28 research outputs found
Exceeding the Shockley-Queisser limit within the detailed balance framework
The Shockley-Queisser limit is one of the most fundamental results in the
field of photovoltaics. Based on the principle of detailed balance, it defines
an upper limit for a single junction solar cell that uses an absorber material
with a specific band gap. Although methods exist that allow a solar cell to
exceed the Shockley-Queisser limit, here we show that it is possible to exceed
the Shockley-Queisser limit without considering any of these additions. Merely
by introducing an absorptivity that does not assume that every photon with an
energy above the band gap is absorbed, efficiencies above the Shockley-Queisser
limit are obtained. This is related to the fact that assuming optimal
absorption properties also maximizes the recombination current within the
detailed balance approach. We conclude that considering a finite thickness for
the absorber layer allows the efficiency to exceed the Shockley-Queisser limit,
and that this is more likely to occur for materials with small band gaps.Comment: 6 pages, 3 figure
Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods
Solar-energy plays an important role in solving serious environmental
problems and meeting high-energy demand. However, the lack of suitable
materials hinders further progress of this technology. Here, we present the
largest inorganic solar-cell material search to date using density functional
theory (DFT) and machine-learning approaches. We calculated the spectroscopic
limited maximum efficiency (SLME) using Tran-Blaha modified Becke-Johnson
potential for 5097 non-metallic materials and identified 1997 candidates with
an SLME higher than 10%, including 934 candidates with suitable convex-hull
stability and effective carrier mass. Screening for 2D-layered cases, we found
58 potential materials and performed G0W0 calculations on a subset to estimate
the prediction-uncertainty. As the above DFT methods are still computationally
expensive, we developed a high accuracy machine learning model to pre-screen
efficient materials and applied it to over a million materials. Our results
provide a general framework and universal strategy for the design of
high-efficiency solar cell materials. The data and tools are publicly
distributed at: https://www.ctcms.nist.gov/~knc6/JVASP.html,
https://www.ctcms.nist.gov/jarvisml/, https://jarvis.nist.gov/ and
https://github.com/usnistgov/jarvis
How to verify the precision of density-functional-theory implementations via reproducible and universal workflows
In the past decades many density-functional theory methods and codes adopting
periodic boundary conditions have been developed and are now extensively used
in condensed matter physics and materials science research. Only in 2016,
however, their precision (i.e., to which extent properties computed with
different codes agree among each other) was systematically assessed on
elemental crystals: a first crucial step to evaluate the reliability of such
computations. We discuss here general recommendations for verification studies
aiming at further testing precision and transferability of
density-functional-theory computational approaches and codes. We illustrate
such recommendations using a greatly expanded protocol covering the whole
periodic table from Z=1 to 96 and characterizing 10 prototypical cubic
compounds for each element: 4 unaries and 6 oxides, spanning a wide range of
coordination numbers and oxidation states. The primary outcome is a reference
dataset of 960 equations of state cross-checked between two all-electron codes,
then used to verify and improve nine pseudopotential-based approaches. Such
effort is facilitated by deploying AiiDA common workflows that perform
automatic input parameter selection, provide identical input/output interfaces
across codes, and ensure full reproducibility. Finally, we discuss the extent
to which the current results for total energies can be reused for different
goals (e.g., obtaining formation energies).Comment: Main text: 23 pages, 4 figures. Supplementary: 68 page
Common workflows for computing material properties using different quantum engines
The prediction of material properties based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use them. We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification. We introduce design rules for reusable, code-agnostic, workflow interfaces to compute well-defined material properties, which we implement for eleven quantum engines and use to compute various material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer’s expertise of the quantum engine directly available to non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.This work is supported by the MARVEL National Centre of Competence in Research (NCCR) funded by the Swiss National Science Foundation (grant agreement ID 51NF40-182892) and by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 824143 (European MaX Centre of Excellence “Materials design at the Exascale”) and Grant Agreement No. 814487 (INTERSECT project). We thank M. Giantomassi and J.-M. Beuken for their contributions in adding support for PseudoDojo tables to the aiida-pseudo (https://github.com/aiidateam/aiida-pseudo) plugin. We also thank X. Gonze, M. Giantomassi, M. Probert, C. Pickard, P. Hasnip, J. Hutter, M. Iannuzzi, D. Wortmann, S. Blügel, J. Hess, F. Neese, and P. Delugas for providing useful feedback on the various quantum engine implementations. S.P. acknowledges support from the European Unions Horizon 2020 Research and Innovation Programme, under the Marie Skłodowska-Curie Grant Agreement SELPH2D No. 839217 and computer time provided by the PRACE-21 resources MareNostrum at BSC-CNS. E.F.-L. acknowledges the support of the Norwegian Research Council (project number 262339) and computational resources provided by Sigma2. P.Z.-P. thanks to the Faraday Institution CATMAT project (EP/S003053/1, FIRG016) for financial support. KE acknowledges the Swiss National Science Foundation (grant number 200020-182015). G.Pi. and K.E. acknowledge the swissuniversities “Materials Cloud” (project number 201-003). Work at ICMAB is supported by the Severo Ochoa Centers of Excellence Program (MICINN CEX2019-000917-S), by PGC2018-096955-B-C44 (MCIU/AEI/FEDER, UE), and by GenCat 2017SGR1506. B.Z. thanks to the Faraday Institution FutureCat project (EP/S003053/1, FIRG017) for financial support. J.B. and V.T. acknowledge support by the Joint Lab Virtual Materials Design (JLVMD) of the Forschungszentrum Jülich.Peer reviewe