Universal QM/MM approaches for general nanoscale applications
Quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address
chemical phenomena in complex molecular environments. Whereas this modeling approach …
chemical phenomena in complex molecular environments. Whereas this modeling approach …
End-to-end differentiable construction of molecular mechanics force fields
Molecular mechanics (MM) potentials have long been a workhorse of computational
chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety …
chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety …
Recent advances in density functional theory approach for optoelectronics properties of graphene
Graphene has received tremendous attention among diverse 2D materials because of its
remarkable properties. Its emergence over the last two decades gave a new and distinct …
remarkable properties. Its emergence over the last two decades gave a new and distinct …
Uncertainty quantification in molecular simulations with dropout neural network potentials
Abstract Machine learning interatomic potentials (IPs) can provide accuracy close to that of
first-principles methods, such as density functional theory (DFT), at a fraction of the …
first-principles methods, such as density functional theory (DFT), at a fraction of the …
Configuration-sampling-based surrogate models for rapid parameterization of non-bonded interactions
In this study, we present an approach for rapid force field parameterization and uncertainty
quantification of the non-bonded interaction parameters for classical force fields. The …
quantification of the non-bonded interaction parameters for classical force fields. The …
Hybrid machine-learning-assisted quantification of the compound internal and external uncertainties of graphene: towards inclusive analysis and design
Molecular dynamics (MD) simulations have emerged to be a vital tool for the analysis of
nanoscale materials like graphene. However, the reliability of the results derived from MD …
nanoscale materials like graphene. However, the reliability of the results derived from MD …
[HTML][HTML] Bayesian, frequentist, and information geometric approaches to parametric uncertainty quantification of classical empirical interatomic potentials
Y Kurniawan, CL Petrie, KJ Williams… - The Journal of …, 2022 - pubs.aip.org
In this paper, we consider the problem of quantifying parametric uncertainty in classical
empirical interatomic potentials (IPs) using both Bayesian (Markov Chain Monte Carlo) and …
empirical interatomic potentials (IPs) using both Bayesian (Markov Chain Monte Carlo) and …
Towards quantitative prediction of ignition-delay-time sensitivity on fuel-to-air equivalence ratio
Several compression-ignition and low-temperature combustion strategies require a fuel
where the ignition-delay-time (IDT) is highly sensitive to the fuel-to-air equivalence ratio (ϕ) …
where the ignition-delay-time (IDT) is highly sensitive to the fuel-to-air equivalence ratio (ϕ) …
End-to-end differentiable molecular mechanics force field construction
Molecular mechanics (MM) potentials have long been a workhorse of computational
chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety …
chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety …
Uncertainty quantification in materials modeling
Y Wang, DL McDowell - Uncertainty Quantification in Multiscale Materials …, 2020 - Elsevier
Abstract Integrated Computational Materials Engineering (ICME) has been widely
articulated and pursued over the past decade. Recognition of the need to ensure reliability …
articulated and pursued over the past decade. Recognition of the need to ensure reliability …