van der Waals layered material
Recent work has reported puzzling results on the surface of 1T-TaS2. Based on first-principles calculations, the authors show that charge density wave order undergoes surface reconstruction, leading to modifications in the surface electronic structure, which can explain recent experiments.
- Sung-Hoon Lee
- & Doohee Cho
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Article
| Open AccessA theory for colors of strongly correlated electronic systems
Strongly correlated transition metal insulators are often coloured. Understanding the underlying optical response from first-principles calculations is challenging. Now, ab initio many body Green’s function theories are shown to reproduce the colours of NiO and MnF2.
- Swagata Acharya
- , Dimitar Pashov
- & Mikhail I. Katsnelson
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Article
| Open AccessAdaptive multi-temperature control for transport and storage containers enabled by phase-change materials
Reliable transportation of multiple goods with different temperature requirements can logistically challenging. Here, the authors propose an adaptive multi-temperature control system using liquid-solid phase change materials to achieve effective thermal management using just a pair of heat and cold sources.
- Xinchen Zhou
- , Xiang Xu
- & Jiping Huang
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Article
| Open AccessEarly-stage bifurcation of crystallization in a sphere
Thermodynamics predicts equilibrium crystal structures and kinetics discover the pathway to form them. The authors investigate the interplay of thermodynamics and kinetics in the formation of colloidal clusters and reveal a bifurcation at an early stage of the crystallization process.
- Chrameh Fru Mbah
- , Junwei Wang
- & Michael Engel
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Article
| Open AccessMaterial symmetry recognition and property prediction accomplished by crystal capsule representation
Learning global crystal symmetry and interpreting equivariance are crucial for developing ML model to predict electronic properties. Here authors propose a symmetry-enhanced model to simulate cluster interactions and to predict materials properties.
- Chao Liang
- , Yilimiranmu Rouzhahong
- & Huashan Li
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Article
| Open AccessMachine learning the microscopic form of nematic order in twisted double-bilayer graphene
Machine learning methods in condensed matter physics are an emerging tool for providing powerful analytical methods. Here, the authors demonstrate that convolutional neural networks can identify nematic electronic order from STM data of twisted double-layer graphene—even in the presence of heterostrain.
- João Augusto Sobral
- , Stefan Obernauer
- & Mathias S. Scheurer
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Perspective
| Open AccessToward a formal theory for computing machines made out of whatever physics offers
Learning from human brains to build powerful computers is attractive, yet extremely challenging due to the lack of a guiding computing theory. Jaeger et al. give a perspective on a bottom-up approach to engineer unconventional computing systems, which is fundamentally different to the classical theory based on Turing machines.
- Herbert Jaeger
- , Beatriz Noheda
- & Wilfred G. van der Wiel
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Article
| Open AccessTuning electronic and phononic states with hidden order in disordered crystals
Hidden local order in disordered crystals is shown to have a strong impact on electronic and phononic band structures. Local correlations within hidden-order states can open band gaps, thereby changing properties without long-range symmetry breaking.
- Nikolaj Roth
- & Andrew L. Goodwin
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Article
| Open AccessAtomic stiffness for bulk modulus prediction and high-throughput screening of ultraincompressible crystals
Fast and accurate prediction of bulk moduli for diverse materials is challenging. Here, the authors introduce the concept of atomic stiffness to accelerate bulk modulus prediction and high-throughput screening of ultra-incompressible crystals.
- Ruihua Jin
- , Xiaoang Yuan
- & Enlai Gao
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Article
| Open AccessQuantifying disorder one atom at a time using an interpretable graph neural network paradigm
Level of atomic disorder in materials is critical to understanding the effect of local structure on materials properties. Here the authors present a workflow combining structure-aware graph neural networks and physics-inspired order parameter to characterize structural disorder on a per atom basis.
- James Chapman
- , Tim Hsu
- & Brandon C. Wood
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Article
| Open AccessAltering the spectroscopy, electronic structure, and bonding of organometallic curium(III) upon coordination of 4,4′−bipyridine
Despite the distinct electronic properties of the wide variety Cm3+ compounds that have been prepared to date, no singlecrystal structural characterization of a complex containing a Cm−C bond has been reported. Here the authors report the synthesis of a Cm complex bearing trimethylsilylcyclopentadienyl and 4,4’-bipyridine ligands with a low energy emission and identify the 4,4’-bipyridine ligand as the primary quenching agent.
- Brian N. Long
- , María J. Beltrán-Leíva
- & Thomas E. Albrecht-Schönzart
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Article
| Open AccessWitnessing light-driven entanglement using time-resolved resonant inelastic X-ray scattering
Quantum Fisher information is a measure of entanglement that has been previously extracted from equilibrium spectra of quantum materials. Here the authors extend this approach to non-equilibrium systems probed by time-resolved resonant inelastic x-ray scattering measurements.
- Jordyn Hales
- , Utkarsh Bajpai
- & Yao Wang
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Article
| Open AccessLow-spin state of Fe in Fe-doped NiOOH electrocatalysts
Although Fe doping boosts the electrocatalytic performance of NiOOH materials for the oxygen evolution reaction, the underlying mechanism has been not well understood. Here, the authors reveal Fe low-spin state configuration as a main driver of this electrochemical phenomenon.
- Zheng-Da He
- , Rebekka Tesch
- & Piotr M. Kowalski
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Article
| Open AccessAbsence of critical thickness for polar skyrmions with breaking the Kittel’s law
Here, the authors find that ferroelectric skyrmions can be sustained in [(PbTiO3)2/(SrTiO3)2]m ultrathin superlattices. The period-thickness relationship of skyrmions in the ultrathin PbTiO3 layers breaks Kittel’s law.
- Feng-Hui Gong
- , Yun-Long Tang
- & Xiu-Liang Ma
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Article
| Open AccessRealistic phase diagram of water from “first principles” data-driven quantum simulations
The molecular modelling of water has been a long sought-after goal in computational sciences for more than 50 years. Here, the authors show that the data-driven many-body MB-pol potential can provide a realistic representation of the phase diagram of water.
- Sigbjørn Løland Bore
- & Francesco Paesani
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Article
| Open AccessLanthanide-doped MoS2 with enhanced oxygen reduction activity and biperiodic chemical trends
Oxygen reduction reaction plays a key role in many applications of MoS2-based materials. Here, using first-principles simulations, the authors find the enhanced oxygen-reduction activity with a biperiodic chemical trend on the lanthanide-doped MoS2.
- Yu Hao
- , Liping Wang
- & Liang-Feng Huang
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Article
| Open AccessMachine learning-assisted crystal engineering of a zeolite
Zeolites are porous aluminosilicate molecular sieves with uniform pores of molecular dimensions that have a wide range of applications. Here authors use machine learning to guide zeolite synthesis and predict the structure and properties of faujasite zeolites from synthesis conditions.
- Xinyu Li
- , He Han
- & Michael Tsapatsis
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Article
| Open AccessMachine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles
Surface Pourbaix diagrams are critical to understanding the stability of nanomaterials. Here, the authors develop a bond-type embedded crystal graph convolutional neural network model and construct reliable Pourbaix diagrams for real-scale nanoparticles.
- Kihoon Bang
- , Doosun Hong
- & Hyuck Mo Lee
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Article
| Open AccessLithium crystallization at solid interfaces
In solid-state lithium metal batteries, the crystallization of Li-ions deposited at interfaces remains unclear. Here, authors use molecular dynamics simulations to reveal lithium crystallization pathways and energy barriers, guiding improved interfacial engineering and accelerated crystal growth.
- Menghao Yang
- , Yunsheng Liu
- & Yifei Mo
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Article
| Open AccessTopology of vibrational modes predicts plastic events in glasses
It remains challenging to understand the relation between mechanical properties of glasses close to the yielding point and plastic behaviors at microscales. Wu et al. examine the plasticity using topological properties of the vibrational modes and identify a correlation between defects and plastic events.
- Zhen Wei Wu
- , Yixiao Chen
- & Limei Xu
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Article
| Open AccessData-driven prediction of complex crystal structures of dense lithium
Recent experiments reveal undetermined crystalline phases near the melting minimum region in lithium. Here, the authors use a crystal structure search method combined with machine learning to explore the energy landscape of lithium and predict complex crystal structures.
- Xiaoyang Wang
- , Zhenyu Wang
- & Yanming Ma
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Article
| Open AccessA map of single-phase high-entropy alloys
The compositional space of potential high-entropy alloys is gigantic and difficult to explore efficiently. Here, the authors use high-throughput first-principles computations to predict what elements can mix to form high-entropy alloys, understanding of the factors favoring their formation.
- Wei Chen
- , Antoine Hilhorst
- & Geoffroy Hautier
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Article
| Open AccessGeneral framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian
Fundamental symmetries are crucial to the deep-learning modeling of physical systems. Here the authors use equivariant neural networks preserving the Euclidean symmetries to accelerate electronic structure calculations by orders of magnitude keeping sub-meV accuracy.
- Xiaoxun Gong
- , He Li
- & Yong Xu
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Article
| Open AccessMachine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
Ionic polymer electrolytes containing non-flammable ionic liquids and polyelectrolytes have the potential to create safe and high-energy batteries. Here, the authors propose a machine-learning approach to identify ionic liquids suitable for such electrolytes in lithium metal batteries.
- Kai Li
- , Jifeng Wang
- & Ying Wang
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Article
| Open AccessSynergistic binding sites in a metal-organic framework for the optical sensing of nitrogen dioxide
Luminescent metal-organic frameworks are an emerging class of optical sensors capable to capture and detect toxic gases. Here, the authors report the incorporation of synergistic binding sites in MOF-808 through post-synthetic modification with copper for optical sensing of NO2 at remarkably low concentrations.
- Isabel del Castillo-Velilla
- , Ahmad Sousaraei
- & Ana E. Platero-Prats
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Article
| Open AccessDynamic evolution of the active center driven by hemilabile coordination in Cu/CeO2 single-atom catalyst
This work explores the concept of hemilability in single atom catalysts. The results imply that this effect can promote reactant activation and product desorption simultaneously to provide a potential approach to circumvent the Sabatier volcano.
- Zheng Chen
- , Zhangyun Liu
- & Xin Xu
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Article
| Open AccessElectronic signatures of Lorentzian dynamics and charge fluctuations in lithiated graphite structures
Lithium graphite intercalation compounds are important for developing Li-ion batteries. Here authors simulate the interaction of high energy X-rays with Li ions intercalated in graphite and show that Li ions behave in an unexpected non-Gaussian fashion, leading to increasingly chaotic behaviour as the ion concentration reduces.
- Sasawat Jamnuch
- & Tod A. Pascal
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Article
| Open AccessControlling doping efficiency in organic semiconductors by tuning short-range overscreening
Doping is widely adopted to make organic semiconductors more conductive, yet the impact of molecular electronic properties on doping performance is still not fully understood. Armleder et al. compute host-dopant interactions and show that a short-range overscreening effect strongly affects conductivity.
- Jonas Armleder
- , Tobias Neumann
- & Artem Fediai
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Article
| Open AccessMagnesium oxide-water compounds at megabar pressure and implications on planetary interiors
Magnesium Oxide and water are abundant in the interior of planets. Here, the authors predict three new MgO-H2O compounds: Mg2O3H2, MgO3H4 and MgO4H6, and they exhibit superionic behavior in planetary interior conditions.
- Shuning Pan
- , Tianheng Huang
- & Jian Sun
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Article
| Open AccessThe crucial role of adhesion in the transmigration of active droplets through interstitial orifices
Active fluid droplets are relevant for development of bio-inspired soft materials, however their motion in heterogeneous surrounding environments remains challenging. The authors uncover the role of adhesion forces for a variety of dynamic regimes of active fluid droplet crossing a narrow constriction.
- A. Tiribocchi
- , M. Durve
- & S. Succi
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Article
| Open AccessComplex-tensor theory of simple smectics
As lamellar materials, smectics exhibit both liquid and solid characteristics, making them difficult to model at the mesoscale. Paget et al. propose a complex tensor order parameter that reflects the smectic symmetries, capable of describing complex defects including dislocations and disclinations.
- Jack Paget
- , Marco G. Mazza
- & Tyler N. Shendruk
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Article
| Open AccessNuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics
The quantum properties of hydrogen atoms in zeolite-catalyzed reactions are generally neglected due to high computational costs. Here, the authors leverage machine learning to derive accurate quantum kinetics for proton transfer reactions in heterogeneous catalysis.
- Massimo Bocus
- , Ruben Goeminne
- & Veronique Van Speybroeck
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Article
| Open AccessLarge-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning
Accurate liquid water modelling is challenging. Here the authors use X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multiphase simulation to simulate fuel cell and guide fuel cell design.
- Ying Da Wang
- , Quentin Meyer
- & Ryan T. Armstrong
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Article
| Open AccessHigher rank chirality and non-Hermitian skin effect in a topolectrical circuit
In this work, the authors implement a crystalline rank-2 chiral modes by employing non-Hermitian dynamics. They showed the momentum-resolved dynamics and non-Hermitian skin effect associated with the rank-2 chirality both theoretically and experimentally.
- Penghao Zhu
- , Xiao-Qi Sun
- & Gaurav Bahl
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Article
| Open AccessLearning local equivariant representations for large-scale atomistic dynamics
The paper presents a method that allows scaling machine learning interatomic potentials to extremely large systems, while at the same time retaining the remarkable accuracy and learning efficiency of deep equivariant models. This is obtained with an E(3)- equivariant neural network architecture that combines the high accuracy of equivariant neural networks with the scalability of local methods.
- Albert Musaelian
- , Simon Batzner
- & Boris Kozinsky
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Article
| Open AccessMachine learning models to accelerate the design of polymeric long-acting injectables
Polymer-based long-acting injectable drugs are a promising therapeutic strategy for chronic diseases. Here the authors use machine learning to inform the data-driven development of advanced drug formulations.
- Pauric Bannigan
- , Zeqing Bao
- & Christine Allen
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Article
| Open AccessQuadrupolar 23Na+ NMR relaxation as a probe of subpicosecond collective dynamics in aqueous electrolyte solutions
Quadrupolar nuclear magnetic relaxometry senses electrical fluctuations around nuclei, but their microscopic interpretation remains elusive. Here, the authors combine experiments and multiscale simulations to interpret relaxation rates in electrolyte solutions and assess commonly used models.
- Iurii Chubak
- , Leeor Alon
- & Benjamin Rotenberg
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Article
| Open AccessToward the design of ultrahigh-entropy alloys via mining six million texts
The avalanche of publications challenges the norm that researchers extract knowledge from literature to design materials. Here the authors present a text-mining method that is implemented based on the abstracts of 6.4 million papers to enable the design of new high entropy alloys.
- Zongrui Pei
- , Junqi Yin
- & Dierk Raabe
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Article
| Open AccessDiscovery of two-dimensional binary nanoparticle superlattices using global Monte Carlo optimization
Binary nanoparticle superlattices exhibit different collective optical, magnetic, and electronic properties. Here, the authors develop an efficient global optimization algorithm for the discovery of periodic 2D architectures forming at fluid interfaces.
- Yilong Zhou
- & Gaurav Arya
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| Open AccessData-driven discovery of dimensionless numbers and governing laws from scarce measurements
Dimension reduction techniques allow to simplify complex process design and system optimization in various engineering problems. The authors propose here a machine learning approach to discover dominant dimensionless numbers and governing laws from scarce measurement data.
- Xiaoyu Xie
- , Arash Samaei
- & Zhengtao Gan