Results 181 to 190 of about 5,008 (215)
We present CatTransVAE, a catalyst‐specialized chemical language model (CLM) built on a transformer variational autoencoder (VAE), developed through pretraining on general compounds followed by fine‐tuning on diverse catalyst databases. A template‐guided generation framework is introduced to enable controlled catalyst design under structural ...
Apakorn Kengkanna, Masahito Ohue
wiley +1 more source
Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation
Predicting a crystal’s structure from composition alone is a long‐standing challenge in materials discovery. The CSP180 benchmark of 180 inorganic crystals evaluates thirteen crystal structure prediction algorithms requiring no density functional theory (DFT) against DFT‐based baselines across twelve metrics.
Lai Wei +9 more
wiley +1 more source
Machine‐learning models predict myopia trajectories from 12 routine ocular variables in 40 445 children across 10 hospitals. Externally validated across school‐based screening and optical‐intervention settings, the best models achieve AUROC 0.97 for myopia status and R2 0.83 for spherical equivalent, enabling personalized risk stratification to curb ...
Yitong Zhang +14 more
wiley +1 more source
Particle Swarm Optimization as a Noise‐Tolerant Strategy for High‐Entropy Alloy Research
A customized particle swarm optimization algorithm is benchmarked against batch Bayesian optimization across four high‐entropy alloy oxygen‐reduction landscapes. While both methods locate the global optimum comparably under noise‐free conditions, Bayesian optimization’s surrogate model tends to overfit noisy data.
Ahmad Tirmidzi +2 more
wiley +1 more source
Data scarcity is a critical bottleneck for machine learning in applied chemistry. We show that physics‐informed descriptors enable a simple linear model to predict the deblocking temperatures of capped diisocyanates to within 11 °C from just 19 measurements, demonstrating that embedding physical insight into descriptors, rather than relying on ...
Remsha Rafiq +9 more
wiley +1 more source
SuperResNET is a powerful integrated software that reconstructs network architecture and molecular distribution of subcellular structures from single molecule localization microscopy datasets. SuperResNET segments the nuclear pore complex and corners, extracts size, shape, and network features of all segmented nuclear pores and uses modularity analysis
Yahongyang Lydia Li +6 more
wiley +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
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
A skin‐conformal wearable device based on laser‐induced graphene is developed for continuous strain measurement across the circumference of the forearm for gesture recognition and hand‐tracking applications. Post material optimization, the strain sensor array is integrated with a wearable wireless readout circuit for real‐time control of a robotic arm,
Vinay Kammarchedu +2 more
wiley +1 more source
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Communications of the ACM, 1995
This brief tutorial on Bayesian networks serves to introduce readers to some of the concepts, terminology, and notation employed by articles in this special section. In a Bayesian network, a variable takes on values from a collection of mutually exclusive and collective exhaustive states.
David Heckerman, Michael P. Wellman
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This brief tutorial on Bayesian networks serves to introduce readers to some of the concepts, terminology, and notation employed by articles in this special section. In a Bayesian network, a variable takes on values from a collection of mutually exclusive and collective exhaustive states.
David Heckerman, Michael P. Wellman
openaire +1 more source
Neurocomputing, 2010
What are Bayesian networks and why are their applications growing across all fields?
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What are Bayesian networks and why are their applications growing across all fields?
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