Results 161 to 170 of about 15,805 (306)

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

Structures and Solvation Energies Effects Versus Temperature. An MP2 Investigations in the Framework of Cluster Model. [PDF]

J Comput Chem
Hattab A, Malloum A, Dhaouadi Z, Russo N.   +3 more
europepmc   +1 more source

Comparison of Bond Strength of Porcelain to Metal in Nobel, Base Metals (with and without) Beryllium with SEM/EDS Analysis

, 2007
Sh Memaran, Omid Savabi, Farahnaz Nejatidanesh, MH Fathi   +3 more
openalex   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Toward the Observation of Dimagnesocene. [PDF]

ACS Omega
Briggs CG, Goodlett SM, Schaefer HF.
europepmc   +1 more source

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

Advanced 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, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

First-Order Symmetry-Adapted Perturbation Theory with Double Exchange for Multireference Systems. [PDF]

J Chem Theory Comput
Cieśliński D, Przybytek M, Chałasiński G, Hapka M.   +3 more
europepmc   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

Physically interpretable descriptors drive the materials design of metal hydrides for hydrogen storage. [PDF]

Chem Sci
Jang SH, Zhang D, Tran HB, Jia X, Konno K, Sato R, Orimo SI, Li H.   +7 more
europepmc   +1 more source

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

Advanced 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