Results 61 to 70 of about 21,815 (240)
Accelerated Discovery of High Performance Ni3S4/Ni3Mo HER Catalysts via Bayesian Optimization
Advanced Functional Materials, EarlyView.Integrated workflow accelerates the catalyst discovery of hydrogen evolution reaction via Bayesian optimization. An experiment‐trained surrogate model proposes synthesis conditions, guiding iterative refinement using electrochemical performance metrics.
Namuersaihan Namuersaihan +9 more
wiley +1 more source
Machine Learning and Knowledge ExtractionQuantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome.
Maria Revythi, Georgia Koukiou
doaj +1 more source
On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms
Big Data Mining and Analytics, 2020 This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms.
Farid Ablayev +5 more
doaj +1 more source
Clinical data classification with noisy intermediate scale quantum computers
Scientific Reports, 2022 Quantum machine learning has experienced significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers.
S. Moradi +7 more
doaj +1 more source
Frontier Advances of Emerging High‐Entropy Anodes in Alkali Metal‐Ion Batteries
Advanced Functional Materials, EarlyView.Recent advances in microscopic morphology control of high‐entropy anode materials for alkali metal‐ion batteries. Abstract With the growing demand for sustainable energy, portable energy storage systems have become increasingly critical. Among them, the development of rechargeable batteries is primarily driven by breakthroughs in electrode materials ...
Liang Du +14 more
wiley +1 more source
, 2016 [EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others ...
Maria Schuld, Francesco Petruccione
openaire +3 more sources
QDataSet, quantum datasets for machine learning
Scientific Data, 2022 AbstractThe availability of large-scale datasets on which to train, benchmark and test algorithms has been central to the rapid development of machine learning as a discipline. Despite considerable advancements, the field of quantum machine learning has thus far lacked a set of comprehensive large-scale datasets upon which to benchmark the development ...
Elija Perrier +2 more
openaire +4 more sources
Artificial Intelligence as the Next Visionary in Liquid Crystal Research
Advanced Functional Materials, EarlyView.The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam +2 more
wiley +1 more source
On a quantum inspired approach to train machine learning models
Applied AI Letters, 2023 In this work, a novel technique to train machine learning models is introduced, which is based on digital simulations of certain types of quantum systems.
Jean Michel Sellier
doaj +1 more source
, 2008 We propose a novel notion of a quantum learning machine for automatically controlling quantum coherence and for developing quantum algorithms. A quantum learning machine can be trained to learn a certain task with no a priori knowledge on its algorithm.
Bang, Jeongho +3 more
openaire +2 more sources