Más contenido relacionado Similar a Software Methods for Sustainable Solutions (20) Software Methods for Sustainable Solutions2. 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 Results
λ=2
λ=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 optimize
biodiesel 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 to
optimize 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 Film
Formation
• 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 Film
Formation
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 Board's 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 and
Toxicology Overview
Some 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 and
Toxicology 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 Aerobic
Biodegradation 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