Research Topic
density functional
20 publications exploring this topic
2025
AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed Across Seven Main Group Elements
(2025)
The AIQM series methods are successful neural network-based models that target coupled-cluster accuracy while maintaining high robustness and transferability across various tasks by leveraging Δ-learning.
2024
AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs
(2024)
Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.
AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs
(2024)
Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.
2023
AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs
(2023)
Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.
2021
Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
Advanced Intelligent Systems, 3 (2021)
The bandgap is one of the most fundamental properties of condensed matter.
Machine learned Hückel theory: Interfacing physics and deep neural networks
The Journal of Chemical Physics, 154 (2021)
The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials.
Teaching a neural network to attach and detach electrons from molecules
Nature Communications, 12 (2021)
Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations.
Teaching a Neural Network to Attach and Detach Electrons from Molecules
(2021)
Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry.
2020
High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications
Advanced Theory and Simulations, 3 (2020)
AbstractThe screening of novel materials is an important topic in the field of materials science.
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
Scientific Data, 7 (2020)
Abstract Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
(2020)
Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
(2020)
Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
Teaching a Neural Network to Attach and Detach Electrons from Molecules
(2020)
Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry.
2019
The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
(2019)
Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.
2018
Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches
Advanced Theory and Simulations, 2 (2018)
Abstract There are now, in principle, a limitless number of hybrid van der Waals (vdW) heterostructures that can be built from the rapidly growing number of 2D layers.
Efficient prediction of structural and electronic properties of hybrid 2D materials using complementary DFT and machine learning approaches
(2018)
There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers.
Efficient prediction of structural and electronic properties of hybrid 2D materials using complementary DFT and machine learning approaches
(2018)
There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers.
2011
Toward robust computational electrochemical predicting the environmental fate of organic pollutants
Journal of Computational Chemistry, 32, 2195–2203 (2011)
AbstractA number of density functionals was utilized for the calculation of electron attachment free energy for nitrocompounds, quinones and azacyclic compounds.
2007
Theoretical calculations: Can Gibbs free energy for intermolecular complexes be predicted efficiently and accurately?
Journal of Computational Chemistry, 28, 1598–1609 (2007)
AbstractThe theoretical study has been performed to refine the procedure for calculations of Gibbs free energy with a relative accuracy of less than 1 kcal/mol.
2004
Modeling the Gas-Phase Reduction of Nitrobenzene to Nitrosobenzene by Iron Monoxide: A Density Functional Theory Study
The Journal of Physical Chemistry A, 108, 4878–4886 (2004)
Modeling the Gas-Phase Reduction of Nitrobenzene to Nitrosobenzene by Iron Monoxide: A Density Functional Theory Study.