Results 221 to 230 of about 40,499 (317)

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

RC-LDPC-Polar Codes for Information Reconciliation in Continuous-Variable Quantum Key Distribution. [PDF]

Entropy (Basel)
Hua F, Chen K, Deng W, Cheng J, Guo B, Xie H.   +5 more
europepmc   +1 more source

A [3]Rotaxane Containing {Ti7Ga} Rings Linking CuII: Synthesis, Structure, and Spectroscopic Studies

Chemistry – A European Journal, EarlyView.
Extended hybrid inorganic‐organic [2]‐ and [3]‐rotaxanes are reported based on heterometallic rings with threads that link CuII complexes; the crystal structures are reported, and the solution behavior is investigated by double electron electron resonance spectroscopy methods.
Selena J. Lockyer, Lubomir Loci, George F. S. Whitehead, Inigo J. Vitorica‐Yrezabal, Grigore A. Timco, Alice M. Bowen, Eric J. L. McInnes, Richard E. P. Winpenny   +7 more
wiley   +1 more source

An Efficient Algorithmic Way to Construct Boltzmann Machine Representations for Arbitrary Stabilizer Code. [PDF]

Entropy (Basel)
Zhang YH, Jia Z, Wu YC, Guo GC.
europepmc   +1 more source

A High Energy Barrier DyIII2 Single‐Molecule Magnet Supported by a Bulky, Anionic N–O Bridging Ligand

Chemistry – A European Journal, EarlyView.
Complex [Dy2(hynad)2(dbm)4] was prepared by the employment of the anionic N–O‐based bridging and chelating N‐hydroxy‐1,8‐naphthalimide ligand, featuring a {Dy2(µ‐OR)2}4+ core and exhibiting zero‐field SMM behavior with a Ueff value of 171 K and double‐S‐shaped hysteresis loops, open up to 3.5 K.
Alexandros S. Armenis, Concepción Molina‐Jirón, Konstantinos N. Pantelis, Wolfgang Wernsdorfer, Mario Ruben, Eufemio Moreno‐Pineda, Theocharis C. Stamatatos   +6 more
wiley   +1 more source

An integrated optical hardware for realization of quantum error correction operators. [PDF]

Sci Rep
Armaghani S, Rostami A.
europepmc   +1 more source

Fault-tolerant hyperbolic Floquet quantum error correcting codes [PDF]

Ali Fahimniya, Hossein Dehghani, Kishor Bharti, Sheryl Mathew, Alicia J. Kollár, Alexey V. Gorshkov, Michael J. Gullans   +6 more
openalex   +1 more source

The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?

Chemistry – A European Journal, EarlyView.
Machine‐learning potentials are increasingly taking on the exploratory tasks of homogeneous catalysis, enabling rapid conformer sampling and reaction‐space mapping. However, when selectivity depends on subtle electronic effects, electronic‐structure methods remain essential.
Maxime Ferrer, Julen Munarriz, Thijs Stuyver, Ruben Laplaza   +3 more
wiley   +1 more source

Attention to quantum complexity. [PDF]

Sci Adv
Kim H, Zhou Y, Xu Y, Varma K, Karamlou AH, Rosen IT, Hoke JC, Wan C, Zhou JP, Oliver WD, Lensky YD, Weinberger KQ, Kim EA.   +12 more
europepmc   +1 more source

Neural Belief-Propagation Decoders for Quantum Error-Correcting Codes

, 2018
Ye-Hua Liu, Poulin, David
openalex   +1 more source