| 1. |
- Aquilante, Francesco, et al.
(författare)
-
Molcas 8 : New capabilities for multiconfigurational quantum chemical calculations across the periodic table
- 2016
-
Ingår i: Journal of Computational Chemistry. - : Wiley. - 0192-8651 .- 1096-987X. ; 37:5, s. 506-541
-
Tidskriftsartikel (refereegranskat)abstract
- In this report, we summarize and describe the recent unique updates and additions to the Molcas quantum chemistry program suite as contained in release version 8. These updates include natural and spin orbitals for studies of magnetic properties, local and linear scaling methods for the Douglas-Kroll-Hess transformation, the generalized active space concept in MCSCF methods, a combination of multiconfigurational wave functions with density functional theory in the MC-PDFT method, additional methods for computation of magnetic properties, methods for diabatization, analytical gradients of state average complete active space SCF in association with density fitting, methods for constrained fragment optimization, large-scale parallel multireference configuration interaction including analytic gradients via the interface to the Columbus package, and approximations of the CASPT2 method to be used for computations of large systems. In addition, the report includes the description of a computational machinery for nonlinear optical spectroscopy through an interface to the QM/MM package Cobramm. Further, a module to run molecular dynamics simulations is added, two surface hopping algorithms are included to enable nonadiabatic calculations, and the DQ method for diabatization is added. Finally, we report on the subject of improvements with respects to alternative file options and parallelization.
|
|
| 2. |
- Galván, Ignacio Fdez., et al.
(författare)
-
OpenMolcas : From Source Code to Insight
- 2019
-
Ingår i: Journal of Chemical Theory and Computation. - : American Chemical Society (ACS). - 1549-9618 .- 1549-9626. ; 15:11, s. 5925-5964
-
Tidskriftsartikel (refereegranskat)abstract
- In this Article we describe the OpenMolcas environment and invite the computational chemistry community to collaborate. The open-source project already includes a large number of new developments realized during the transition from the commercial MOLCAS product to the open-source platform. The paper initially describes the technical details of the new software development platform. This is followed by brief presentations of many new methods, implementations, and features of the OpenMolcas program suite. These developments include novel wave function methods such as stochastic complete active space self-consistent field, density matrix renormalization group (DMRG) methods, and hybrid multiconfigurational wave function and density functional theory models. Some of these implementations include an array of additional options and functionalities. The paper proceeds and describes developments related to explorations of potential energy surfaces. Here we present methods for the optimization of conical intersections, the simulation of adiabatic and nonadiabatic molecular dynamics, and interfaces to tools for semiclassical and quantum mechanical nuclear dynamics. Furthermore, the Article describes features unique to simulations of spectroscopic and magnetic phenomena such as the exact semiclassical description of the interaction between light and matter, various X-ray processes, magnetic circular dichroism, and properties. Finally, the paper describes a number of built-in and add-on features to support the OpenMolcas platform with postcalculation analysis and visualization, a multiscale simulation option using frozen-density embedding theory, and new electronic and muonic basis sets.
|
|
| 3. |
- Hamed, Tareq Abu, et al.
(författare)
-
Multiscale in modelling and validation for solar photovoltaics
- 2018
-
Ingår i: EPJ Photovoltaics. - : EDP Sciences. - 2105-0716. ; 9
-
Tidskriftsartikel (refereegranskat)abstract
- Photovoltaics is amongst the most important technologies for renewable energy sources, and plays a key role in the development of a society with a smaller environmental footprint. Key parameters for solar cells are their energy conversion efficiency, their operating lifetime, and the cost of the energy obtained from a photovoltaic system compared to other sources. The optimization of these aspects involves the exploitation of new materials and development of novel solar cell concepts and designs. Both theoretical modeling and characterization of such devices require a comprehensive view including all scales from the atomic to the macroscopic and industrial scale. The different length scales of the electronic and optical degrees of freedoms specifically lead to an intrinsic need for multiscale simulation, which is accentuated in many advanced photovoltaics concepts including nanostructured regions. Therefore, multiscale modeling has found particular interest in the photovoltaics community, as a tool to advance the field beyond its current limits. In this article, we review the field of multiscale techniques applied to photovoltaics, and we discuss opportunities and remaining challenges.
|
|
| 4. |
- Michaels, Hannes, et al.
(författare)
-
Dye-sensitized solar cells under ambient light powering machine learning : towards autonomous smart sensors for the internet of things
- 2020
-
Ingår i: Chemical Science. - : ROYAL SOC CHEMISTRY. - 2041-6520 .- 2041-6539. ; 11:11, s. 2895-2906
-
Tidskriftsartikel (refereegranskat)abstract
- The field of photovoltaics gives the opportunity to make our buildings "smart'' and our portable devices "independent", provided effective energy sources can be developed for use in ambient indoor conditions. To address this important issue, ambient light photovoltaic cells were developed to power autonomous Internet of Things (IoT) devices, capable of machine learning, allowing the on-device implementation of artificial intelligence. Through a novel co-sensitization strategy, we tailored dye-sensitized photovoltaic cells based on a copper(II/I) electrolyte for the generation of power under ambient lighting with an unprecedented conversion efficiency (34%, 103 mu W cm(-2) at 1000 lux; 32.7%, 50 mu W cm(-2) at 500 lux and 31.4%, 19 mu W cm(-2) at 200 lux from a fluorescent lamp). A small array of DSCs with a joint active area of 16 cm(2) was then used to power machine learning on wireless nodes. The collection of 0.947 mJ or 2.72 x 10(15) photons is needed to compute one inference of a pre-trained artificial neural network for MNIST image classification in the employed set up. The inference accuracy of the network exceeded 90% for standard test images and 80% using camera-acquired printed MNIST-digits. Quantization of the neural network significantly reduced memory requirements with a less than 0.1% loss in accuracy compared to a full-precision network, making machine learning inferences on low-power microcontrollers possible. 152 J or 4.41 x 10(20) photons required for training and verification of an artificial neural network were harvested with 64 cm(2) photovoltaic area in less than 24 hours under 1000 lux illumination. Ambient light harvesters provide a new generation of self-powered and "smart" IoT devices powered through an energy source that is largely untapped.
|
|
| 5. |
|
|
