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Metal-halide perovskites are a promising class of semiconducting materials for optoelectronics, such as solar cells and light-emitting diodes, due to their excellent optical and electronic properties, processability from both solution and vapor phase, and application-specific chemistry. In fact, perovskites are both excellent light emitters and show great promise for tandems with Si. However, several fundamental properties of these materials are yet to be fully understood as they can be difficult to probe experimentally. This includes the role of mobile ions on efficiency losses, the origin of degradation and self-healing, and why perovskites appear to be more defect-tolerant than other thin-film semiconductors. Fortunately, material and device modeling tools are powerful for understanding fundamental properties not directly available from experiments and can provide complementary insight. This Special Topic aims to publish contributions to material and device simulations that target the various unsolved questions regarding perovskites, whether numerical, first-principle, or ML/AI-informed.

Topics covered include, but are not limited to:

• Novel Device architectures and device optimization

• Modeling within metrology and characterization

• Insight into degradation pathways and self-healing properties

• The role of mobile ions on hysteresis and device performance

• Trap states, recombination pathways, and optical losses

• Single-crystal growth and applications

• Defect formation and compensation

• Grain boundaries, interfaces, and surfaces

• Tuning of light emission

• Crystallization kinetics during thin-film deposition

Guest Editors:

• Jason Alexander Röhr, New York University

• Vincent Le Corre, University of Southern Denmark

• Pietro Caprioglio, Hanwha Qcells Europe

In the current era of Big Data, with over 150 zettabytes of data being created and replicated globally, the energy consumption of information and communication technologies (ICT) is rising exponentially. A significant portion of this energy is wasted as heat due to the Joule effect (i.e., from electric currents required to operate memory devices). Additionally, traditional computers have separate memory and data processing units that must continuously communicate, leading to substantial time and energy expenditure.

Given the limitations of current computing devices, advancing ICT has become increasingly challenging. A paradigm shift in computing is essential. Presently, several strategies aim to develop memory devices that emulate the human brain, where data storage and processing occur in the same unit (in-memory neuromorphic computing). Various materials are being explored for this purpose, including memristive, spintronic, ferroelectric, multiferroic, magneto-ionic, 2D or phase-change materials. In addition to new materials, advanced computing concepts are also being developed, such as deep, spiking, recurrent, or Hopfield neural networks. Other approaches include reservoir, photonic, thermodynamic,or analog computing, amongst others.

This Special Topic brings together scholars from diverse scientific disciplines—physics, chemistry, materials science, engineering—to explore all aspects related to advanced materials for energy-efficient memories, from fundamentals to applications.

This special topic is co-organized by APL Materials. Authors are welcome to submit to either journal for this Special Topic.

Topics covered include, but are not limited to:

• Ferroelectric materials

• Topological insulators

• Reservoir computing

• Skyrmion and domain wall memories

• Wide band gap semiconductors

• In-memory computing

• Magnonic materials (spin waves)

• Opto-electronic memristors

• Magnetic tunnel junction (MTJ)

• Multiferroic materials

• Token-based computing

• Spin transfer torque (STT) memory

• Spintronic materials

• Neuromorphic or brain-inspired computing

• Spin orbit torque (SOT) memory

• Redox-based memory

• Deep neural networks

• Memristive memories

• Metal oxide resistive switching memory

• In-memory logic

• Voltage-controlled magnetic memories

• Conductive bridge random-access memory (RAM)

• Spiking neural network (SNN)

• Phase change memories

• Perovskite memristors

• Convolutional neural network (CNN)

• Stochastic and probabilistic computing

• Electrochemical random-access memory (ECRAM)

• Recurrent neural network

• Thermodynamic computing

• Organic memristors

• Hopfield networks

• Photonic computing

• Molecular memristors

• In-materia computing

• Combinatorial optimization

• 2D semiconductors

• Ising machine

• Analog computing

Guest Editors

• Karin Everschor-Sitte, Universität Duisburg-Essen

• Daniele Ielmini, Politecnico di Milano

• Jordi Sort, Universitat Autònoma de Barcelona, Associate Editor, APL Materials

• Monica Lira-Cantu, Catalan Institute of Nanoscience and Nanotechnology, Editor-in-Chief, APL Energy

This Special Topic aims to collect research related to energy carried out by Latin America’s scientists. In this collection, we will publish peer-reviewed papers that cover a broad spectrum of energy research from fundamentals to applied science. Topics covered include, but are not limited to, solar cells, energy devices, energy transition in Latin America, and hydrogen and materials for energy. Interdisciplinary research from physics, chemistry, materials science, engineering, and related fields is welcome. A central criterion for acceptance is the scientific quality and novelty of the manuscripts submitted. This Special Topic will accept reviews, perspectives, original research articles, and proof of concept and prototype articles.

Topics covered include, but are not limited to:

• Third generation solar cells (organic, perovskites, QDs, tandem cells)

• Energy transition in Latin America (hydropower, wind power, solar, bioenergy, smart grids, carbon capture technologies, energy efficiency, energy economy)

• Hydrogen (generation, storage, transformation, and uses)

• Energy devices (batteries, supercapacitors, LEDs, electro and photocatalysis)

• Materials for energy (novel materials, materials stability, synthesis and characterization protocols and standards, in-situ and in-operando characterization)

• Simulation of energy systems and materials

Guest Editors:

• Franklin Jaramillo, Universidad de Antioquia (UdeA), Colombia

• Juan Felipe Montoya, Universidad de Antioquia (UdeA), Colombia

• Diego Solis-Ibarra, Universidad Nacional Autónoma de México (UNAM)

• Tatiana Gómez Cano, Universidad Autónoma de Chile