The Problem: Computer models and simulations can yield inconsistent and often contradictory results; the Need: Experimental data infrastructure for validation of models and simulation to drive the science forward

Chaka, Anne

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InterNano Nanomanufacturing Repository

Data Infrastructure to Meet the Prediction ChallengeAnne ChakaPhysics LaboratoryNational Institute of Standards and TechnologyNanoInfomatics 2010 WorkshopMaterials by Design and EHS Depend On Chemical and Physical Property Data• Published evaluated chemical and physical property data doubles every 10 years.• Woefully inadequate to keep up with demand.  For nano it’s even worse.• Requires meticulous experimental measurements and/or thorough evaluations of related data from multiple sources• Data acquisition processes are time- and resource-consuming must be initiated well prior to an anticipated need within an industrial or scientific application• A significant part of the existing data infrastructure is not directly used in any meaningful application, because data requirements often shift between the initiation and completion of a data project• Analysis and fitting, such as for EOS, QSAR models, must be reinitiated when significant new data become availableSimulations have the potential to fill the gap for both performance and EHS.Data Infrastructure to Address the Prediction ChallengeThe Problem: Computer models and simulations can yield inconsistent and often contradictory results• Range of validity of models is unknown. Models developed for point solutions are not transferable.• Academic researchers and software companies don’t have experimental capability to validate simulation tools, and are not rewarded for establishing where methods fail.• Only the industrial elite and large national programs have been able to run expensive experiments to validate models over a narrow range of applicability.• Physics-based models are predictive, but are so computationally expensive that without HPC and extensive experimental data they can only be run on toy problems of limited relevance.• Obtaining systematic experimental data to validate simulations is not glamorous. Very difficult to fund.Need: Experimental data infrastructure for validation of models and simulation to drive the science forward• New physical measurements needed to delineate underlying science, test limits of models, and support applications of practical relevance• Methodology, tools for verification, validation, and uncertainty assessment of models and simulations“The prediction challenge is now the most serious limiting factor for computational science…New methods of verifying and validating complex codes are mandatory if computational science is to fulfill its promise for science and society.” –D.E. Post (LANL) and L.G. Votta (Sun Microsystems), Physics Today, January 2005.“…further development [of verification and validation and uncertainty quantification] will have a profound impact on the reliability and utility of simulation methods in the future.” NSF Blue Ribbon Panel, 2006.“The issue of model validation is crucial to regulatory approval of HPC models as an alternative to physical testing.”–Council on Competitiveness, 2004.The Problem in Materials DesignIf we are interested in perhaps 50 elements on the periodic table, there are:2,500 binary combinations, 125,000 ternary combinations, etc. . . . How to vary concentration and distribution? Materials with identical chemical compositions can have totally different properties, depending upon processing and dimensions (nano vs. macro)Screening classes of materials prior to a synthesis and testing cycle is essential.The ever-increasing pace of science and technology has already outstripped our ability to produce needed data.Numberof atoms1 10 100 1000 104 105 …10nElectronicOpticalMagneticMechanicalThermalReactivityStructureFor pure material X:  ^^ ^ ^^^^^^^^How do these points change as X is doped with Y?For other AB binaries?  For XYA ternary compounds….?What is reasonable to try next? (For industrial design)What is reasonable to measure next? (For NIST to develop SR data)All these properties may be important for device or particle performance Morphology What can we currently measure to provide data and guidance?What can we calculate?Where are the gaps?The Problem for Design of Nanoproperties: As size decreases, at what point do qualitatively different properties emerge? How do these points change with material and property? With temperature, pressure, and environment?Numberof atoms1 10 100 1000 104 105 …10nElectronicOpticalMagneticMechanicalThermalReactivityStructureFor pure material X:  ^^ ^ ^^^^^^^^How do these points change as X is doped with Y?For other AB binaries?  For XYA ternary compounds….?What is reasonable to try next? (For industrial design)What is reasonable to measure next? (For NIST to develop SR data)All these properties may be important for device or particle performance Morphology What can we currently measure to provide data and guidance?What can we calculate?Where are the gaps?The Problem for Design of Nanoproperties: As size decreases, at what point do qualitatively different properties emerge? How do these points change with material and property? With temperature, pressure, and environment?We can’t measure everythingNumberof atoms1 10 100 1000 104 105 …10nElectronicOpticalMagneticMechanicalThermalReactivityStructureFor pure material X:  ^^ ^ ^^^^^^^^How do these points change as X is doped with Y?For other AB binaries?  For XYA ternary compounds….?What is reasonable to try next? (For industrial design)What is reasonable to measure next? (For NIST to develop SR data)All these properties may be important for device or particle performance Morphology What can we currently measure to provide data and guidance?What can we calculate?Where are the gaps?The Problem for Design of Nanoproperties: As size decreases, at what point do qualitatively different properties emerge? How do these points change with material and property? With temperature, pressure, and environment?We can’t measure everythingWe can’t calculate everything either…How good is that molecular simulation for calculating a physical property for organic or biological molecules?NIST - in conjunction with industry - administers a blind simulation challenge every year.http://FluidProperties.orgThe results were shocking.What we thought was easy to do, could be very wrong.Example:  Determine the heat of mixing of n-butylamine and waterCH3-CH2-CH2-CH2-NH2Butylamine in water results underscore the greatest challenge for reliable predictionsFor reliable molecular simulations and property predictions, the balance has to be right between all terms:• Bonded and Non-bonded interactions• Hydrophobic & Hydrophilic interactions• H - bonding• Torsions• Enthalpy & EntropyExtensive experimental data are needed to ensure we have the balances correct.The drug discovery and life science simulation community has observed the same problem.**Report on NIST Workshop 2006 “Validating Modeling and Experimental Methods to Enable Drug Discovery”UncertaintyDataMeasurementModelNIST niche:Measurement, data, and models are all necessary to quantify uncertaintyWhat is needed to succeed?Goal: • To develop a synergy between experiment, theory, and modeling to delineate the essential physics required for quantitative predictions and transferability of models to novel systems.•To develop guidelines and standards to achieve quantitative simulationsFor each model:                QuantumAssumptions, advantages, limitations, applicability, cost, data quality required for validation?MacroscopicComputational Chemistry Comparison and Benchmark Database“How good is that ab initio calculation?”PURPOSE: Expand the applicability of computational thermochemistry by providing benchmark data for evaluating theoretical methods and assigning uncertainties to computational predictions.Contents– Computed, accurate experimental thermo-chemical quantities for >650 species,>85,000 calculations– Dipole moments, polarizabilities, transition states, barriers to internal rotation, atomic charges, group additivity methodsTutorialsUser InterfaceGraphical and statistical analysis of theory vs. experimentWeb Access>25,000 webpages/month ave., 100,000 peakhttp://srdata.nist.gov/cccbdb russell.johnson@nist.gov020406080100120-80 -60 -40 -20 0 20 40 60 80atomization energy exp. - calc. (kJ/mol)number of speciesG2MP2G2B3LYPBLYPErrors in computed atomization energies at 0 K for 150 carbon-containing species and four theoretical methods.From Data Infrastructure to Innovation –Quantitative Simulations for Nonbonded InteracitonsWHAT WHOData to calibrate, validate models Tools to assess uncertainty  in models, simulations Derived performance properties Specific industrial problemNIST& collaborators: Develops, verifies, validates, and assesses uncertainty in standard reference calculations. Characterize range of validity of models, analyze sensitivity to input variables.TheoryMeasurementIndustry & agencies advise on prioritiesSolubility, calorimetry, and speciation in water & solvents across range of pH, temperature, and ionic strengthHydrophobic groups with range of shapesX20-30 hydrophillic chemical functional groupsElectrostatics: range of polar & polarizable moleculesH-bonding strengthPka rangeTautomersSalts,mineralsDispersion/aggregationSolubility/precipitationSelf-assembly, molecular recognitionBioavailabilityOil/water & blood/lipid partitioningDrug designSoftware companies, academic researchers, industrial partners:Industry: utilizes software for product and process developmentSoftware companies: packaging,user interfaces, manuals, user supportMetal complex speciationNIST & Collaborators: Close collaboration between theorists, experimentalists to select representative species to bracket range of physics Quantum wavefunction components Bayesian uncertainty analysisNumerical error estimation • Develop efficient methods, software • Verify, validate against reference data and standard reference simulationsMeasurements quantifying fundamental interactionsWater treatmentConcretePlastics PaintsAssessment methods, protocols Standard Reference SimulationsMultiscale methods DATAINNOVATIONFoodsInfrastructureApplicationEHSFrom Data Infrastructure to Innovation –Quantitative Simulations for Nonbonded InteracitonsWHAT WHOData to calibrate, validate models Tools to assess uncertainty  in models, simulations Derived performance properties Specific industrial problemNIST& collaborators: Develops, verifies, validates, and assesses uncertainty in standard reference calculations. Characterize range of validity of models, analyze sensitivity to input variables.TheoryMeasurementIndustry & agencies advise on prioritiesSolubility, calorimetry, and speciation in water & solvents across range of pH, temperature, and ionic strengthHydrophobic groups with range of shapesX20-30 hydrophillic chemical functional groupsElectrostatics: range of polar & polarizable moleculesH-bonding strengthPka rangeTautomersSalts,mineralsDispersion/aggregationSolubility/precipitationSelf-assembly, molecular recognitionBioavailabilityOil/water & blood/lipid partitioningDrug designSoftware companies, academic researchers, industrial partners:Industry: utilizes software for product and process developmentSoftware companies: packaging,user interfaces, manuals, user supportMetal complex speciationNIST & Collaborators: Close collaboration between theorists, experimentalists to select representative species to bracket range of physics Quantum wavefunction components Bayesian uncertainty analysisNumerical error estimation • Develop efficient methods, software • Verify, validate against reference data and standard reference simulationsMeasurements quantifying fundamental interactionsWater treatmentConcretePlastics PaintsAssessment methods, protocols Standard Reference SimulationsMultiscale methods DATAINNOVATIONFoodsInfrastructureApplicationEHSNan Hub Pilot?We can’t measure everythingWe can’t calculate everything either…Aim: • Develop a synergy between experiment, theory, and modeling to delineate the essential physics and develop a rational strategic approach to design. •Perform benchmark calculations and experiments•Determine extent of “neighborhood” in periodic table and composition: Same neighborhood => same model can be used with reasonable certainty to determine properties.Example:AB Charge Transfer(EA/IP)& band gapCCSiSiSiCWCFeSNaClMgOAlOAuAuIonicityWhat is the effect on  brittleness vs ductility across classes of materials?Standard Reference Simulation: contains physics-based rules for deciding what class of model to be used. For example, if band gap is more  than 2 eV or EA/IP ratio is greater than y, then maintain constant charge on atoms during classical simulation. If small band gap, allow charge to redistribute, which requires QM.

Data Infrastructure to Meet the Prediction Challenge

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