Results 71 to 80 of about 115,388 (320)

High‐Throughput Screening and Characterization of Non‐Flammable Na‐Cl Solid Electrolytes

Advanced Electronic Materials, EarlyView.
A Na‐Cl solid electrolyte with high ionic conductivity is screened from a structural database using force‐field molecular dynamics (MD) simulations and density functional theory (DFT)‐MD calculations. Na3La5Cl18 is identified, synthesized, and characterized.
Naoto Tanibata, Naoki Nonaka, Daisuke Urushihara, Hayami Takeda, Ryo Kobayashi, Naoaki Kuwata, Masanobu Nakayama   +6 more
wiley   +1 more source

Degradation Mechanism of Phosphate‐Based Li‐NASICON Conductors in Alkaline Environment

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
The presence of water in the cathode of a Li‐air battery shifts reactions to produce LiOH, creating a corrosive, alkaline environment. This study investigates the alkaline stability of the common Li‐NASICON solid‐state conductor chemistries through a systematic experimental study combined with computational modeling to understand the degradation ...
Benjamin X. Lam, Zhuohan Li, Tara P. Mishra, Gerbrand Ceder   +3 more
wiley   +1 more source

Deephullnet: a deep learning approach for solving the convex hull and concave hull problems with transformer

International Journal of Digital Earth
Convex and concave hulls originating from computational geometry are widely applied in practice. For instance, to determine the boundaries of a geographical area within a group of cities, convex hulls can represent the approximate boundaries of the areas.
Haojian Liang, Shaohua Wang, Song Gao, Huilai Li, Cheng Su, Hao Lu, Xueyan Zhang, Xi Chen, Yinan Chen   +8 more
doaj   +1 more source

Design for flexibility: An adjustable robust optimization approach with decision‐dependent uncertainty

AIChE Journal, EarlyView.
ABSTRACT Flexibility is a crucial characteristic of industrial systems that face increasing volatilities and is therefore essential to ensure feasible operation under uncertainty. Flexibility is often closely tied to the design of a system, and careful consideration must be taken to understand the trade‐off between design cost and operational ...
Jnana Sai Jagana, Sreekanth Rajagopalan, Satyajith Amaran, Qi Zhang   +3 more
wiley   +1 more source

Optimizing convex hull discovery: Introducing a quintuple-region algorithm with enhanced computational efficiency

Engineering Science and Technology, an International Journal
This paper introduces a novel algorithm for computing the convex hull of a finite set of points in two-dimensional space. Unlike traditional methods, this algorithm strategically partitions the input set into five distinct regions, isolating interior ...
Fidan Nuriyeva, Hakan Kutucu
doaj   +1 more source

A Fast and Robust Support Vector Machine With Anti-Noise Convex Hull and its Application in Large-Scale ncRNA Data Classification

IEEE Access, 2019
Support vector machine (SVM) achieves successful classification performance with the application in non-coding RNA (ncRNA) data. With the rapid increase of the species and sizes of ncRNA sequences, several fast SVM methods based on data distribution and ...
Xiaoqing Gu, Tongguang Ni, Yiqing Fan
doaj   +1 more source

Approximate Convex Hulls: sketching the convex hull using curvature

, 2017
Convex hulls are fundamental objects in computational geometry. In moderate dimensions or for large numbers of vertices, computing the convex hull can be impractical due to the computational complexity of convex hull algorithms. In this article we approximate the convex hull in using a scalable algorithm which finds high curvature vertices with high ...
Graham, Robert, Oberman, Adam M.
openaire   +2 more sources

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Estimating the probability that a given vector is in the convex hull of a random sample [PDF]

, 2023
Satoshi Hayakawa, Terry Lyons, Harald Oberhauser   +2 more
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source