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Materials Informatics: Accelerating the Pace of Nano-Manufacturing Process Development Curt M. Breneman*, Mike Krein, Rick Barto and Jason Poleski Nanomaterials for Defense Conference, May 6, 2010. What is Materials Informatics?. According to Wikipedia….
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Materials Informatics: Accelerating the Pace of Nano-Manufacturing Process Development Curt M. Breneman*, Mike Krein, Rick Barto and Jason Poleski Nanomaterials for Defense Conference, May 6, 2010
What is Materials Informatics? According to Wikipedia… • Involves the collection, storage and use of materials information • Holds the promise of prospective materials property prediction… Brinson Advanced Materials Lab
Materials Informatics:Significance and Context • Unified vision of Cheminformatics and Materials Informatics • In terms of fundamental physics of molecular interactions • Builds upon expertise and capabilities of RECCR • Leverages corporate datasets, experimental efforts
Structure of Today’s Talk Traditional Approaches Materials Informatics The Future
Traditional Approaches • “Cut-and-try” strategies were used to find better materials for specific applications • Progress depended on Local Expertise • “Off the shelf” materials improved slowly over time
Design of Application-Specific High-performance Materials Materials with exotic properties are needed Multiple-property optimization is required Existing design methods fail to address these issues This is the driving force for the Paradigm Shift
The Paradigm Shift… • There is a rich history of Cheminformatics development in drug discovery • Enabling advances have taken place in computer software, hardware and data collection methods • Hard lessons were learned - particularly about Descriptors, Model Validation and Domains of Applicability • Then a Key Observation: • Given appropriate descriptors, predictive models of materials properties should be available through the application of these same advances… • The Tipping Point: Materials Informatics can streamline design - • Realization that Cheminformatics approaches to materials design will become standard engineering tools • Cheminformatics modeling and validation methods can be used to optimize materials with exotic properties
Materials Quantitative Structure-Property Relationships (MQSPR) Successful (M)QSPR methods can: Find "hidden" information in chemical data Retrieve structures with well-defined property ranges from a database Identify appropriate descriptor types for each situation Assess model applicability domains Validate modeling results
Quantitative Structure-Property Relationship (QSPR) Modeling • Analysis of variance and identification of relationships within data
Structure-Property Relationships X PROPERTY STRUCTURE Statistical or Pattern Recognition Methods Computational Chemistry MOLECULAR DESCRIPTOR REPRESENTATION
Structure-Property Relationships X PROPERTY STRUCTURE Data Acquisition Statistical or Pattern Recognition Methods Encoded Process Design Parameters
Capture experimental data in one central repository Provides centralized web-based tool for data review and analysis Build repository with considerable input from experimentalists GUI-based analysis interfaces included Perform analysis on experimental data to provide additional guidance Visualization and clustering, linear and nonlinear modeling Next Steps: Build-out analysis capabilities, robustness, GUI design Provide inputs for optimal experimental design Technical Approach
Modeling and Validation DATASET Training set Test set Real Y-scrambling model validation! Bootstrap sample k Predictive Model Training Validation Scrambled Learning Model Tuning / Prediction Prediction
Simple models are better Interpretable models are better Reality: need to balance predictive ability and interpretability Model Parsimony Rules:
Linear and Nonlinear Methods Data Preprocessing Model / Method Validation Domain of Applicability Assessment “Automated” Modeling
http://reccr.chem.rpi.edu/ONR/ “Automated” Modeling
Modeling Workflow Data Analysis Results Experiment Results Multivariate Analyses Inspect Model Results Predict Unknown Cases Experiment Selection Scrutinize Validity of Model Validation /Domain Assessment Test Predictions Data Reduction Expand Database & “Learn” Evaluate, Reselect Data or Descriptors Data Clustering Human Task Descriptor/ Feature Selection Modeling Software Task Legend Satisfactory Unsatisfactory
Smart Material Manufacturing • What to make next? • How to make it? • With what confidence? Results Predict Unknown Cases Test Predictions Expand Database & “Learn”
Inverse QSAR Approach • First Steps: Beyond “Sensitivity Analysis”: (Now) • Build models from existing data and find important parameters • Alter identified parameters for the best experimental data points • Make predictions on “new” data points using existing models • Create materials and compare properties with predictions Validation Do Prediction Run Experiment If Results Don’t Match Prediction, Why?
Inverse QSAR Approach • Next Steps: Making it Useful ! (Just on horizon) • • The most interesting materials usually • aren’t the best performing in one category • • User selects the experimental parameters to alter for “what if” analysis • • Logical extension of this is multi-objective QSAR and iQSAR Validation Do Prediction Run Experiment If Results Don’t Match Prediction, Why?
The RECCR Community http://reccr.chem.rpi.edu
ACKNOWLEDGMENTS Current and Former members of the RECCR/DDASSL group Breneman Research Group (RPI Chemistry) N. Sukumar M. Sundling Min Li Long Han Jed Zaretski Taowei Huang Theresa Hepburn Mike Krein Steve Mulick Shiina Akasaka Hongmei Zhang C. Whitehead (Pfizer Global Research) L. Shen (BNPI) L. Lockwood (Syracuse Research Corporation) M. Song (Synta Pharmaceuticals) D. Zhuang (Simulations Plus) W. Katt (Yale University chemistry graduate program) Q. Luo (J & J) Embrechts Research Group (RPI DSES) Tropsha Research Group (UNC Chapel Hill) Bennett Research Group (RPI Mathematics) Collaborators: Brinson Advanced Materials Lab – (Northwestern) Schadler Research Group (RPI Materials Engineering) Funding Office of Naval Research (ONR) Lockheed-Martin (LMCO) NIH (GM047372-07) NIH (1P20HG003899-01) NSF (BES-0214183, BES-0079436, IIS-9979860) GE Corporate R&D Center Ford-Boeing Alliance NSF NIRT Program Chemical Computing Group (CCG)
