Add to ORKGComputational materials discovery efforts are enabled by large databases of properties derived from high-throughput density functional theory (DFT), which now contain millions of calculations at the generalized gradient approximation (GGA) level of theory. It is now feasible to carry out high-throughput calculations using more accurate methods, such as meta-GGA DFT; however recomputing an entire database with a higher-fidelity method would not effectively leverage the enormous investment of computational resources embodied in existing (GGA) calculations. Instead, we propose here a general procedure by which higher-fidelity, low-coverage calculations (e.g., meta-GGA calculations for selected chemical systems) can be combined with lower-fidel...
Many technologically important material properties directly relate to their electronic structure. Co...
Since the inception of computational chemistry, its practitioners have imagined the ability to predi...
We present the GMTKN55 benchmark database for general main group thermochemistry, kinetics and nonco...
Computational materials discovery efforts are enabled by large databases of properties derived from ...
Computational materials discovery efforts utilize hundreds or thousands of density functional theory...
a b s t r a c t The use of high-throughput density functional theory (DFT) calculations to screen fo...
Standard approximations to the density functional theory exchange-correlation functional have been e...
Machine learning has emerged as a novel tool for the efficient prediction of material properties, an...
The search for new materials based on computational screening relies on methods that accurately pred...
This report documents research carried out by the author throughout his 3-years Truman fellowship. T...
The evaluation of reaction energies between solids using density functional theory (DFT) is of pract...
The question of material stability is of fundamental importance to any analysis of system properties...
Generalized gradient approximations (GGA’s) seek to improve upon the accuracy of the local-spin-dens...
In the past decade we have witnessed the appearance of large databases of calculated material proper...
In this work, we demonstrate a method to quantify uncertainty in corrections to density functional t...
Many technologically important material properties directly relate to their electronic structure. Co...
Since the inception of computational chemistry, its practitioners have imagined the ability to predi...
We present the GMTKN55 benchmark database for general main group thermochemistry, kinetics and nonco...
Computational materials discovery efforts are enabled by large databases of properties derived from ...
Computational materials discovery efforts utilize hundreds or thousands of density functional theory...
a b s t r a c t The use of high-throughput density functional theory (DFT) calculations to screen fo...
Standard approximations to the density functional theory exchange-correlation functional have been e...
Machine learning has emerged as a novel tool for the efficient prediction of material properties, an...
The search for new materials based on computational screening relies on methods that accurately pred...
This report documents research carried out by the author throughout his 3-years Truman fellowship. T...
The evaluation of reaction energies between solids using density functional theory (DFT) is of pract...
The question of material stability is of fundamental importance to any analysis of system properties...
Generalized gradient approximations (GGA’s) seek to improve upon the accuracy of the local-spin-dens...
In the past decade we have witnessed the appearance of large databases of calculated material proper...
In this work, we demonstrate a method to quantify uncertainty in corrections to density functional t...
Many technologically important material properties directly relate to their electronic structure. Co...
Since the inception of computational chemistry, its practitioners have imagined the ability to predi...
We present the GMTKN55 benchmark database for general main group thermochemistry, kinetics and nonco...