Software Methods for Sustainable Solutions

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Software can be used to speed up R&D into sustainable solutions such as alternative energy (batteries, fuel cells, biomass conversion), catalysts, and eljminiating environmental toxins. The …

Software can be used to speed up R&D into sustainable solutions such as alternative energy (batteries, fuel cells, biomass conversion), catalysts, and eljminiating environmental toxins. The presentation gives an overview of the various methods and illustrates their applicaiton with case studies.

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  • 1. Software Tools forDevelopment of SustainableSolutionsGeorge Fitzgerald, Ph.D.
  • 2. Introduction• What do we mean by ‘sustainable solutions?’• In this presentation we will focus on: – Alternative energy – Catalysis – Identifying and reducing environmental toxins• What tools will we use? – Molecular modeling like DFT & force fields – Data analysis like recursive partitioning and neural networks – Knowledge extraction tools – database searching and reporting• These tools have also been used in research on – Carbon capture and sequestration – Replacement of chlorofluorocarbons – Improved crop production and protection – Hypoallergenic formulations – …© 2008 Accelrys, Inc. 2
  • 3. Outline• Overview of software methods• Solutions for Alternative Energy• Solutions for Catalysis• Solutions for Toxicology• Demos© 2008 Accelrys, Inc. 3
  • 4. Why Use Modeling? Select a Test for Screen for Synthesize Analyze new candidate Effectiveness adverse Select a Screen Test for Screen for Synthesize Analyze new candidate in silico Effectiveness adverse Fast loop! • Typical workflows with and without modeling. • Modeling accelerates the discovery process by allowing you to test materials before going into the lab – Modeling faster than experiment – Though not 100% accurate, modeling can distinguish good leads from bad – Modeling lets you extract trends, understand what contributes to a “good” lead • Modeling allows you to focus your efforts on only the most promising leads, saving time and expense© 2008 Accelrys, Inc. 4
  • 5. Virtual Screening• Virtual screening is the cornerstone of in silico drug discovery• Allows researchers to effectively screen drug design space to identify most promising structures – reduces the size of a chemical library to be screened experimentally: O(106) to O(10) Quick & dirty calculations• Improves the likelihood of finding interesting structures – systematic screening – screen possible design space before synthesized Sophisticated calculations• Saves time and money – computational evaluation is faster and much less expensive than experimental Experiment testing Now possible to apply techniques to materials science© 2008 Accelrys, Inc. 5
  • 6. Modeling & Simulation Overview • Quantum – Solution of the Schrödinger equation – Good results for structural, electronic, optical properties HΨ = EΨ – Necessary for systems with bond-breaking, reactions and catalysis – Limited to <1000 atoms • Molecular – Approximate atomic forces with ball-spring model, charges, vdW forces – Good results for structures, interaction energies, miscibility, solubility, adhesion – Diffusion, permeation, membrane transport – Useful up to around 10,000 atoms© 2008 Accelrys, Inc. 6
  • 7. Modeling & Simulation Overview • Mesoscale – Groups of atoms represented by beads – Empirical forces between beads account for effects such as viscosity – Micelle or vesicle formation – Emulsions, kinetics and properties – Polymeric microspheres – Applicable to 100,000 atoms • Bulk – Finite element models – Requires reliable parameters, built up from more accurate methods or determined empirically – Structural properties for bulk-scale systems – Elastic constants, thermal expansion, gas permeability, crack propagation© 2008 Accelrys, Inc. 7
  • 8. Overview of Statistical Methods• Goal: analyze the results of many calculations to – Extract trends – Gain understanding of which parameters are important to performance• QSAR (Quantitative Structure Activity Relationship) – Assume a relationship exists between structure and function – Use things are easy to calculate to make predictions about things are hard – Ex, toxicology models – Can be quite quantitative when fit to large data set• Data reduction – Simplify the way you look at many variables – Correlation matrix – Principle component analysis• Cluster analysis – Define similarity based on some criteria – Nearest-neighbor analysis – Hierarchical clustering© 2008 Accelrys, Inc. 8
  • 9. Overview of Reporting• Sometimes the best thing you can do is just look at your data – Do good results tend to one side or the other? – Can I spot an obvious minimum or maximum? – Does one result stand out?• When you have lots & lots of data you can use interactive reports – One view gives high-level overview – Click on a point to zoom in and get detailed information – Create comparative reports of your selected results© 2008 Accelrys, Inc. 9
  • 10. Alternative Energy Examples• Fuel cells – Stability of polymer membranes – Hydrogen storage – Oxygen activation catalysts• Biodiesel: fat to fuel• Batteries: extending lifetime• Other examples – Gas to liquid – Coal to liquid – Improved combustion© 2008 Accelrys, Inc. 10
  • 11. Anatomy of a Fuel Cell • Components we can model – Hydrogen storage – Hydrogen activation – Proton exchange – Oxygen activation • Applications – Power Stations – Space Vehicles – Home and Business Power Supply – Transportation (buses, trucks, cars, motorcycles…) – Portable Applications • Laptops, cell phones etc. • Military© 2008 Accelrys, Inc. 11
  • 12. Polymer Membranes• Polymer membranes used in both hydrogen and direct methanol types of fuel cells – PEMFC, DMFC• Proton conduction membranes usually consist of polymer with covalently-bound acidic groups such as SO3H or CO2H• Traditionally based on Nafions (Dupont® perfluorosulfonate polymers)• Some problems with Nafion include: – Poisoning of catalyst. Could be reduced by operating at higher temps (120-200°C) – Water must be present; Dehydration at higher temperatures (>~80°C) leads to loss of proton conductivity – Expensive – Diminished mechanical stability at higher temperatures – Undesirable permeability to methanol (DMFCs)• Need new polymers to overcome limitations and create less expensive and more efficient cells capable of running at higher temperatures Acknowledgements James Wescott (Accelrys) Lalitha Subramanian (Accelrys)© 2008 Accelrys, Inc. 12
  • 13. Steps to Modeling PEFC• Pick one problem at a time to start out• Create appropriate model• Decide on appropriate modeling methods• Validate against known results before doing predictive modeling!• Systematically change materials to optimize properties• Ultimate goal: create a PEFC membrane that is more stable with respect to moisture• Initial goal: predict structure as a function of water content – Experiment only probes surface structure, or has lead to ambiguous results – Need the structure in order do any other modeling – Maybe looking at the structure will give us ideas• What model? – Amorphous Nafion, large periodic cell – Morphology of Nafion/water system has structures on the order of 10’s of nm – Requires 1000’s of atoms• What tools? – Mesoscale model is needed because of the size© 2008 Accelrys, Inc. 13
  • 14. Molecular Structure of Nafion® Non-polar Polar N P CF2 CF2 x CF2 CF2 y n O CF2 CF O CF2 CF2 SO3H z CF3 Atomistic model Parameterization “Bead” model© 2008 Accelrys, Inc. 14
  • 15. Nafion Calculations• Program: MS MesoDyn – Uses mean-field density functional theory – Coarse-grained method for the study of complex fluids, kinetics, and their equilibrium structures• Considers interaction parameters between “beads”• Parameters derived from force field calculations or obtained from literature• Start from initial guess structure and allow to evolve until stable Atomistic model Parameterization “Bead” model© 2008 Accelrys, Inc. 15
  • 16. Mesocale Modeling Resultsλ=2λ=8 Mean squared difference of concentration from average concentration, i.e., a measure of phase separation.© 2008 Accelrys, Inc. 16
  • 17. Mesoscale Modeling Results• Phase separated micelles filled with water, surrounded by side chains containing sulfonic groups, and embedded in the fluorocarbon matrix starting around λ = 4• General agreement with the experimental morphologies in terms of – Distribution and shape of water domains – Quantitative prediction of 2–5 nm cluster sizes• Next steps – Study dynamic processes, e.g., hydrate – dehydrate – Model proton mobility – Change membrane components systematically and predict performance• Acknowledgements – James Wescott (Accelrys) – Lalitha Subramanian (Accelrys)© 2008 Accelrys, Inc. 17
  • 18. Hydrogen Storage Challenges• Seek a material that will allow on-board storage of Hydrogen (as H2, CH4, CH3OH, etc.)• Engineering challenges – Target driving range of ≥ 300 mi – Must also meet cost, safety, etc. standards• Materials Science Challenges – High H2 storage capacity: 6 wt% by 2010; 9 wt% by 2015 – Low device weight – Rapid discharge/recharge – Durable for many discharge/recharge cycles© 2008 Accelrys, Inc. 18
  • 19. Hydrogen Storage Materials• Metal hydrides • Chemical storage – Alanates – Sodium borate – Destabilized binary hydride alloys – Liquid chemical hydride – Lithium amides – Magnesium hydride slurry – Nanoscale lithium nitride materials • New materials• High surface area sorbents – Conducting polymers – Graphitic materials – Metal organic frameworks – Nanostructured carbon – Clathrates – Perhydrides This list is not comprehensive© 2008 Accelrys, Inc. 19
  • 20. Steps in Modeling H2 Storage• Focus on one problem – Type of material (e.g., metal clusters) – Form of hydrogen (e.g., H2) – Particular challenge (e.g., binding energy, loading capacity)• Create appropriate model, .e.g., – Generally, you will be working with a team that has already decided on a class of material – Periodic super-structure or cluster? – Make approximations in size? • Larger model → more accurate • Smaller model → faster calculations• Select appropriate theoretical approach – Chemisorption needs QM-based method – Physisorption can use force fields – Time-evolution (diffusion) very expensive to do with anything but force fields© 2008 Accelrys, Inc. 20
  • 21. Aluminum clusters for H2 Storage• Magic cluster sizes, i.e. those with closed- shell electron numbers, are: N= 2, 8, 18, 20, 34, 40, 70, 112 … – Al13 cluster is only one electron short from ‘magic’• Experimentally and theoretically both have been found to be especially stable• Might these work for H2 storage? … Acknowledgements Alexander Goldberg (Accelrys) Irene Yarovsky (RMIT)© 2008 Accelrys, Inc. 21
  • 22. Goals of this work • Long term: – Develop a porous solid of Al nanostructures for use in H-storage • Short term: – Model stable geometries of atomic and molecular hydrogen adsorbed on Al clusters – Calculate adsorption capacity of Al clusters – Calculate adsorption-desorption barriers – Estimate mobility of H on the surface – Study the cluster size effects on H adsorption • Method – QM-based approach – Density Functional Theory (DFT) – Determination of energy minima – Determination of transition states and energy barriers • Model – Single nanoclusters of Al13 Two isomers of (Al13H)2 from Alonso, et al., Nanotechnology 13(2002) 253-257.© 2008 Accelrys, Inc. 22
  • 23. Al Cluster Calculations• Density Functional Theory using MS DMol3 – Fast implementation of DFT – Works for molecules and periodic solids• DNP basis set – equivalent in size to Gaussian 6-31G**• Exchange-correlation functional: BLYP• TS search using LST/QST method of Halgren and Lipscomb: Chem. Phys. Lett. 49, 225 (1977)• Construct clusters starting from periodic Al metal models• Approach validated by comparing to experimentally determined LUMO and IP of Al13- and Al13H© 2008 Accelrys, Inc. 23
  • 24. Potential Energy Diagram Potential H-H bond breaking Energy Al-H bond formation Distance Physisorption well H H Al13 Chemisorption well H H Al13© 2008 Accelrys, Inc. 24
  • 25. Potential Energy Diagram Potential H-H bond breaking Energy Al13+H2 energy 5 Energy, kcal/mol Al-H bond formation 0 1.59 Distance -5 Physisorption well Physisorption H H -10 3.6 kcal/mol Al13 -15 Chemisorption 14.24 kcal/mol -20 Chemisorption well H H separation distance, Å Al13© 2008 Accelrys, Inc. 25
  • 26. Hydrogen Storage Conclusions• The reaction Al13 + H has no potential barrier• The reaction Al13 + H2 has a small potential barrier• Al13 is a potential storage medium!• Future plans – Effect of element substitution – Crystals of clusters – Diffusion rates – Thermal stability© 2008 Accelrys, Inc. 26
  • 27. Challenges in biodiesel fuel development• Free fatty acid (FFA) content can result in soap formation and reduced yield of biodiesel (methyl ester) upon reaction with alkali catalysts.• Soaps may allow emulsification that causes the separation of the glycerol and ester phases to be less sharp.• When FFA levels are above 1%, it is possible to add extra alkali catalyst.• For feedstock containing 5-30% FFAs, one needs to convert the FFA to biodiesel or the overall conversion will be low. Biodesiel Production Technology, J. Van Gerpen, B. Shanks, R. Pruszko, D. Clements, G. Knothe, NREL/SR-510-36244, July 2004.© 2008 Accelrys, Inc. 27
  • 28. Options for High FFA• Enzymatics methods: Expensive and not used commercially• Glycerolysis: Requires high temperature and is slow.• Acid catalysis: Esterification of FFAs is fast, but transesterfication is slow. Water is produced which can halt reaction.• Acid catalysis followed by alkali catalysis. Acid catalysis is used for pre-treatment. When the FFA content is near 0.5%, alkali is added to convert triglycerides to methyl esters• Goal: predict fatty acid volume (FAV) as function of process conditions• Method – Apply statistical methods (neural networks and genetic function algorithms) to optimize process conditions (reaction time, methanol-to-oil ratio, H2SO4 concentration) Biodesiel Production Technology, J. Van Gerpen, B. Shanks, R. Pruszko, D. Clements, G. Knothe, NREL/SR-510-36244, July 2004.© 2008 Accelrys, Inc. 28
  • 29. Statistical methods to optimizebiodiesel production• Does not require much computational power• Requires “lots” of data, 5 data points/parameter or more• Once you create a model, easy to test 1000’s of combinations• Start with systematic grid of data – Fit to a function (GFA or NN) – Search parameter space for optima “Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA”, Rajendra, P. C. Jena, H. Raheman, Fuel 88 (2009) 868–875.© 2008 Accelrys, Inc. 29
  • 30. Applying statistical methods tooptimize a function• Development of statistical methods and process parameter optimization via graphical workflow tools• Define input variables (reaction time, etc)• Define dependent variable (FAV)• Number of terms in the model• Model can be saved, reused, sent to collaborators• Workflow can set up systematic search of grid, identify optima© 2008 Accelrys, Inc. 30
  • 31. Lithium Ion Batteries and SEI FilmFormation• The electrolyte typically consists of one or more lithium salts dissolved in an aprotic solvent with at least one additional functional additive• Additives are included in electrolyte formulations to increase the dielectric strength and enhance electrode stability by facilitating the formation of the solid/electrolyte interface (SEI) layer Acknowledgements Ken Tasaki (Mitsubishi Chemicals Inc.) Mathew Halls (Accelrys) Computational resources: HP© 2008 Accelrys, Inc. 31
  • 32. Lithium Ion Batteries and SEI FilmFormation 1 e- decomposition scheme 2 e- decomposition scheme • Initiation step leading to anode SEI formation is electron transfer to the SEI forming species resulting in a concerted or multi-step decomposition reaction producing the passivating SEI layer at the graphite-electrolyte interface • Important requirements for electrolyte additives selected to facilitate good SEI formation are: – higher reduction potential than the base solvent – maximal reactivity for a given chemical design space – large dipole moment for interaction with Li© 2008 Accelrys, Inc. 32
  • 33. Modeling Battery Additives• Choose one aspect – Identify compounds that will form SEI by breaking down before the electrolyte does• Select models – Library of candidate structures based on known additives with modification of pendant groups• Choose computational approach – Modeling entire SEI formation is hard – Requirements for a good additive are easier to calculate: • Increased reduction potential correlates with a lower LUMO or higher electron affinity (EAv) • Measure of stability or reactivity is the chemical hardness of a system (η) • Larger dipole moment leads to stronger dipole-cation interactions (µ) – QM required for these properties • Work by Chung et al., has shown that semiempirical method is effective© 2008 Accelrys, Inc. 33
  • 34. Anode SEI Additive Structure Library X X Z Z X X X X X X X X X X R4 O O X Z X X X R3 X Z O R2 X X X R1 Z X z1 X X X = F or H• Cyclic carbonates, related to ethylene carbonate (EC), are often used as anode SEI additives for use with graphite anodes• To explore the effect of alkylation or fluorination on EC-based additive properties an R-Group based enumeration scheme was used to generate a EC-based additive structure library (7381 stereochemically unique structures)© 2008 Accelrys, Inc. 34
  • 35. Anode SEI Additive Descriptors• Increased reduction potential correlates with a lower LUMO energy value or a higher vertical electron affinity (EAv) ELUMO, EAv• Measure of stability or reactivity is the chemical hardness of a system (η)• Larger dipole moment leads to stronger dipole- cation interactions (µ)• Lots of calculations – Requires neutral, cation, anion for each molecule µ – 1000’s of molecules – Automate computation and analysis with workflow management tools© 2008 Accelrys, Inc. 35
  • 36. Anode SEI Additive Library Results• No one material satisfies all 3 simultaneous objectives• Multi-objective solutions represent a trade-off• Pareto-optimal solutions are defined as a set of solutions such that is not possible to improve one property without making any other property worse• For anode SEI additives Pareto optimal solution is the structure shown© 2008 Accelrys, Inc. 36
  • 37. Li-ion Battery Summary • The generation of virtual structure libraries can be used to explore materials design space • Advanced materials modeling workflows can be captured in pipelined protocols enabling the analysis of virtual materials libraries • Combination of molecular modeling and data analysis can identify leads efficiently • Acknowledgements – Ken Tasaki (Mitsubishi Chemicals Inc.) – Mathew Halls (Accelrys) – Computational resources: HP© 2008 Accelrys, Inc. 37
  • 38. Catalysis • Catalysis is critical to modern chemical industry – 60% of chemical products – 90% of chemical processes Z • A good catalyst will 1 – Make the reaction proceed faster & at lower T Without – Make the reaction run at lower temperature Catalyst – Increase yield Z Ea,0 2 Energy • Catalyst lowers the reaction energy barrier, increases rate Ea,1 Ea,2 R P • Modeling can provide ∆HR – Reaction energies ∆HR With A* – Energy barriers Ea P* Catalyst – Structure of intermediates • Modeling allows you to explore in silico Reaction Coordinate – Effect of catalyst composition – Effect of poisons or promoters – Efficiency of catalyst for alternative R© 2008 Accelrys, Inc. 38
  • 39. Introduction to iCatDesign• Goal: develop combined computational and experimental methods for improved catalysts for oxygen reduction reaction (ORR) in fuel cells• Collaboration with CMR Fuel Cells and Johnson Matthey• Co-funded by Technology Strategy Boards Collaborative Research and Development Programme© 2008 Accelrys, Inc. 39
  • 40. Adsorption and activation energies: ORR E E0=E(O2+*) ETS=E(O*-O*) E1=E(O2*) E2=2E(O*) Reaction coordinate Ediss=E2-E1 Eads1=E1-E0 Ea=ETS-E1 Eads2=E2-E0 Eads1=E1-E0© 2008 Accelrys, Inc. iCatDesign iCatDesign 40
  • 41. Models• Approach: – DFT calculations using plane-wave + pseudopotentials – 5-layer slabs, with 2 bottom layers ‘frozen’ – Calculate reaction barrier for each combination of base & promoter element – Substitute 3 promoter atoms at a time• Initial step: find alloys that bind atomic oxygen more tightly – Observation: center of d-band correlates with Oxygen absorption energy and is faster to calculate• For Pd3Co in 3 layer model, there are 220 unique structures – Most stable corresponds to all Co atoms in the 3rd layer – Other configurations contribute to ensemble average – How do we keep track of all the calculations? Config gi ∆E=E-E0, eV exp(-∆E/kBT), T=300K A0B0C3 4 0.0000 0.643 A0B1C2 24 0.122-0.128 0.986E-02 A0B2C1 24 0.105-0.127 0.0154 A0B3C0 4 0.016 0.331 Total 0.9993© 2008 Accelrys, Inc. 41
  • 42. High Throughput Workflow Calculate stable surface alloy Less expensive calculations: perform many structures Oxygen reduction descriptors - D-band centre positions - Electron work functions … Potential Candidate? Adsorption energies Activation energies Database More expensive calculations: of results perform fewer Predict reaction rates Compare to experiments© 2008 Accelrys, Inc. 42
  • 43. Chemical reactivity and mechanical strain • What causes change of catalytic activity upon alloying? – Electronic properties of base metal are important – Base metal admixture results in mechanical surface strain, which in turn affects its catalytic activity • d-band center and work function are analyzed as functions of unit cell parameters • Using band structure as a guide – d-band should overlap O2 HOMO – Plot of band center shows how to change unit cell parameter to reposition d-band – Alloy with promoters that push the lattice parameter in the right direction! Pt Pd Cu Pt: d-band overlaps O2 Pd: need to compress Cu: need to expand lattice HOMO at equilibrium or lattice to improve d-band to improve d-band smaller values© 2008 Accelrys, Inc. iCatDesign 43
  • 44. Summary of iCatDesign• iCatDesign project resulted in developments of new solutions – Streamlining high throughput QM calculations – Analysis and reporting of large amount of calculated data – New science in Fuel Cells developments and in heterogeneous and electro- catalysis in general© 2008 Accelrys, Inc. 44
  • 45. iCatDesign Acknowledgements• Primary researchers – Accelrys: Jacob Gavartin, Alexander Perlov, Dan Ormsby – Johnson Matthey: Sam French, Misbah Sarwar – CMR Fuel Cells: Dimitrios Papageorgopoulos• Assistance from – Amity Andersen – Alexander Perlov – Alexandra Simperler – Victor Milman – Patricia Gestoso-Suoto – Gerhard Goldbeck-Wood – Julian Willmott – Mark Faller – Jaroslaw Tomczak – Stephane Vellay – Richard Cox© 2008 Accelrys, Inc. 45
  • 46. Environmental Chemistry andToxicology OverviewSome challenges facing industry today:• Inefficiency in collecting analyzing and acting on disparate data• Determine toxicity of new compound – Compile physico-chemical and toxicity data with a minimum of additional testing• Determine if a new compound will break down to toxic by-products• Reduce animal testing© 2008 Accelrys, Inc. 46
  • 47. Environmental Chemistry andToxicology Regulation• Existing U.S. regulations – OSHA – Occupational Safety and Health Administration • Permissible Exposure Limits, Hazard Communication – RCRA – Resource Conservation and Recovery Act • Subtitle C – “Cradle to Grave” chemical tracking – CWA – Clean Water Act • Requires permitting of point source polluters including industrial facilities• European Community REACH – Registration, Evaluation, and Authorization Chemicals * – Guiding principle: “No data, no market.” – Reduce unnecessary experiments using QSAR and read-across – Protect human health and the environment from potentially harmful chemicals and make manufacturers and importers responsible for managing the risks of the chemicals• Revision Looms For U.S. Chemical Law (C&E News, June 9, 2008) – 1976 Toxic Substances Control Act (TSCA) allows EPA to request toxicity data – EPA has no resources or mechanism for collecting this data – Thousands of high-production-volume (HPV) chemicals have no toxicity data – Congressional bills S. 3040 and H.R. 6100 introduced in May would require toxicity data * Environ Health Perspect. 2008 March; 116(3): A124–A127© 2008 Accelrys, Inc. 47
  • 48. Solutions for Toxicology • Statistical data mining • Substructure searching • QSAR-based tools for – Predictive toxicology – Degradation products – ADME products • Data storage and retrieval • Deployment – By combining modeling tools with Pipeline Pilot, a simple web page is presented to a user who enters the structure or name of the compound. That input is seamlessly past to the modeling tool that returns related compounds and their known and predicted toxicity.© 2008 Accelrys, Inc. 48
  • 49. Collect, Analyze, Act on Data• Interactive reports – Ability to analyze specific components over time – Add all components that contain a specific regulated chemistry• Molecular substructures – Automatically search for any compounds that contribute to a regulated endpoint• Higher level reports – Principle compounds in effluent• Lower level reports – Within a given time frame what did the raw analytical results look like© 2008 Accelrys, Inc. 49
  • 50. Identify Degradation Products• Challenge: For a given set of compounds identify the likely breakdown products – Generally monotonous, prone to oversights – Specialized reactions may be missed• ECT-encoded biodegradation pathways – Automatically and systematically process compounds – Any unique pathways can be encoded so the reactions are never overlooked© 2008 Accelrys, Inc. 50
  • 51. Identify Degradation Products• Beyond first-level breakdown products – View the chemical breakdown process with references – Explore toxicity models – Expand to multiple levels of products© 2008 Accelrys, Inc. 51
  • 52. Complete AerobicBiodegradation of Aspirin O O O O O CH3 OH O • •• OH O O OH OH OH OH HO • HO •• •• O O O O HO O O O CH2 O O O O O O O O ••• O O •• •• O O O O O O O O O CH3 CH3 O O O O O O O O •• • • O ••• O •• Mutagenicity • Hepatotoxicity • O O Fathead Minnow • O O O O Solubility • O O •• ••© 2008 Accelrys, Inc. 52 52
  • 53. Predictive Analysis• Get a comprehensive overview of physical, ADME, and toxicological properties – Easy-to-interpret graphical representations showing both calculated properties and business rules – With appropriate authority easily update business rules in response to changing regulations or environmental conditions© 2008 Accelrys, Inc. 53 53
  • 54. Detailed Bayesian Model• Get a full understanding of the model used to predict end-points or other properties – Automatic “learning,” QSAR models – Optimum prediction space (OPS) analysis to ensure results are relevant© 2008 Accelrys, Inc. 54 54
  • 55. Other Reports: Facility Reports• Summary reports that can be live and include historical trends• Drill-down capabilities – Summary data from multiple facilities or teams – Reports for individual chemists – Detailed reporting and analysis on each compound with all assay results© 2008 Accelrys, Inc. 55 55
  • 56. Summary• Environmental health & safety regulations will force companies to maintain accurate records of materials, screening results, effluents, etc• Companies will be responsible for demonstrating the safety of compounds• Simple tools like databases and web reports make it simple to keep track of data• Predictive tools based on QSAR make it possible to predict activity of new compounds© 2008 Accelrys, Inc. 56
  • 57. Conclusions• Software tools have already contributed to easing impact on the environment – Fuel cells – Batteries – Biomass conversion – Catalysts – Toxicology• Tools include – Conventional molecular modeling – Statistical analysis – Reporting• Software is becoming easier & easier to use, but …• Applying some careful thought ahead of time to get the most out of your calculations© 2008 Accelrys, Inc. 57