AIMC Topic: Density Functional Theory

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DFT-assisted machine learning for polyester membrane design in textile wastewater recovery applications.

Water research Jul 1, 2025
Resource recovery from textile wastewater has attracted increasing interest because it simultaneously addresses wastewater treatment and maximizes the utilization of the residual dyes. Although polyester membranes have demonstrated great potential fo...
Machine Learning Wastewater Water Purification Waste Disposal, Fluid Water Pollutants, Chemical Density Functional Theory Coloring Agents Polyesters Membranes, Artificial Textiles
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Predicting Oxidation Potentials with DFT-Driven Machine Learning.

Journal of chemical information and modeling Jun 9, 2025
We introduce OxPot, a comprehensive open-access data set comprising over 15 thousand chemically diverse organic molecules. Leveraging the precision of DFT-derived highest occupied molecular orbital energies (), OxPot serves as a robust platform for a...
Machine Learning Oxidation-Reduction Density Functional Theory
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Band Gap and Reorganization Energy Prediction of Conducting Polymers by the Integration of Machine Learning and Density Functional Theory.

Journal of chemical information and modeling Jun 9, 2025
The performance and reliability of machine learning (ML)-quantitative structure-property relationship (QSPR) models depend on the quality, size, and diversity of the data set used for model training. In this study, we manually curated a large-scale d...
Machine Learning Density Functional Theory Polymers Quantitative Structure-Activity Relationship Electric Conductivity
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Atomic Energy Accuracy of Neural Network Potentials: Harnessing Pretraining and Transfer Learning.

Journal of chemical information and modeling May 26, 2025
Machine learning-based interatomic potentials (MLIPs) have transformed the prediction of potential energy surfaces (PESs), achieving accuracy comparable to calculations. However, atomic energy predictions, often assumed to lack physical meaning, rem...
Neural Networks, Computer Machine Learning Density Functional Theory Thermodynamics
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Machine learned calibrations to high-throughput molecular excited state calculations.

The Journal of chemical physics Apr 7, 2022
Understanding the excited state properties of molecules provides insight into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to inc...
Machine Learning Density Functional Theory High-Throughput Screening Assays Calibration Databases, Chemical
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