Advanced Functional Materials, EarlyView.Tabular foundation model interrogates the synthetic likelihood of metal−organic frameworks. Abstract Metal–organic frameworks (MOFs) are celebrated for their chemical and structural versatility, and in‑silico screening has significantly accelerated their discovery; yet most hypothetical MOFs (hMOFs) never reach the bench because their synthetic ...
Xiaoyu Wu +3 more
wiley +1 more source
Distributed Identity Authentication with Lenstra-Lenstra-Lovász Algorithm-Ciphertext Policy Attribute-Based Encryption from Lattices: An Efficient Approach Based on Ring Learning with Errors Problem. [PDF]
Entropy (Basel)Yuan Q +6 more
europepmc +1 more source
Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning [PDF]
A. He, Ziwen Liu
openalex +1 more source
Integrative Approaches for DNA Sequence‐Controlled Functional Materials
Advanced Functional Materials, EarlyView.DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo +4 more
wiley +1 more source
Why segmentation matters: Experience-driven segmentation errors impair “morpheme” learning.
, 2015 Amy S. Finn, Carla L. Hudson Kam
openalex +1 more source
The Rescorla-Wagner model, prediction error, and fear learning
, 2023 Gavan P. McNally, Joanna Oi-Yue Yau
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Advanced Functional Materials, EarlyView.This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
Learning Dynamic User Behavior Based on Error-driven Event Representation
, 2021 Honglian Wang +4 more
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Intrinsic neuronal properties represent song and error in zebra finch vocal learning [PDF]
, 2020 Arij Daou, Daniel Margoliash
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Electron–Matter Interactions During Electron Beam Nanopatterning
Advanced Functional Materials, EarlyView.This article reviews the electron–matter interactions important to nanopatterning with electron beam lithography (EBL). Electron–matter interactions, including secondary electron generation routes, polymer radiolysis, and electron beam induced charging, are discussed.
Camila Faccini de Lima +2 more
wiley +1 more source