Results 121 to 130 of about 12,488,537 (258)

Mesoscale Simulations of Blood Coagulation in Flow and Quiescent Domains Using SDPD. [PDF]

Int J Numer Method Biomed Eng
Ferrero ME, Moreno N, Ellero M.
europepmc   +1 more source

Regularized reproducing kernel particle method

Computational Particle Mechanics
Huy Anh Nguyen, Satoyuki Tanaka
openaire   +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

Statistics of Marginal Wave Functions as a Real-Space Diagnostic of Quantum Entanglement. [PDF]

ACS Omega
Christov IP.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Data-driven Mori-Zwanzig modeling of Lagrangian particle dynamics in turbulent flows. [PDF]

Proc Natl Acad Sci U S A
de Wit XM, Gabbana A, Woodward M, Lin YT, Toschi F, Livescu D.   +5 more
europepmc   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Machine learning enabled molecular dynamics-Monte Carlo framework for nanoconfined fluid adsorption. [PDF]

Commun Chem
Liu J, Chen G, He S, Guan R, Gong L, Yang Y, Zhang T, Sun S.   +7 more
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Linear Residual Network Modeling for Anti-HIV-1 Activity Prediction and Docking-Validated Design of Biphenyl-DAPY-Based NNRTIs. [PDF]

Molecules
Wang H, Zhang Y, Wang A, Zhang P.
europepmc   +1 more source