Distinguishing electronic contributions of surface and sub-surface transition metal atoms in Ti-based MXenes

Autor: Yang, Y, Hantanasirisakul, K, Frey, NC, Anasori, B, Green, RJ, Rogge, PC, Waluyo, I, Hunt, A, Shafer, P, Arenholz, E, Shenoy, VB, Gogotsi, Y, May, SJ

Publikováno v: 2D Materials, vol 7, iss 2

MXenes are a rapidly-expanding family of 2D transition metal carbides and nitrides that have attracted attention due to their excellent performance in applications ranging from energy storage to electromagnetic interference shielding. Numerous other

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::c876a2cfea01c62e1c678c49ffe0cc56
https://escholarship.org/uc/item/7107m6gq

Efficient catalyst screening using graph neural networks to predict strain effects on adsorption energy.

Autor: Price CC; Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA., Singh A; Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA., Frey NC; Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA., Shenoy VB; Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.

Publikováno v: Science advances [Sci Adv] 2022 Nov 25; Vol. 8 (47), pp. eabq5944. Date of Electronic Publication: 2022 Nov 23.

SELFIES and the future of molecular string representations.

Autor: Krenn M; Max Planck Institute for the Science of Light (MPL), Erlangen, Germany., Ai Q; Department of Chemistry, Fordham University, The Bronx, NY, USA., Barthel S; Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands., Carson N; Syngenta Jealott's Hill International Research Centre, Bracknell, Berkshire, UK., Frei A; Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, Wood Lane, London, UK., Frey NC; Massachusetts Institute of Technology, Cambridge, MA, USA., Friederich P; Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.; Institute of Nanotechnology, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany., Gaudin T; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; IBM Research Europe, Zürich, Switzerland., Gayle AA; Sapporo, Japan., Jablonka KM; Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland., Lameiro RF; Medicinal and Biological Chemistry Group, São Carlos Institute of Chemistry, University of São Paulo, São Paulo, Brazil., Lemm D; Faculty of Physics, University of Vienna, Vienna, Austria., Lo A; Department of Computer Science, University of Toronto, Toronto, ON, Canada., Moosavi SM; Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany., Nápoles-Duarte JM; Facultad de Ciencias Químicas, Universidad Autónoma de Chihuahua, Chihuahua, Mexico., Nigam A; Department of Computer Science, Stanford University, Stanford, CA, USA., Pollice R; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada., Rajan K; Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller Universität Jena, Jena, Germany., Schatzschneider U; Institut für Anorganische Chemie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany., Schwaller P; IBM Research Europe, Zürich, Switzerland.; Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.; National Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland., Skreta M; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; Vector Institute for Artificial Intelligence, Toronto, ON, Canada., Smit B; Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland., Strieth-Kalthoff F; Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada., Sun C; Department of Computer Science, University of Toronto, Toronto, ON, Canada., Tom G; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada., Falk von Rudorff G; Faculty of Physics, University of Vienna, Vienna, Austria., Wang A; Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada.; Solar Fuels Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada., White AD; Department of Chemical Engineering, University of Rochester, Rochester, NY, USA., Young A; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; Vector Institute for Artificial Intelligence, Toronto, ON, Canada., Yu R; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA., Aspuru-Guzik A; Department of Computer Science, University of Toronto, Toronto, ON, Canada.; Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada.; Vector Institute for Artificial Intelligence, Toronto, ON, Canada.; Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada.; Department of Materials Science, University of Toronto, Toronto, ON, Canada.; Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow, Toronto, ON, Canada.

Publikováno v: Patterns (New York, N.Y.) [Patterns (N Y)] 2022 Oct 14; Vol. 3 (10), pp. 100588. Date of Electronic Publication: 2022 Oct 14 (Print Publication: 2022).

High-throughput search for magnetic and topological order in transition metal oxides.

Autor: Frey NC; Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.; Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA., Horton MK; Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.; Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA 94704, USA., Munro JM; Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA., Griffin SM; Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.; Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA., Persson KA; Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.; Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA 94704, USA., Shenoy VB; Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA. vshenoy@seas.upenn.edu.

Publikováno v: Science advances [Sci Adv] 2020 Dec 09; Vol. 6 (50). Date of Electronic Publication: 2020 Dec 09 (Print Publication: 2020).

Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing.

Autor: Frey NC; Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States., Akinwande D; Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States., Jariwala D; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States., Shenoy VB; Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.

Publikováno v: ACS nano [ACS Nano] 2020 Oct 27; Vol. 14 (10), pp. 13406-13417. Date of Electronic Publication: 2020 Sep 16.