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In this work, machine learning models were successfully developed to capture the relationship between composition and dielectric properties of strontium-containing dielectrics using different ...
Machine learning helps improve accuracy and efficiency of small-molecule calculations Microsoft researchers used deep learning to create new DFT model by Sam Lemonick, special to C&EN June 20, 2025 ...
The prediction of molecular properties remains a challenging task in the field of drug design and development. Recently, there has been a growing interest in the analysis of biological images.
The machine learning algorithms were used to predict or forecast outcomes based on the experimental data. During the machine learning process, a model is trained using historical data to enable ...
Machine learning–driven approaches for predicting T-cell–mediated immunity and beyond.. If you have the appropriate software installed, you can download article citation data to the citation manager ...
Electronic health records (EHR)-based machine learning (ML) approach to predict risk of progression to metastatic melanoma after initial diagnosis.. If you have the appropriate software installed, you ...
The next generation of force fields for molecular dynamics will be developed using a wealth of data. Training systematically with experimental data remains a challenge, however, especially for machine ...
Matthew Versaggi ’87 (left) looks on as Alfred University students David Gonzalez (center) and Jane Tkachenko discuss their entry in a poster session held Thursday during AI Week at Alfred. Versaggi ...
ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...