Results 91 to 100 of about 19,877 (264)

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Access to Finance and Innovation in the Canadian Food Processing

Agribusiness, EarlyView.
ABSTRACT Innovation is a presumed channel through which finance affects productivity, yet there is limited research testing the relationship between finance and innovation in the food manufacturing sector. The purpose of the paper is to explore the determinants (e.g., financing, R&D, firm size, expenditure on innovation) of the adoption of innovation ...
Getu Hailu, Deepananda Herath
wiley   +1 more source

Quantitative assessment of the universal thermopower in the Hubbard model. [PDF]

Nat Commun, 2023
Wang WO, Ding JK, Huang EW, Moritz B, Devereaux TP.   +4 more
europepmc   +1 more source

Limitations of Foundation Models in Energy Materials Simulations: A Case Study in Polyanion Sodium Cathode Materials

Advanced Intelligent Discovery, EarlyView.
Several simulation techniques are used to explore static and dynamic behavior in polyanion sodium cathode materials. The study reveals that universal machine learning interatomic potentials (MLIPs) struggle with system‐specific chemistry, emphasizing the need for tailored datasets.
Martin Hoffmann Petersen, Chiku Parida, Jonas Busk, Jin Hyun Chang, Arghya Bhowmik, Juan Maria Garcia Lastra   +5 more
wiley   +1 more source

The Hubbard Model: Basic Ideas and Some Results. [PDF]

ACS Omega, 2023
Celebonovic V.
europepmc   +1 more source

Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination

Advanced Intelligent Discovery, EarlyView.
In this work, the Doubao large language model (LLM) is involved in the formula derivation processes for Hubbard U determination regarding the second‐order perturbations of the chemical potential. The core ML tool is optimized for physical domain knowledge, which is not limited to parameter prediction but rather serves as an interactive physical theory ...
Mingzi Sun, Tong Wu, Baian Chen, Zikang Li, Qiuyang Lu, Lu Lu, Hon Ho Wong, Cheuk Hei Chan, Bolong Huang   +8 more
wiley   +1 more source

A simple case of d(x2-y2) pairing: Hubbard ladder

EPJ Web of Conferences, 2012
We study the strength and the temperature scale of the d(x2-y2) pairing correlations in the Hubbard model on a ladder lattice using Quantum Monte Carlo (QMC) simulations.
Bulut N.
doaj   +1 more source

Long distance entanglement and high-dimensional quantum teleportation in the Fermi-Hubbard model. [PDF]

Sci Rep, 2023
Abaach S, Mzaouali Z, El Baz M.
europepmc   +1 more source

The Interoperability Challenge in DFT Workflows Across Implementations

Advanced Intelligent Discovery, EarlyView.
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen, Tushar Singh Thakur, Manuel Dillenz, Johan M. Carlsson, Celso R. C. Rêgo, Eibar Flores, Hamidreza Hajiyani, Felix Hanke, Juan María García Lastra, Wolfgang Wenzel, Nicola Marzari, Tejs Vegge, Giovanni Pizzi, Ivano E. Castelli   +13 more
wiley   +1 more source

Tunable quantum criticalities in an isospin extended Hubbard model simulator. [PDF]

Nature, 2022
Li Q, Cheng B, Chen M, Xie B, Xie Y, Wang P, Chen F, Liu Z, Watanabe K, Taniguchi T, Liang SJ, Wang D, Wang C, Wang QH, Liu J, Miao F.   +15 more
europepmc   +1 more source