Machine-learning-revealed reaction statistics via 3D spectroimaging for copper sulfidation of adhesive layers in rubber/brass composite [PDF]
, 2023 H. Matsui +8 more
openalex +1 more source
Quantum Emitters in Hexagonal Boron Nitride: Principles, Engineering and Applications
Advanced Functional Materials, EarlyView.Quantum emitters in hexagonal boron nitride have emerged as a promising candidate for quantum information science. This review examines the fundamentals of these quantum emitters, including their level structures, defect engineering, and their possible chemical structures.
Thi Ngoc Anh Mai +8 more
wiley +1 more source
Flow‐Induced Vascular Remodeling on‐Chip: Implications for Anti‐VEGF Therapy
Advanced Functional Materials, EarlyView.Flow‐induced vascular remodeling plays a critical role in network stabilization and function. Using a vasculature‐on‐chip system, this study reveals how physiological VEGF levels and flow affect vascular remodeling and provides insights into tumor vessel normalization.
Fatemeh Mirzapour‐Shafiyi +6 more
wiley +1 more source
A special issue on: Bayesian statistics and machine learning in business [PDF]
, 2016 Hongxia Yang
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Machine Learning-Statistics Ensemble Battery EOL Prediction Model [PDF]
, 2023 Brian Benjamin Hansen, M Snyder
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Machine Learning: Deepest Learning as Statistical Data Assimilation Problems
Neural Computation, 2018 We formulate an equivalence between machine learning and the formulation of statistical data assimilation as used widely in physical and biological sciences. The correspondence is that layer number in a feedforward artificial network setting is the analog of time in the data assimilation setting.
Paul J. Rozdeba +2 more
openaire +4 more sources
Machine Learning‐Enabled Polymer Discovery for Enhanced Pulmonary siRNA Delivery
Advanced Functional Materials, EarlyView.This study provides an efficient approach to train a machine learning model by merging heterogeneous literature data to predict suitable polymers for siRNA delivery. Without the need for extensive laboratory synthesis, the machine learning enabled a virtual screening and successfully predicted a polymer that is validated for effective gene silencing in
Felix Sieber‐Schäfer +10 more
wiley +1 more source
Machine Learning Guided Design of Nerve‐On‐A‐Chip Platforms with Promoted Neurite Outgrowth
Advanced Functional Materials, EarlyView.Compared to labor‐intensive trial‐and‐error experimentation, a machine learning (ML)‐guided workflow, incorporating cell viability assays, data augmentation, ensemble modeling, and model interpretation, is developed to accelerate nerve‐on‐a‐chip optimization and uncover data‐driven design principles.
Tsai‐Chun Chung +8 more
wiley +1 more source
Sampling Algorithms in Statistical Physics: A Guide for Statistics and Machine Learning
Statistical ScienceWe discuss several algorithms for sampling from unnormalized probability distributions in statistical physics, but using the language of statistics and machine learning. We provide a self-contained introduction to some key ideas and concepts of the field, before discussing three well-known problems: phase transitions in the Ising model, the melting ...
Faulkner, Michael F. +1 more
openaire +5 more sources
Advanced Functional Materials, EarlyView.By integrating machine learning into flux‐regulated crystallization (FRC), accurate prediction of solvent evaporation rates in real time, improving crystallization control and reducing crystal growth variability by over threefold, is achieved. This enhances the reproducibility and quality of perovskite single crystals, leading to reproducible ...
Tatiane Pretto +8 more
wiley +1 more source