1 / 10

New materials/electronic structure in 21 st century

New materials/electronic structure in 21 st century. Typical features: - multi-component, hierarchies - 0-3D (dots, chains, layers ... ) - d- and f- elements - H: proton as a quantum part. - organic/inorganic/solid - bioinspired. Challenges: - lack of a "unifying" strategy

joey
Download Presentation

New materials/electronic structure in 21 st century

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. New materials/electronic structure in 21st century Typical features: - multi-component, hierarchies - 0-3D (dots, chains, layers ... ) - d- and f- elements - H: proton as a quantum part. - organic/inorganic/solid - bioinspired Challenges: - lack of a "unifying" strategy - complexity - competition of mechanisms: quantum, temperature, etc - single electron/quantum effects important Solving these one-by-one, ie, by a postdoc focused on a class of materials for X years is ultimately inefficient Unifying concept on which we all agree: Schrodinger equation Solve it in the many-body framework (!) with the original Hamiltonian Lubos_Mitas@ncsu.edu

  2. Computational Materials Research Key goals: - predict, design and optimize new materials for 21st century - complement, guide and/or replace experiment - new science frontier: from one-particle to many-body Broad application areas: - new energy sources: production/storage/processing of H - nanosystems based materials - bioinspired materials and processes: waste is nonexistent Clear-cut example of previous impact: - 3rd most cited PRL in all physics and history is Ceperley/Alder Quantum Monte Carlo of homogeneous electron gas Possibilities/breakthroughs with 500-fold increase in compute power: -a few meV accuracy for energy differences - quantum effects, temperature, dynamics on the same footing - nanosystems in action, magnetism, supreconductivity in a wave function framework - H (bonded, solvated, ...): proton as a quantum particle Lubos_Mitas@ncsu.edu

  3. Quantum Monte Carlo: a unique strategy/opportunity for quantum many-body problems Schrodinger equation in a propagator form -sample the wave function by walkers in space -boost the efficiency with explicitly correlated trial functions -propagate the walkers while enforcing all required symmetries -evaluate the expectation values of interest QMC: - new physics/paradigm: work directly on many-body effects - scalable, robust,highly efficient on parallel architectures - favorable scaling in # of particles: nominally ~ O( N3) and implentation with almost ~ O( N ) feasible - accurate: typically ~ 95% of correlation energy across systems 0.1 eV/1% accuracy/agreement with experiment - benchmarks for other methods, consistent results Lubos_Mitas@ncsu.edu

  4. QMC bottlenecks and advanatges: next 5-10 years Scientific: - beyond the fixed-node approximation, very active research: obtain ~ 99% of correlation with polynomial scaling - spin and spatial degrees of freedom on the same footing - responses to external fields and spectral functions - from wave functions to density matrices (temperature) Mix of Science and Algorithmic/Computational: - more efficient and accurate building of trial functions: eg, robust stochastic optimizations - efficient coupling and data exchange with one-particle approaches Hardware/ 1. processor speed Software: 2. parallelism 3. stability (QMC can test it real well) 4. memory, communication, etc, relevant but secondary Lubos_Mitas@ncsu.edu

  5. Qauntum Monte Carlo: typical run System: 50 atoms, 200 electrons, desired accuracy ~ 0.1 - 0.2 eV Typical input: tens/hunderds of MB (initial/trial wave function) Typical run: - tens of processors for days and weeks - MPI - 10-100s walkers in 3N-dim. space per processor - evolved for hundreds of steps (independently, or occasionally rebalanced) - accumulate statistics from processors Typical output: - most of the data reduced to simple physical quantities - current walker configurations stored (tens of MB per proc) - restartable Lubos_Mitas@ncsu.edu

  6. Materials with competing many-body effects: hexaborides CaB6, LaxCa1-xB6 , ... 5% La-doped CaB6 is a weak magnet up to 900K (!) No d or f electrons: - genuine itinerant magnetism ? - promising spintronics material ? Undoped CaB6 : insulator ? exitonic insulator ? metal ? Experiments contardictory: ARPES: insulator de Haas-van Alphen: metal Optical, etc: metal, insulator Calculations inconclusive: DFT: band overlap 1 eV (Swiss,...) DFT: small gap (Japan) GW (DFT+ pert. corr.): 1 eV gap (NL) GW: small overlap (Japan) Can we predict the correct gap before the experiment ? Lubos_Mitas@ncsu.edu

  7. CaB6 band structure in Hartree-Fock Large gap of the order of 7 eV Lubos_Mitas@ncsu.edu

  8. CaB6 band structure in DFT - B3LYP Gap is now only about 0.5 eV ! Lubos_Mitas@ncsu.edu

  9. CaB6 band structure in DFT - PW91 1 eV overlap at the X point: d-states on Ca ! Fixed-node DMC gap: 1.3(3) eV X G Lubos_Mitas@ncsu.edu

  10. Predict a "nanomagnet": caged transition elements TM@Si12 TM=Sc, Ti, ... 3d, 4d, 5d Find the smallest stable "nanomagnet" made from silicon and a transition metal atom ... - attempt to predict caged d-spin - no success, hybridized, unstable Experiment in Japan in '01! W@Si12 APS March Meeting in '94: L. M.: Electronic structure of Mn@Si12 Lubos_Mitas@ncsu.edu

More Related