Results 101 to 110 of about 1,386 (248)

Design Rules for Quantitative Control of Intragrain Planar Defects in Halide Perovskites

Advanced Science, EarlyView.
Dose‐budgeted electron microscopy reveals the design rules governing intragrain planar defects in halide perovskites: {112}t ferroelastic twins proliferate with increasing tetragonality and grain size, whereas {111}c twins and stacking faults emerge as the lattice approaches cubic symmetry.
Byeongjun Gil, So Jeong Park, Jin Young Kim, Miyoung Kim   +3 more
wiley   +1 more source

Fault tolerance against amplitude-damping noise using Bacon-Shor codes

Physical Review Research
Designing efficient fault-tolerance schemes is crucial for building useful quantum computers. Most standard schemes assume no knowledge of the underlying device noise and rely on general-purpose quantum error-correcting (QEC) codes capable of handling ...
Long D. H. My, Akshaya Jayashankar, Prabha Mandayam, Hui Khoon Ng   +3 more
doaj   +1 more source

SPICE‐Compatible Compact Modeling of Cuprate‐Based Memristors Across a Wide Temperature Range

Advanced Electronic Materials, EarlyView.
A physics‐guided compact model for YBCO memristors is introduced, incorporating carrier trapping, field‐induced detrapping, and a differential balance equation to describe their switching dynamics. The model is compared with experiments and implemented in LTspice, allowing realistic circuit‐level simulations.
Thomas Günkel, Aleix Barrera, Lluís Balcells, Narcís Mestres, Enrique Miranda, Anna Palau, Jordi Suñé   +6 more
wiley   +1 more source

Effects of Doping, Disorder, and Compensation on Electron Conduction in Si‐Doped k‐Ga2O3 Close to the Metal‐to‐Insulator Transition

Advanced Electronic Materials, EarlyView.
Self‐compensation effects attributed to doping‐dependent shift‐defects at APBs Validation of Hall data for VRH transport near the MIT Persistence of VRH transport for any SiH4 flow in Si‐doped κ‐Ga2O3 due to high compensation coupling transport and EPR data as strategy to study electronic properties and doping T‐dependence of transport data agrees with
Antonella Parisini, Hans Jürgen von Bardeleben, Ildicò Cora, Zsolt Fogarassy, Paola Alippi, Vincenzo Fiorentini, Ian Vickridge, Piero Mazzolini, Alessio Bosio, Roberto Fornari   +9 more
wiley   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Machine learning discovers new champion codes

npj Artificial Intelligence
Linear error-correcting codes form the mathematical backbone of modern digital communication and storage systems, but identifying champion linear codes (linear codes achieving or exceeding the best known minimum Hamming distance) remains challenging.
Yang-Hui He, Alexander M. Kasprzyk, Q Le, Dmitrii Riabchenko   +3 more
doaj   +1 more source

Limitations of Foundation Models in Energy Materials Simulations: A Case Study in Polyanion Sodium Cathode Materials

Advanced Intelligent Discovery, EarlyView.
Several simulation techniques are used to explore static and dynamic behavior in polyanion sodium cathode materials. The study reveals that universal machine learning interatomic potentials (MLIPs) struggle with system‐specific chemistry, emphasizing the need for tailored datasets.
Martin Hoffmann Petersen, Chiku Parida, Jonas Busk, Jin Hyun Chang, Arghya Bhowmik, Juan Maria Garcia Lastra   +5 more
wiley   +1 more source

Toward Capacitive In‐Memory‐Computing: A Device to Systems Level Perspective on the Future of Artificial Intelligence Hardware

Advanced Intelligent Discovery, EarlyView.
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj, Ella Paasio, Sayani Majumdar   +2 more
wiley   +1 more source

Quantum error-correcting codes

, 2022
Quantum computing is a new and exciting field of research that, using the properties of quantum mechanics, has the potential to be a disruptive technology, being able to per form certain computations faster than any classical computer, such as Shor’s factorization algorithm and Grover’s algorithm.
openaire   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source