Materials, Chemistry and Nanoscience

1. erhtjhtyhy Co-Leads Bert de Jong– Lawrence Berkeley National Laboratory Bobby G. Sumpter – Oak Ridge National Laboratory Markus Eisenbach – Oak Ridge National Laboratory Participants – 42 with full list in the xls. Materials, Chemistry and Nanoscience

2. Physical model & understanding Materials, Chemistry and Nanoscience Rational design of materials/chemicals Autonomous smart synthesis Smartly integrated and federated experimental instruments and computational resources

3. Accelerated knowledge discovery for materials and chemistry Vision Materials, Chemical, Nano challenges an analytical step to confirm that the target chemicals and/or materials are produced; characterization of the physical properties, morphologies, defects and interfaces of the materials by multiple probes/techniques; characterization of the functional properties, in-situ/operando, in devices. On-the-fly analysis and active learning with integrated theory and modeling during an experiment for maximizing information gain, including in-situ multimodal analysis Data & machine learning challenges Potential impact Automated optimization of analysis across multimodal platforms, including registration and scaling for structure-property mapping Integrating information for modeling and simulations for interpretation and decision-making during experiments AI/ML to automate the model-building, model-testing (Bayesian analysis) and surrogate modeling Data workflow architecture (compute, storage, network, algorithms, middleware, and software) to best handle “diverse data” in-situ. • Enhancing new and existing theoretical and experimental capabilities to accelerate development of materials • Unique to DOE: including supercomputers, experimental science facilities and Esnet • Unlock the potential of in-situ/streaming data 3

4. Autonomous instruments for materials Vision Materials, Chemical, Nano challenges • Metastable phases and materialsthat persist out of equilibrium. • Interfacial processes and properties • Harnessing heterogeneity in complex systems • Materials for quantum information sciences Opportunity: AI/ML integrate all aspects of the materials discovery loop—from material preparation, through characterization, to data interpretation and feedback to minimize the experimental trials needed to achieve desired properties Data & machine learning challenges Potential impact AI/ML infrastructure to automate the model-building and decision-making aspects of the experimental loops to enable machine-guided synthesis, processing, and ultimately materials discovery. Automation of aspects of experiments: tuning environment, importance sampling, next-experiment recommendation, etc. • Access to diversity of properties beyond the limits drawn by equilibrium thermodynamics • Multifunctional and self-regenerating catalytic systems • Controlling interfaces  interface design 4

5. Rational Design of Materials/Chemicals Materials, Chemical, Nano challenges Scientific challenges • Design of unknown materials to satisfy certain design requirements • What is the search space? Combinatorically large for material and molecular design! Defining suitable submanifold for search. • Interpretable design rules • Scale hierarchy of material properties • Incorporation of physical constraints into ML models • Rational design of drug molecule without side effects / Patient specific drug design • High Tc Superconductor design • Quantum Materials Design • Catalyst design Potential impact Basic research requirements • Active learning/learning in high dimensional parameter spaces • Validation, verification and UQ of surrogate models • Improved modeling techniques to reduce unintended constrains • Control of data acquisition and generation • Materials with improved capabilities for energy and information applications • Enabling new applications, e.g. in quantum computing • On the fly materials design and synthesis 5

6. Database, Uncertainty and Validation for Autonomous-Smart Synthesis • Creating a support structure that enables and incentives open source database and challenge problems for prototypical synthesis. • Validation simulations and characterize uncertainty for prototypical synthesis • Advanced learning method that can predict behavior for combinatory large input spaces and complex output domain that describe structures and physical dynamics of materials. • Methods that can produce distributions of outliers and rare events in synthesis that can possibly provide discovery of new synthesis pathways. • Identify and characterize new synthesis pathways with uncertainty in synthesis step, describing possible alternatives to step with long time scales. • Validation occurs through well sampled set of test cases that exercise predictive methods across the range of material properties and characteristics • Capture new fundamental understanding from machine learning structure