AIMC Topic: Density Functional Theory

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A Battery-Like Self-Selecting Biomemristor from Earth-Abundant Natural Biomaterials.

ACS applied bio materials Feb 2, 2021
Using the earth-abundant natural biomaterials to manufacture functional electronic devices meets the sustainable requirement of green electronics, especially for the practical application of memristors in data storage and neuromorphic computing. Howe...
Biocompatible Materials Electric Power Supplies Density Functional Theory Anthocyanins Electronics Materials Testing Humans Neural Networks, Computer
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Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters.

ACS combinatorial science Nov 4, 2020
Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for ...
Machine Learning Density Functional Theory Copper Cobalt Nanoparticles Adsorption Hydrogen Thermodynamics
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Machine Learning Prediction of Electronic Coupling between the Guanine Bases of DNA.

The journal of physical chemistry. A Sep 14, 2020
Charge transport in deoxyribonucleic acid (DNA) is of immense interest in biology and molecular electronics. Electronic coupling between the DNA bases is an important parameter describing the efficiency of charge transport in DNA. A reasonable estima...
Guanine Molecular Dynamics Simulation Electronics Machine Learning DNA Density Functional Theory
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Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens.

Journal of chemical theory and computation Jun 29, 2020
Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of ...
Halogens Sulfur Thermodynamics Density Functional Theory Molecular Dynamics Simulation Deep Learning
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Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals.

Journal of chemical information and modeling Apr 6, 2020
Computational high throughput screening (HTS) has emerged as a significant tool in material science to accelerate the discovery of new materials with target properties in recent years. However, despite many successful cases in which HTS led to the no...
Machine Learning Density Functional Theory Uncertainty High-Throughput Screening Assays Neural Networks, Computer
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Graph Convolutional Neural Networks as "General-Purpose" Property Predictors: The Universality and Limits of Applicability.

Journal of chemical information and modeling Jan 3, 2020
Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one of which is the choice of chemical structure representation. The classical approach ...
Neural Networks, Computer Machine Learning Algorithms Structure-Activity Relationship Crystallization Density Functional Theory Models, Molecular
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Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.

Journal of chemical information and modeling Nov 5, 2019
The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. How...
Alloys Thermodynamics Computer Simulation Surface Properties Models, Chemical Models, Molecular Density Functional Theory Neural Networks, Computer Titanium Crystallization Gold
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A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning.

Journal of chemical information and modeling Aug 8, 2019
Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FF are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize ...
Molecular Conformation Neural Networks, Computer Density Functional Theory Models, Molecular
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Efficient Corrections for DFT Noncovalent Interactions Based on Ensemble Learning Models.

Journal of chemical information and modeling Apr 8, 2019
Machine learning has exhibited powerful capabilities in many areas. However, machine learning models are mostly database dependent, requiring a new model if the database changes. Therefore, a universal model is highly desired to accommodate the wides...
Linear Models Computational Chemistry Machine Learning Databases, Chemical Density Functional Theory
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Generating a vast chemical space for high polar surface area triphenylamine polymers by machine learning-DFT calculations assisted reverse engineering for photovoltaics.

Journal of molecular graphics & modelling Sep 1, 2025
The total polar surface area (TPSA) is a crucial parameter in photovoltaic (PV) materials, as it directly influences their solubility, processability, and device performance. This study leverages machine learning-assisted reverse engineering to gener...
Machine Learning Algorithms Polymers Density Functional Theory Aniline Compounds Models, Molecular Solar Energy
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