Proton conducting neuromorphic materials and devices

Y Yuan, RK Patel, S Banik, TB Reta, RS Bisht… - Chemical …, 2024 - ACS Publications
Neuromorphic computing and artificial intelligence hardware generally aims to emulate
features found in biological neural circuit components and to enable the development of …

Thermal transport in 2D materials

MH Kalantari, X Zhang - Nanomaterials, 2022 - mdpi.com
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 …

Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence

N Baker, F Alexander, T Bremer, A Hagberg… - 2019 - osti.gov
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 …

Machine learning force field parameters from ab initio data

Y Li, H Li, FC Pickard IV, B Narayanan… - Journal of chemical …, 2017 - ACS Publications
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 …

Machine learning classical interatomic potentials for molecular dynamics from first-principles training data

H Chan, B Narayanan, MJ Cherukara… - The Journal of …, 2019 - ACS Publications
The ever-increasing power of modern supercomputers, along with the availability of highly
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

MJ Cherukara, B Narayanan, A Kinaci… - The journal of …, 2016 - ACS Publications
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 …

Phosphine-Stabilized Hidden Ground States in Gold Clusters Investigated via a Aun(PH3)m Database

CA McCandler, JC Dahl, KA Persson - ACS nano, 2022 - ACS Publications
Nanoclusters are promising materials for catalysis and sensing due to their large surface
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

TD Loeffler, S Manna, TK Patra, H Chan… - …, 2020 - Wiley Online Library
Small metal clusters are of fundamental scientific interest and of tremendous significance in
catalysis. These nanoscale clusters display diverse geometries and structural motifs …

Atomistic simulations of the elastic compression of platinum nanoparticles

IM Padilla Espinosa, TDB Jacobs, A Martini - Nanoscale research letters, 2022 - Springer
The elastic behavior of nanoparticles depends strongly on particle shape, size, and
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

F Qian, L Peng, Y Zhuang, L Liu, Q Chen - Chinese Journal of Chemical …, 2023 - Elsevier
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 …