Software Methods for Sustainable Solutions

Software Tools for
Development of Sustainable
Solutions

George Fitzgerald, Ph.D.

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

Outline

• Overview of software methods
• Solutions for Alternative Energy
• Solutions for Catalysis
• Solutions for Toxicology
• Demos


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


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


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


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


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


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


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


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


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)


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


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


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

Mesocale Modeling Results

λ=2

λ=8

Mean squared difference of concentration
from average concentration, i.e., a measure
of phase separation.


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)


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


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


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


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)


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.


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


Potential Energy Diagram

Potential H-H bond breaking
Energy
Al-H bond formation

Distance

Physisorption well
H H

Al13

Chemisorption well
H H

Al13


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


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


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.

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.

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.


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


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

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


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


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)

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


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


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


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


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


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

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

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

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

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


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


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


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

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.


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


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


Identify Degradation Products

• Beyond first-level breakdown products
– View the chemical breakdown process with references
– Explore toxicity models
– Expand to multiple levels of products


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
•• ••
52

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

53

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

54

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

55

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


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


Software Methods for Sustainable Solutions

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