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Machine-learned potentials for next-generation matter simulations | Nature Materials
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Computational methods to simulate molten salt thermophysical properties | Communications Chemistry
A Chemical Toolbox for Labeling and Degrading Engineered Cas Proteins | JACS Au
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A comprehensive study on the effect of carbonization temperature on the physical and chemical properties of carbon fibers | Scientific Reports
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Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset | Journal of Chemical Information and Modeling
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Catalyze Materials Science with Machine Learning | ACS Materials Letters
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Quantitative Prediction of Vertical Ionization Potentials from DFT via a Graph-Network-Based Delta Machine Learning Model Incorporating Electronic Descriptors | The Journal of Physical Chemistry A
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Genes | Free Full-Text | Applications of CRISPR-Cas Technologies to Proteomics
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