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Proton conducting neuromorphic materials and devices
Neuromorphic computing and artificial intelligence hardware generally aims to emulate
features found in biological neural circuit components and to enable the development of …
features found in biological neural circuit components and to enable the development of …
Thermal transport in 2D materials
In recent decades, two-dimensional materials (2D) such as graphene, black and blue
phosphorenes, transition metal dichalcogenides (eg, WS2 and MoS2), and h-BN have …
phosphorenes, transition metal dichalcogenides (eg, WS2 and MoS2), and h-BN have …
Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence
Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …
Machine learning force field parameters from ab initio data
Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to
determine a polarizable force field parameters using only ab initio data from quantum …
determine a polarizable force field parameters using only ab initio data from quantum …
Machine learning classical interatomic potentials for molecular dynamics from first-principles training data
The ever-increasing power of modern supercomputers, along with the availability of highly
scalable atomistic simulation codes, has begun to revolutionize predictive modeling of …
scalable atomistic simulation codes, has begun to revolutionize predictive modeling of …
Ab Initio-Based Bond Order Potential to Investigate Low Thermal Conductivity of Stanene Nanostructures
We introduce a bond order potential (BOP) for stanene based on an ab initio derived training
data set. The potential is optimized to accurately describe the energetics, as well as thermal …
data set. The potential is optimized to accurately describe the energetics, as well as thermal …
Phosphine-Stabilized Hidden Ground States in Gold Clusters Investigated via a Aun(PH3)m Database
Nanoclusters are promising materials for catalysis and sensing due to their large surface
areas and unique electronic structures which can be tailored through composition …
areas and unique electronic structures which can be tailored through composition …
Active learning a neural network model for gold clusters & bulk from sparse first principles training data
Small metal clusters are of fundamental scientific interest and of tremendous significance in
catalysis. These nanoscale clusters display diverse geometries and structural motifs …
catalysis. These nanoscale clusters display diverse geometries and structural motifs …
Atomistic simulations of the elastic compression of platinum nanoparticles
The elastic behavior of nanoparticles depends strongly on particle shape, size, and
crystallographic orientation. Many prior investigations have characterized the elastic …
crystallographic orientation. Many prior investigations have characterized the elastic …
Direct atomic-level insight into oxygen reduction reaction on size-dependent Pt-based electrocatalysts from density functional theory calculations
Develo** novel oxygen reduction reaction (ORR) catalysts with high activity is urgent for
proton exchange membrane fuel cells. Herein, we investigated a group of size-dependent Pt …
proton exchange membrane fuel cells. Herein, we investigated a group of size-dependent Pt …