Results 211 to 220 of about 1,813,277 (247)

A Breadth‐First Pruned‐Enriched Rosenbluth Method for Force–Extension Simulations of Confined Semiflexible Chains

open access: yesAdvanced 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

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

open access: yesAdvanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi   +3 more
wiley   +1 more source

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

open access: yesAdvanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha   +2 more
wiley   +1 more source

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

open access: yesAdvanced 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

Diamond on Kurepa trees. [PDF]

open access: yesMon Hefte Math
Kostana Z, Rinot A, Shelah S.
europepmc   +1 more source

A Critical Assessment of Bonding Descriptors for Predicting Materials Properties

open access: yesAdvanced Intelligent Discovery, EarlyView.
The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik   +6 more
wiley   +1 more source

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