TY - JOUR
T1 - Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction
AU - Amsler, Maximilian
AU - Ward, Logan
AU - Hegde, Vinay I.
AU - Goesten, Maarten G.
AU - Yi, Xia
AU - Wolverton, Chris
N1 - Funding Information:
We thank R. Hoffmann for valuable expert discussions. M.A. (DFT calculations) acknowledges support from the Novartis Universitat Basel Excellence Scholarship for Life Sciences and the Swiss National Science Foundation (project Nos. P300P2-158407 and P300P2-174475). M.G.G. (COOP calculations) acknowledges support from the Rubicon Research Programme (project 019.161BT.031), which is (partly) financed by the Netherlands Organization for Scientific Research (NWO). L.W. (ML model), and C.W. acknowledge the financial assistance Award 70NANB14H012 from the US Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). V.I.H (OQMD calculations) acknowledges support from the National Science Foundation Materials Research Science and Engineering Center (MRSEC) of Northwestern University (DMR-1720139). The computational resources from the Swiss National Supercomputing Center in Lugano (projects s621, s700, and s861), the Extreme Science and Engineering Discovery Environment (XSEDE) (which is supported by National Science Foundation grant No. OCI-1053575), the Bridges system at the Pittsburgh Supercomputing Center (PSC) (which is supported by NSF award No. ACI-1445606), the Quest high performance computing facility at Northwestern University, and the National Energy Research Scientific Computing Center (DOE: DE-AC02-05CH11231), are gratefully acknowledged.
Funding Information:
We thank R. Hoffmann for valuable expert discussions. M.A. (DFT calculations) acknowledges support from the Novartis Universität Basel Excellence Scholarship for Life Sciences and the Swiss National Science Foundation (project Nos. P300P2-158407 and P300P2-174475). M.G.G. (COOP calculations) acknowledges support from the Rubicon Research Programme (project 019.161BT.031), which is (partly) financed by the Netherlands Organization for Scientific Research (NWO). L.W. (ML model), and C.W. acknowledge the financial assistance Award 70NANB14H012 from the US Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). V.I.H (OQMD calculations) acknowledges support from the National Science Foundation Materials Research Science and Engineering Center (MRSEC) of Northwestern University (DMR-1720139). The computational resources from the Swiss National Supercomputing Center in Lugano (projects s621, s700, and s861), the Extreme Science and Engineering Discovery Environment (XSEDE) (which is supported by National Science Foundation grant No. OCI-1053575), the Bridges system at the Pittsburgh Supercomputing Center (PSC) (which is supported by NSF award No. ACI-1445606), the Quest high performance computing facility at Northwestern University, and the National Energy Research Scientific Computing Center (DOE: DE-AC02-05CH11231), are gratefully acknowledged.
Publisher Copyright:
© 2019 American Physical Society.
PY - 2019/3/26
Y1 - 2019/3/26
N2 - We report the computational investigation of a series of ternary X4Y2Z and X5Y2Z2 compounds with X=Mg, Ca, Sr, Ba; Y=P, As, Sb, Bi; and Z=S, Se, Te. The compositions for these materials were predicted through a search guided by machine learning, while the structures were resolved using the minima hopping crystal structure prediction method. Based on ab initio calculations, we predict that many of these phases are thermodynamically stable. In particular, 21 of the X4Y2Z compounds crystallize in a tetragonal structure with I42d symmetry, and exhibit band gaps in the range of 0.8 and 1.8 eV, well suited for various energy applications. We show that several candidates (in particular X4Y2Te and X4Sb2Se) exhibit good photo absorption in the visible range, while others (e.g., Ba4Sb2Se) show excellent thermoelectric performance due to high power factors and extremely low lattice thermal conductivities.
AB - We report the computational investigation of a series of ternary X4Y2Z and X5Y2Z2 compounds with X=Mg, Ca, Sr, Ba; Y=P, As, Sb, Bi; and Z=S, Se, Te. The compositions for these materials were predicted through a search guided by machine learning, while the structures were resolved using the minima hopping crystal structure prediction method. Based on ab initio calculations, we predict that many of these phases are thermodynamically stable. In particular, 21 of the X4Y2Z compounds crystallize in a tetragonal structure with I42d symmetry, and exhibit band gaps in the range of 0.8 and 1.8 eV, well suited for various energy applications. We show that several candidates (in particular X4Y2Te and X4Sb2Se) exhibit good photo absorption in the visible range, while others (e.g., Ba4Sb2Se) show excellent thermoelectric performance due to high power factors and extremely low lattice thermal conductivities.
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U2 - 10.1103/PhysRevMaterials.3.035404
DO - 10.1103/PhysRevMaterials.3.035404
M3 - Article
AN - SCOPUS:85064166426
SN - 2475-9953
VL - 3
JO - Physical Review Materials
JF - Physical Review Materials
IS - 3
M1 - 035404
ER -