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Found 9 papers, 5 papers with code
Wyckoff Transformer: Generation of Symmetric Crystals
Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization behavior, and mechanical strength.
Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning
Generative artificial intelligence (GenAI) and large language models (LLMs) have simultaneously opened new avenues for enhancing human learning and increased the prevalence of poor-quality information in student response - termed Botpoop.
Constructing Custom Thermodynamics Using Deep Learning
Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate.
Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers
For the subset of highly conductive samples, we employed a regression model to predict their conductivities, yielding an impressive test R2 value of 0. 984.
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems.
Efficacious symmetry-adapted atomic displacement method for lattice dynamical studies
Small displacement methods have been successfully used to calculate the lattice dynamical properties of crystals.
Materials Science
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties.
Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials
We introduce the use of Crystal Graph Convolutional Neural Networks (CGCNN), Fully Connected Neural Networks (FCNN) and XGBoost to predict thermoelectric properties.
Graph Convolutional Neural Networks for Polymers Property Prediction
A fast and accurate predictive tool for polymer properties is demanding and will pave the way to iterative inverse design.
