Chapter 1: United States MI Projects

The United States has been at the forefront of Materials Informatics (MI) development since the launch of the Materials Genome Initiative in 2011. This chapter provides a comprehensive overview of major US government-funded programs, national laboratory initiatives, and academic-industry collaborations that have shaped the global MI landscape.

1.1 Materials Genome Initiative (MGI)

The Materials Genome Initiative (MGI) is a multi-agency initiative launched by the White House Office of Science and Technology Policy (OSTP) in June 2011. It represents the most comprehensive government-led effort to accelerate materials discovery and deployment.

1.1.1 Initiative Overview

1.1.2 Participating Agencies

MGI coordinates efforts across multiple federal agencies:

• National Science Foundation (NSF): Fundamental research and education
• Department of Energy (DOE): Energy materials and computational resources
• National Institute of Standards and Technology (NIST): Standards and data infrastructure
• Defense Advanced Research Projects Agency (DARPA): High-risk/high-reward projects
• Department of Defense (DoD): Defense-related materials
• National Aeronautics and Space Administration (NASA): Aerospace materials
• National Institutes of Health (NIH): Biomaterials

1.1.3 Key Outputs and Infrastructure

1.1.4 MGI Strategic Framework

1.2 DMREF (NSF)

The Designing Materials to Revolutionize and Engineer our Future (DMREF) program is NSF's primary contribution to the Materials Genome Initiative, focusing on fundamental research that integrates theory, computation, and experiment.

1.2.1 Program Overview

1.2.2 Program Objectives

DMREF supports research that:

• Develops new theoretical and computational approaches for materials design
• Creates feedback loops between computation, synthesis, and characterization
• Establishes materials design principles that can be broadly applied
• Trains the next generation of materials researchers in integrated approaches

1.2.3 Research Focus Areas

1.2.4 Representative DMREF Funded Projects (2015-2024)

The following table presents representative DMREF-funded projects that exemplify the program's integrated computational-experimental approach to materials discovery:

1.3 ARPA-E Programs

The Advanced Research Projects Agency-Energy (ARPA-E) funds high-risk, high-reward energy technology projects, including several materials-focused programs that leverage MI approaches.

1.3.1 Agency Overview

1.3.2 Materials-Focused Programs

HESTIA (High-Energy Storage Through Advanced Materials)

• Budget: $39 million
• Focus: Next-generation thermal energy storage materials
• MI Component: Computational screening of phase-change materials

MAGNITO (Magnetic Materials for Grid Technologies)

• Focus: Advanced magnetic materials for grid-scale applications
• MI Component: Machine learning for rare-earth-free magnet discovery

ULTIMATE (Ultra-Lightweight Technologies for Innovative Materials and Advanced Transportation Efficiency)

• Focus: Lightweight structural materials for transportation
• MI Component: High-throughput computational screening of alloys

1.3.3 Impact Metrics

1.3.4 ARPA-E DIFFERENTIATE Program Projects (2019-2024)

The DIFFERENTIATE (Design Intelligence Fostering Formidable Energy Reduction Through Integrated AI and Experimentation) program specifically funds AI/ML approaches for energy materials discovery:

1.4 Materials Project (LBNL)

The Materials Project is one of the most influential open-access databases in materials science, hosted at Lawrence Berkeley National Laboratory (LBNL). It provides computed materials properties using density functional theory (DFT) calculations.

1.4.1 Database Overview

1.4.2 Available Data and Properties

The Materials Project provides the following computed properties:

• Thermodynamic Properties: Formation energy, stability (energy above hull), decomposition products
• Electronic Properties: Band gap, band structure, density of states
• Mechanical Properties: Elastic constants, bulk modulus, shear modulus
• Magnetic Properties: Magnetic ordering, magnetic moments
• Structural Properties: Crystal structure, space group, lattice parameters
• Phonon Properties: Phonon band structure, thermal properties

1.4.3 API and Tools

# Requirements:
# - Python 3.9+
# - mp-api>=0.30.0

"""
Example: Querying Materials Project for battery materials

Purpose: Demonstrate Materials Project API usage
Target: Intermediate
Execution time: 10-30 seconds
Dependencies: mp-api
"""

from mp_api.client import MPRester

# Initialize with your API key (get from materialsproject.org)
with MPRester("YOUR_API_KEY") as mpr:
    # Search for lithium-containing oxides with band gap > 2 eV
    results = mpr.summary.search(
        elements=["Li", "O"],
        band_gap=(2, None),  # Band gap greater than 2 eV
        fields=["material_id", "formula_pretty", "band_gap", "energy_above_hull"]
    )

    print(f"Found {len(results)} materials")

    # Display top 5 results
    for doc in results[:5]:
        print(f"ID: {doc.material_id}")
        print(f"  Formula: {doc.formula_pretty}")
        print(f"  Band Gap: {doc.band_gap:.2f} eV")
        print(f"  Energy Above Hull: {doc.energy_above_hull:.4f} eV/atom")
        print()

# Example output:
# Found 2847 materials
# ID: mp-1234
#   Formula: Li2O
#   Band Gap: 5.02 eV
#   Energy Above Hull: 0.0000 eV/atom

1.4.4 Impact on the Field

• Citation Impact: Original paper cited 10,000+ times
• Discovery Examples: Used to discover new battery cathode materials, thermoelectrics, and photocatalysts
• Industry Adoption: Used by major companies including Toyota, Samsung, and BASF
• Educational Use: Integrated into university curricula worldwide

1.5 MICCoM (DOE)

The Midwest Integrated Center for Computational Materials (MICCoM) is a DOE-funded center focused on developing and applying computational methods for materials discovery.

1.5.1 Center Overview

1.5.2 Research Focus Areas

• Solar Energy Materials: Perovskites, organic photovoltaics, tandem cells
• Battery Materials: Solid-state electrolytes, high-capacity cathodes
• Thermoelectric Materials: High-efficiency thermoelectrics for waste heat recovery
• Method Development: Beyond-DFT methods, machine learning potentials

1.5.3 Computational Capabilities

MICCoM leverages DOE supercomputing resources including:

• Aurora: Exascale supercomputer at Argonne (2+ exaflops)
• Polaris: GPU-accelerated system for AI/ML workloads
• ALCF: Argonne Leadership Computing Facility resources

1.6 ORNL Autonomous Chemistry Lab

Oak Ridge National Laboratory (ORNL) operates an Autonomous Chemistry Laboratory that combines AI-driven planning with robotic synthesis for accelerated materials discovery.

1.6.1 Facility Overview

1.6.2 Key Capabilities

• Robotic Synthesis: Automated preparation and processing of materials
• AI Planning: Machine learning algorithms for experiment design
• Real-time Characterization: In-situ monitoring of synthesis outcomes
• Closed-loop Optimization: Iterative refinement based on experimental feedback

1.6.3 Notable Achievements

1.7 NIST Materials Data Infrastructure

The National Institute of Standards and Technology (NIST) plays a crucial role in MGI by developing standards, reference data, and infrastructure for materials data.

1.7.1 NIST MGI Role

1.7.2 Key Infrastructure Projects

Materials Resource Registry (MRR)

• Catalog of materials data resources
• Standardized metadata for data discovery
• Links to distributed data repositories

CALPHAD Databases

• Thermodynamic data for phase diagram calculations
• Critically assessed experimental data
• Integration with computational tools (Thermo-Calc, PANDAT)

Materials Data Curation System (MDCS)

• Tools for standardized data entry and validation
• XML schema for materials data
• Support for FAIR data principles

1.8 Comparison of US MI Projects

The following table provides a comprehensive comparison of major US Materials Informatics initiatives:

1.9 Timeline of US MI Development

The following diagram illustrates the chronological development of Materials Informatics in the United States:

1.10 Chapter Summary

The United States has established a comprehensive ecosystem for Materials Informatics through coordinated government initiatives, national laboratory programs, and academic research. Key takeaways include:

Practical Recommendations

• For Researchers: Leverage Materials Project and DMREF funding opportunities for data-driven research
• For Industry: Engage with ARPA-E programs for high-risk technology development
• For Students: Build skills in both computation and experiment to align with MGI goals
• For International Collaborators: Partner with US institutions through established exchange programs

Exercises

from mp_api.client import MPRester

with MPRester("YOUR_API_KEY") as mpr:
    results = mpr.summary.search(
        elements=["Fe", "O"],
        band_gap=(1.5, 3.0),
        energy_above_hull=(0, 0.001),  # Essentially stable
        fields=["material_id", "formula_pretty", "band_gap"]
    )

    print(f"Found {len(results)} stable Fe-O compounds with band gap 1.5-3.0 eV")
    for doc in results[:10]:
        print(f"  {doc.formula_pretty}: {doc.band_gap:.2f} eV")

# Typical result: ~50-100 materials depending on database version

References

1. National Science and Technology Council. (2021). Materials Genome Initiative Strategic Plan 2021. White House Office of Science and Technology Policy.
2. Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., Persson, K. A. (2013). Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1), 011002.
3. National Science Foundation. (2024). DMREF: Designing Materials to Revolutionize and Engineer our Future. NSF Program Solicitation 24-529.
4. ARPA-E. (2024). ARPA-E Impact Report 2024. U.S. Department of Energy.
5. Curtarolo, S., Hart, G. L., Nardelli, M. B., Mingo, N., Sanvito, S., Levy, O. (2013). The high-throughput highway to computational materials design. Nature Materials, 12(3), 191-201.
6. de Pablo, J. J., Jackson, N. E., Webb, M. A., Chen, L. Q., Moore, J. E., Morgan, D., Jacobs, R., Pollock, T., Schlom, D. G., Toberer, E. S., Analytis, J., Dabo, I., DeLongchamp, D. M., Fiez, G. A., Grason, G. M., Hautier, G., Mo, Y., Rajan, K., Reed, E. J., ... Ward, L. (2019). New frontiers for the materials genome initiative. npj Computational Materials, 5(1), 41.