Advanced Intelligent Discovery, EarlyView.The behaviors of semiflexible polymers such as DNA and protein are often reshaped by coupled interactions. Monte Carlo simulations assist in studying these systems. This work recasts the traditional chain‐growth strategy into a new framework: a fixed number of chains grow synchronously, while less relevant chains to the target system are removed and ...
Yihan Zhao, Jizeng Wang
wiley +1 more source
Detecting Polarized Side-Scattering Signals in Media with Ultra-Low-Scattering Coefficients: An Improved Monte Carlo Simulation Approach. [PDF]
Sensors (Basel)Shan C, He L, Jin B, Wu Z, Yi S.
europepmc +1 more source
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 +2 more
wiley +1 more source
Assessing food procurement greenhouse gas emissions and food waste in UK fine dining.
Clean Food SystZick A, Schmidt R X, Reynolds C.
europepmc +1 more source
Lessons From Deep Eutectic Solvents to Design High Entropy Electrolytes for Electrochemical Energy Storage. [PDF]
ChemSusChemZhang W +8 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo +3 more
wiley +1 more source
Integrating preliminary test and Stein-type techniques to improve estimation in the time-dependent Cox model. [PDF]
PLoS OneRamezani R, Rabiei MR, Arashi M.
europepmc +1 more source
MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design
Advanced Intelligent Discovery, EarlyView.MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa +2 more
wiley +1 more source
Modeling of In Vivo Electrochemical Noise: A Computational Framework to Optimize the Corrosion Monitoring of Biodegradable Magnesium Implants. [PDF]
J Funct BiomaterMakrinsky K, Klyuev A, Batishchev O.
europepmc +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source