Showing 1–5 of 5 results for author: Rasch, K

The AFLOW Fleet for Materials Discovery

Authors: Cormac Toher, Corey Oses, David Hicks, Eric Gossett, Frisco Rose, Pinku Nath, Demet Usanmaz, Denise C. Ford, Eric Perim, Camilo E. Calderon, Jose J. Plata, Yoav Lederer, Michal Jahnátek, Wahyu Setyawan, Shidong Wang, Junkai Xue, Kevin Rasch, Roman V. Chepulskii, Richard H. Taylor, Geena Gomez, Harvey Shi, Andrew R. Supka, Rabih Al Rahal Al Orabi, Priya Gopal, Frank T. Cerasoli , et al. (26 additional authors not shown)

Abstract: The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community… ▽ More The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community of users. AFLOW incorporates different computational modules to robustly determine thermodynamic stability, electronic band structures, vibrational dispersions, thermo-mechanical properties and more. The AFLOW data repository is publicly accessible online at aflow.org, with more than 1.7 million materials entries and a panoply of queryable computed properties. Tools to programmatically search and process the data, as well as to perform online machine learning predictions, are also available. △ Less

Submitted 1 December, 2017; originally announced December 2017.

Comments: 14 pages, 8 figures

Fixed-Node Diffusion Monte Carlo of Lithium Systems

Authors: Kevin Rasch, Lubos Mitas

Abstract: We study lithium systems over a range of number of atoms, e.g., atomic anion, dimer, metallic cluster, and body-centered cubic crystal by the diffusion Monte Carlo method. The calculations include both core and valence electrons in order to avoid any possible impact by pseudo potentials. The focus of the study is the fixed-node errors, and for that purpose we test several orbital sets in order to… ▽ More We study lithium systems over a range of number of atoms, e.g., atomic anion, dimer, metallic cluster, and body-centered cubic crystal by the diffusion Monte Carlo method. The calculations include both core and valence electrons in order to avoid any possible impact by pseudo potentials. The focus of the study is the fixed-node errors, and for that purpose we test several orbital sets in order to provide the most accurate nodal hyper surfaces. We compare our results to other high accuracy calculations wherever available and to experimental results so as to quantify the the fixed-node errors. The results for these Li systems show that fixed-node quantum Monte Carlo achieves remarkably high accuracy total energies and recovers 97-99 % of the correlation energy. △ Less

Submitted 25 February, 2015; originally announced February 2015.

Comments: 7 pages, 4 figures

Journal ref: Phys. Rev. B 92, 045122 (2015)

Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints

Authors: Olexandr Isayev, Denis Fourches, Eugene N. Muratov, Corey Oses, Kevin Rasch, Alexander Tropscha, Stefano Curtarolo

Abstract: As the proliferation of high-throughput approaches in materials science is increasing the wealth of data in the field, the gap between accumulated-information and derived-knowledge widens. We address the issue of scientific discovery in materials databases by introducing novel analytical approaches based on structural and electronic materials fingerprints. The framework is employed to (i) query la… ▽ More As the proliferation of high-throughput approaches in materials science is increasing the wealth of data in the field, the gap between accumulated-information and derived-knowledge widens. We address the issue of scientific discovery in materials databases by introducing novel analytical approaches based on structural and electronic materials fingerprints. The framework is employed to (i) query large databases of materials using similarity concepts, (ii) map the connectivity of the materials space (i.e., as a materials cartogram) for rapidly identifying regions with unique organizations/properties, and (iii) develop predictive Quantitative Materials Structure-Property Relation- ships (QMSPR) models for guiding materials design. In this study, we test these fingerprints by seeking target material properties. As a quantitative example, we model the critical temperatures of known superconductors. Our novel materials fingerprinting and materials cartography approaches contribute to the emerging field of materials informatics by enabling effective computational tools to analyze, visualize, model, and design new materials. △ Less

Submitted 16 December, 2014; v1 submitted 9 December, 2014; originally announced December 2014.

Comments: 13 pages and 5 figures, Chem. Mater., 2015

Fixed-node errors in quantum Monte Carlo: interplay of electron density and node nonlinearities

Authors: Kevin M. Rasch, Shuming Hu, Lubos Mitas

Abstract: We elucidate the origin of large differences (two-fold or more) in the fixed-node errors between the first- vs second-row systems for single-configuration trial wave functions in quantum Monte Carlo calculations. This significant difference in the fixed-node biases is studied across a set of atoms, molecules, and also Si, C solid crystals. The analysis is done over valence isoelectronic systems th… ▽ More We elucidate the origin of large differences (two-fold or more) in the fixed-node errors between the first- vs second-row systems for single-configuration trial wave functions in quantum Monte Carlo calculations. This significant difference in the fixed-node biases is studied across a set of atoms, molecules, and also Si, C solid crystals. The analysis is done over valence isoelectronic systems that share similar correlation energies, bond patterns, geometries, ground states, and symmetries. We show that the key features which affect the fixed-node errors are the differences in electron density and the degree of node nonlinearity. The findings reveal how the accuracy of the quantum Monte Carlo varies across a variety of systems, provide new perspectives on the origins of the fixed-node biases in electronic structure calculations of molecular and condensed systems, and carry implications for pseudopotential constructions for heavy elements △ Less

Submitted 29 October, 2013; v1 submitted 8 October, 2013; originally announced October 2013.

Many-body nodal hypersurface and domain averages for correlated wave functions

Authors: Shuming Hu, Kevin Rasch, Lubos Mitas

Abstract: We outline the basic notions of nodal hypersurface and domain averages for antisymmetric wave functions. We illustrate their properties and analyze the results for a few electron explicitly solvable cases and discuss possible further developments. We outline the basic notions of nodal hypersurface and domain averages for antisymmetric wave functions. We illustrate their properties and analyze the results for a few electron explicitly solvable cases and discuss possible further developments. △ Less

Submitted 21 July, 2013; originally announced July 2013.

Journal ref: ACS Symposium Series, Volume 1094, pp 77-87, (2012)