Multiscale Modeling and Materials by Design Group

Vision

To advance the understanding and application of materials science by bridging the gap between fundamental research and industrial applications, with a focus on innovative materials and scalable technologies, enabled by multiscale modeling, artificial intelligence/machine learning (AI/ML), and leadership-class computing. 

Mission

To develop and implement multiscale approaches to materials science, from atomic-level properties to macroscopic behavior, enabling the creation of advanced materials for electronic, magnetic, quantum, and structural applications through cutting-edge research, AI/ML and collaboration with industry and other DOE facilities. 

R&D Scope

The Multiscale Modeling and Materials by Design (M2MD) Group is dedicated to pioneering research that integrates fundamental science with practical applications. Our work involves the computational and theoretical design of functional and structural materials, with particular emphasis on those operating under extreme conditions. This includes the development of nuclear materials, scalable microstructure simulations for additive manufacturing, and solidification processes, and digital-twin frameworks for predictive manufacturing performance. By addressing the current needs of applied programs, we aim to understand atomic-level properties and develop multiscale approaches that seamlessly integrate nanoscale phenomena with macroscale applications. Our research also explores materials with unique quantum properties, with the goal of exploiting these properties for multifunctional applications in diverse fields. 

In addition, we use high-throughput methods and AI/ML-driven workflows to develop predictive models that accelerate the discovery of new materials. Our research spans multiple scales, both temporal and spatial, to create comprehensive models and simulations that inform materials design and application. Our work is highly collaborative, and we benefit greatly from interactions with other groups in our section, division, and across ORNL, including classical high-performance computing (HPC) resources and quantum computing (QC) resources at DOE facilities such as the Oak Ridge Leadership Computing Facility. By leveraging HPC and QC resources and collaborative efforts, we aim to advance the field of materials science and contribute to the development of innovative materials for a wide range of industrial applications. 

Core Competencies

  • Multiscale Modeling and Materials by Design: Bridging basic science and applied research by understanding properties at the atomic level and developing multiscale approaches that link the nanoscale to the macroscale 
  • Computational and AI-Enabled Theoretical Design: Integrating first-principles methods, data analytics, and ORNL-developed AI/ML tools for predictive materials discovery  
  • Quantum and Electronic Materials: Focusing on materials with multifunctional quantum properties, as well as electronic and magnetic materials for diverse applications 
  • Energy and Structural Materials: Lightweight alloys, energy-storage materials, electrical transmission systems, and next-generation high-performance devices 
  • Advanced Manufacturing and Digital Twins: Development of scalable microstructure simulations for additive manufacturing and solidification processes, and predictive quality frameworks 
  • Nuclear and Extreme-Environment Materials: Advancing the development of materials designed for extreme conditions and nuclear applications 
  • High-Throughput and AI Approaches: Leveraging high-throughput methods and AI/ML for predictive theory approaches and materials design across multiple time and space scales