Results 91 to 100 of about 5,723 (211)

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

open access: yesAdvanced 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   +8 more
wiley   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

open access: yesAdvanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Water‐Mediated Phosphoryl Wires Stabilize Pathological Tau Fibrils

open access: yesAngewandte Chemie, EarlyView.
Extended 1D phosphoryl “wires” stabilize in‐register amyloid tau fibrils, as demonstrated by multiple‐quantum spin‐counting NMR, TEM, and MD simulations, using fibrils of tau peptide jR2R3‐P301L (tau295–313) with phosphorylation at S305 or Y310. ABSTRACT Hyperphosphorylation of tau is a hallmark of tauopathies, with specific phosphorylation sites ...
Lokeswara Rao Potnuru   +8 more
wiley   +2 more sources

“It Is Much Safer to Be Sparse than Connected”: Safe Control of Robotic Swarm Density Dynamics with PDE Optimization with State Constraints

open access: yesAdvanced Intelligent Systems, EarlyView.
This paper proposes a novel control framework to ensure safety of a robotic swarm. A feedback optimization controller is capable of driving the swarm toward a target density while keeping risk‐zone exposure below a safety threshold. Theory and experiments show how safety is more effectively achieved for sparsely connected swarms.
Longchen Niu, Gennaro Notomista
wiley   +1 more source

On the Ability of Bismuth to Couple Weakly Coordinating Anions

open access: yesAngewandte Chemie, EarlyView.
Motivated by the growing number of catalytic processes based on high‐valent Bi, we investigated in silico the reductive elimination step responsible for the coupling of weakly coordinating anions. Relativistic contributions appeared to be decisive to the electronic description of the intermediates involved in this process.
Lucas Mele   +3 more
wiley   +2 more sources

Predicting Crystal Structures and Ionic Conductivities in Li3YCl6−xBrx Halide Solid Electrolytes Using a Fine‐Tuned Machine Learning Interatomic Potential

open access: yesAdvanced Intelligent Systems, EarlyView.
This study refines the Crystal Hamiltonian Graph Network to predict energies, structures, and lithium‐ion dynamics in halide electrolytes. By generating ordered structural models and using an iterative fine‐tuning workflow, we achieve near‐ab initio accuracy for phase stability and ionic transport predictions.
Jonas Böhm, Aurélie Champagne
wiley   +1 more source

Iterative Synthesis of Pyrene–Coronene Molecular Graphene Nanoribbons

open access: yesAngewandte Chemie, EarlyView.
An iterative approach delivers a new family of undoped, cove‐edged graphene nanoribbons with lengths up to 9.1 nm that exhibit both molar absorptivity and fluorescence brightness values on the order of 105 M−1 cm−1 and an intrinsic charge‐carrier mobility of 475 ± 32 cm2 V−1 s−1.
Miguel A. Medel   +6 more
wiley   +2 more sources

A path integral formula of quantum gravity emergent from entangled local structures

open access: yesJournal of High Energy Physics
We couple to group field theory (GFT) a scalar field that encodes the entanglement between manifold sites. The scalar field provides a relational clock that enables the derivation of the Hamiltonian of the system from the GFT action.
Jinglong Liu   +3 more
doaj   +1 more source

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

open access: yesAdvanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez   +4 more
wiley   +1 more source

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