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INELASTIC AND REACTIVE ELE- MENTARY PROCESSES IN ATOM- DIATOM, DIATOM-DIATOM COLLISIONS AND BEYOND Antonio Laganà* Dipartimento di Chimica University of Perugia * Antonio Riganelli, Dimitris Skouteris, Leonardo Pacifici, Noelia Faginas Lago, Stefano Crocchianti

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2 MULTISCALE SIMULATIONS Electronic structure Kinetics of non elementary processes Macroscopic properties of realistic systems Fluid dynamics, electrodynamics, etc. Nuclear dynamics

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3 SUMMARY A priori molecular simulations: theoretical means The N + N 2 collisions: beyond quasiclassical The need for accurate potential energy surfaces Some diatom-diatom, atom-polyatom processes Towards complex molecular systems Concurrent computing Metalaboratories for molecular calculations Grid enabled molecular simulators

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4 AC ( f ) + B (reactive) A + B + C (dissociative) The atom- diatom case TRAJECTORY CALCULATIONS The Hamilton equations Integrate the above differential equations from a given configuration of the reactants until a final reactive, non reactive or dissociation configuration is reached dR x /dt=P Rx /µ A,BC dr x /dt=P rx /µ BC dP Rx /dt=-∂V/∂R x dP rx /dt=-∂V/∂r x dr y /dt=P ry /µ BC dr z /dt=P rz /µ BC dR y /dt=P Ry /µ A,BC dR z /dt=P Rz /µ A,BC dP ry /dt=-∂V/∂r y dP rz /dt=-∂V/∂r z dP Ry /dt=-∂V/∂R y dP Rz /dt=-∂V/∂R z A + BC ( i ) AB ( f ) + C (reactive) A + BC ( f ) (non reactive)

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5 QUANTUM METHODS Time dependent Time independent {W} – set of position vectors of the nuclei or any other choice of coordinates H n - nuclear Hamiltonian Factor out time and choose a different continuity va- riable (or transformation from reactants to products)

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6 THE DYNAMICAL QUANTITIES PROBABILITY: P if =N if /N or =|S if | 2 CROSS SECTION: σ if =πb 2 max P if RATE COEFFICIENTS: averaging σ if (E) over discrete distributions and integrating over continuous distributions Reaction and Molecular Dynamics, Springer, 2000

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7 N + N 2, H+H 2, O+O 2 H 2 +OH, H 2 +H 2, OH+HCl, OH+CO Cl + CH 4 RECENT DYNAMICAL STUDIES

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8 Nitrogen atom Nitrogen molecule reaction Previous calculations: extended quasiclassical trajectory calculations of state to state rate coefficients (available for distribution)

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9 : exact quantum calculations Zero total angular momentum Time dependent approach in Jacobi coordinates Initial quantum states v=0-5 j=0,1,2 Collision energy interval Iterations: ~2000 LEPS surface E= eV

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10 THE TIME DEPENDENT METHOD Collocate the wavepacket Time propagate the wavepacket Carry out its analysis at the product asymptote

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11 : state to state probabilities eV eV eV eV E(v) V=0 V=1 V=2 V= eV 1.543eV V=4 V=5

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12 : threshold energies eV eV eV eV Etr V=0 V=1 V=2 V=4

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13 Product vibrational distributions (1.65 eV)

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14 Time dependent 3D Time independent RIOS

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15 RIOS: state to state probabilities (v=0)

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16 RIOS vs 3D product vibrational distributions

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17 State specific probabilities. Effect of rotation

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18 FITTING A NEW POTENTIAL ENERGY SURFACE (PES) Fit the parameters of the PES to ab initio data Adopt process coordinates instead of arrangement coordinates (like Jacobi coordinates) Use bond order (BO) variables defined as n ij =exp[-β ij (r ij -r eij )] and their polar version ρ=[n n 23 2 ] ½ α=atan(n 23 /n 12 )

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21 OH + HCl

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22 POLYATOMIC REACTION FUNCTIONAL FORMS ROtating BO (ROBO) and Largest Angle Generalization ROBO (LAGROBO). Many Process Expansion (MPE) W= ξ W ξ V ξ

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30 MOVING TO LARGER SYSTEMS Simplify the interaction Decompose the domain

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31 THE FORCE FIELD The most popular formulations of force fields separate intra- from inter-molecular forces Intramolecular terms are associated to functional forms fitted to ab initio data Intermolecular are expressed as sums of two body semiempirical (usually of the Lennard Johnes type) functionals

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Interaction representation Many body expansion truncated to the second term Two body interactions are of the atom(ion) – atom(ion) type Portability among different systems

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33 Energy minimization Ar n C 6 H 6 Isomer (1|1) Isomer (2|0) -665C 3v (2|0)2 -711D 6h (1|1)2 -356C 6v (1׀0)(1׀0)1 E( 1/cm )GPIsomern

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34 OTHER NEW GLOBAL POTENTIALS 1.Atom-bond pseudo two-body ( Pirani et al.) 2. Full Bond Order potential (ALLBO) ( Laganà et al. ) n kl is the Bond Order variable of the kl atomic pair V ({r}) = ∑ k ∑ m L km (r km,,α km ) L = Lennard Johnes potential, k = atom index, m = bond index P = Bond order potential, k = atom index, l = bond index V ({r}) = ∑ k ∑ l L kl (n kl )

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35 CONCURRENCY IN MOLECULAR CALCULATIONS 1. NATURAL CONCURRENCY FROM EXTENSIVE TRAJECTORY CALCULATIONS 2. MULTILEVEL CONCURRENCY IN QUANTUM CALCULATIONS

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36 SISD (Single Instruction stream Single Data stream) CUPUMM CU Control Unit PU Processing Unit MM Memory Module ISDS IS Instruction stream DS Data stream

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37 SIMD (Master - workers) CU PU1 PU2 PUn MM1 MM2 MMn IS DS1 DS2 DSn

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38 MIMD (Cooperative workers) CUPU1 PU2 PUn MM1 MM2 MMn DS1 DS2 DSn CU IS1 IS2 ISn

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39 MPI QUASICLASSICAL PSEUDOCODE SEND “ready” status message RECEIVE seed integrate trajectory update indicators SEND “ready” status message GOTO RECEIVE Worker: DO traj_index =1, traj_number RECEIVE status message IF worker “ready” THEN generate seed SEND seed to worker ELSE GOTO RECEIVE endIF endDO Master:

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40 COLLABORATIVE INITIATIVES TO DEVELOP REALISTIC A PRIORI SIMULATORS Innovative approaches to chemical (as well as to physical, aerospace, medicinal, biological, etc.) problems need the cooperation of knowledge and computer resources. The concentration of human and hardware resources is no longer practical for logistic, economic and psycological reasons.

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METACHEM Metalaboratories for complex computational applications in Chemistry

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42 THE METALABORATORY The METALABORATORY is a cluster of geographically distributed laboratories having complementary expertise and software programs and having some hardware resources grafted on a computing grid.

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43 THE STRUCTURE OF A METALABORATORY Several computational science laboratories acting as reservoirs of specific expertise relevant to the realization of a given project. One particularly skilled computer science laboratory (or Large Scale Computing Facility) acting as the regulator of the grid. Other laboratories having complementary expertises (for example an experimental laboratory).

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44 ONGOING MOLECULAR SCIENCE METALABORATORIES CI Calculations (Carsky). DIRAC (Faegri). SIMBEX (Gervasi) Atmospheric processes (Aguilar) Elchem (Laganà) Chemical knowledge (Rossi)

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45 The CHEMISTRY community Simbex Murqm Dirac Elchem Dysts Comovit Icab

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46 LABS per NATIONALITY (51) 1 Isr,Pl,Sk,Nl 2 Cz,Ch, Fr, Dk, A, Sw, No 3 Hu 4 Gr 5 E 6 D, Uk, 9 I

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47 SIMBEX: SIMUL. MOLECULAR BEAM EXPERIMENTS Managing an a priori simulation to be inter- faced with the experi- ment in crossed mole- cular beam measure- ments Exper. Simul.

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48 REQUEST: a potential fitted to beam experiments Interaction Observables SUPPLY: the potential and related monitors Dynamics YES NO Theoretical and experimental results agree? The GEMS.0 demo application

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49 The INTERACTION module INTERACTION DYNAMICS Is there a suitable Pes? Are ab initio calculations available? Are ab initio calculations feasible? Ab initio application using programs for electronic structure Application using fitting programs to generate a PES routine Import the PES routine NO YES Force field- application taking empirical data from database to generate a PES START

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50 The DYNAMICS module DYNAMICS OBSERVABLES Are quantum dynamics calculations inappropriate? Is the calculation single initial state? NO YES TI: application carrying out time-independent quantum calculations TD: application carrying out time- dependent quantum calculations ABCtraj: quasiclassical trajectory calculations

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51 The OBSERVABLES module OBSERVABLES NO YES Is the observable a state-to-state observable? Is the observable a state specific observable? RATE: virtual monitor (VM) for thermal rate coefficients CROSS: VM for state specific cross sections, rate constants and maps of product intensity DISTRIBUTIONS: VM for scalar and vector product distributions, and state-to-state crosssections Do calculated and measured properties agree? END YES INTERACTION NO Beam VM for Intensity in the Lab frame

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52 The Virtual monitors

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53 THE PROTOTYPE COMPUTING GRID The computers that the participating laboratories will put outside the firewall to be clustered on the network and act as a single virtual parallel machine. The running version (not the ones under develop- ment) of the relevant codes that the participating laboratories will implement to run concurrently on the grid for the project. The distribution software to allocate the different tasks on the most suitable machines

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54 Demo deployment layout Sites –GILDA testbed sites, in which INFN Grid (fully compatible with LCG 2.2.0) middleware is installed. Key services –Resource Broker: grid004.ct.infn.it –Computing Element: ce.grid.unipg.it –User Interface: ui.grid.unipg.it –GENIUS Portal: https://genius.ct.infn.it https://genius.ct.infn.it –CompChem VOMS

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55 The testbed sites The Chemistry Department of the University of Perugia has been included in the sponsor and the testbed site list The Chemistry Department node is made by a cluster of 14 nodes (2 proc. Intel Pentium III, 2G RAM, 40G HD) + Computing Element + LCNGFG server

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56 Demo scale of usage Number of jobs: 10 per day Storage: 100KB – 50GB depending on the type of computational engine used and the chemical system studied: –Trajectory calculations: <100KB –Time Dependent Quantum: 10GB –Time Independent Quantum: 50GB RAM: 100KB – 2GB –Trajectory calculations: <100K –Time Dependent Quantum: 1GB –Time Independent Quantum: 2GB Success rate: 98%

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57 EGEE Grid Usage of LCG-2 middleware services GEMS Application Deployment Server Resource Broker Computing Element MPI GEMS program GEMS program GEMS program GEMS programs Working nodes License Server Outbound connectivity

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58 The grid added value SOFTWARE INTEGRATION INTO DISTRIBUTED WORKFLOWS: assemble applications out of various (different or complementary) distributed competences coordinated via the grid. COMPUTATIONAL CAMPAIGNS: evaluate properties depending on the fate of few out of millions or more events by distributing the computations on the grid COLLABORATIVE ENGINEERING OF KNOWLEDGE: handle (when is the case also in a privacy protecting fashion) chemical information and knowledge including training and production of new knowledge.

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SIMBEX: a research/educa tional tool for the simulation of elementary chemical reaction High interactivity Advanced visualization In deep insight into the chemical mechansm

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60 GRID PROJECTS GRID.IT: the Italian project on grid platforms EGEE: the European production grid

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61 GRID.IT INFN ASI CNIT The Italian GRID CNR Università

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62 Programming Environment High-level services Knowledge services, Data bases, Scientific libraries, Image processing, … Domain-specific Problem Solving Environments (PSEs) High-performance, Grid-aware component-based programming model and tools Resource management, Performance tools, Security, VO, … Next Generation Middleware Basic infrastructure - standards (OGSA-compliant) Software technology of Grid.it Applications (Chemistry, Biology, Earth science, etc.)

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63 PG MI PD BO BA NA RM ChemGrid.it: a Grid model for Chemistry Comp. Center UPV UB CESCA Comp. Center

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64 ChemGrid: the PG configuration Access to GRID.IT

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65 The VO CompChem has been created to register to the VO: The implementation of the Grid Molecular Simulator prototype has prompted a modification of the EGEE infrastructure in order to guarantee the strong need for real-time interaction with the Grid The prototype will be rewritten in XML (instead of PHP) to be included in the Genius application testbed to demonstrate the power of the Grid. We attended the following EGEE events: –EGEE Generic Applications Advisory Panel (EGAAP), First Meeting, Geneva, June 14 th, 2004 –EGEE NA4 Open Meeting, Catania, July 14-16, 2004 The outcomes of CompChem

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66 COLLABORATIONS INFORMATICS M. Vanneschi, R. Baraglia, Pisa (Italy) O. Gervasi, S. Tasso, Perugia (Italy) CHEMISTRY G.G. Balint-Kurti, Bristol (UK) E. Garcia, Vitoria (Spain) M. Alberti, Barcelona (Spain) G. Parker, Norman (USA) SIMBEX EU COST CHEMISTRY GROUP

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67 CONCLUSIONS Rigorous dynamical calculations are becoming routinely feasible for polyatomic systems Molecular dynamics treatments are tackling extremely complex systems Dynamical treatments are becoming essential part of multiscale approaches on the computing Grid Research must adopt a service oriented orgnization to become sustainable

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