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Efficient search of compositional space for hybrid organic-inorganic perovskites via Bayesian optimization

npj Computational Materials 2018年第1期4卷 232-238页

作者： Henry C.Herbol Weici Hu peter frazier Paulette Clancy Matthias Poloczek Department of Materials Science and Engineering Cornell UniversityIthacaNYUSA School of Operations Research and Information Engineering Cornell UniversityIthacaNYUSA Robert F.Smith School of Chemical and Biomolecular Engineering Cornell UniversityIthacaNYUSA Department of Systems and Industrial Engineering University of ArizonaTucsonAZUSA

Accelerated searches,made possible by machine learning techniques,are of growing interest in materials discovery.A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as hybrid organic–inorganic perovskites(HOIPs).The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large“compositional”space(at times,exceeding 500,000 possible combinations).Selecting a HOIP with desirable characteristics involves choosing different cations,halides,and solvent blends from a diverse palette of *** unguided search by experimental investigations or molecular simulations is prohibitively *** this work,we propose a Bayesian optimization method that uses an application-specific kernel to overcome challenges where data is scarce,and in which the search space is given by binary variables indicating whether a constituent is present or *** demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time(less than 10%)needed to complete an exhaustive *** find an optimal composition within 15±10 iterations in a HOIP compositional space containing 72 combinations,and within 31±9 iterations when considering mixed halides(240 combinations).Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization *** paper demonstrates the potential of the Bayesian optimization methodology reported here for new materials discovery.

关键词： optimization inorganic perovskite

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On the Use of Adjoint-Based Sensitivity Estimates to Control Local Mesh Refinement

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Communications in Computational Physics 2010年第3期7卷 631-638页

作者： Joseph D.frazier peter K.Jimack Robert M.Kirby School of Computing University of UtahSalt Lake CityUTUSA School of Computing University of LeedsLeedsLS29JTUK

The goal of efficient and robust error control, through local mesh adaptationin the computational solution of partial differential equations, is predicated on theability to identify in an a posteriori way those localized regions whose refinement willlead to the most significant reductions in the error. The development of a posteriori errorestimation schemes and of a refinement infrastructure both facilitate this goal, howeverthey are incomplete in the sense that they do not provide an answer as to where themaximal impact of refinement may be gained or what type of refinement — elementalpartitioning (h-refinement) or polynomial enrichment (p-refinement) — will best leadto that gain. In essence, one also requires knowledge of the sensitivity of the error toboth the location and the type of refinement. In this communication we propose theuse of adjoint-based sensitivity analysis to discriminate both where and how to *** present both an adjoint-based and an algebraic perspective on defining and usingsensitivities, and then demonstrate through several one-dimensional model problemexperiments the feasibility and benefits of our approach.

关键词： Adaptivity error estimation hp-finite elements sensitivity discrete adjoint

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