Author: Gina Wilson

  • AI/ML for Automated Critical Materials Resource Mapping

    Ahamed – INL R&D Staff Scientist

    Research Overview

    Efficient exploration and development of critical mineral resources increasingly depend on integrating large volumes of geologic and geophysical data. Traditional log‑analysis workflows for solution‑mined deposits require detailed interpretation of lithology, stratigraphic boundaries, fluid‑flow‑controlled alteration zones, and grade‑related signals. These processes are often labor‑intensive and subject to interpreter variability.

    This project seeks to apply modern machine learning methodologies to automate essential components of mineral resource mapping. Students will work with representative geophysical and lithologic datasets to:

    • Clean and preprocess well‑log data (e.g., resistivity, natural gamma, spontaneous potential)
    • Correct depth misalignments between lithologic and geophysical logs
    • Build ML models for lithology prediction, stratigraphic correlation, and geologic zonation analysis
    • Investigate pattern recognition methods to identify alteration fronts and mineralization indicators
    • Explore workflow expansion using physics‑informed ML or sparse sensing approaches to improve mapping under limited‑data conditions

    The overarching goal is to develop scalable data‑driven methods that enhance subsurface interpretation for critical materials and solution‑mining applications.

    Student Research Experience

    Students will engage in interdisciplinary work spanning geoscience, machine learning, and data engineering. Activities include:

    • Data Integration & Preprocessing – preparing and aligning multi‑source well‑log datasets, correcting sample‑lag effects, and constructing geologically meaningful features.
    • Machine Learning Model Development – implementing algorithms such as PCA, dynamic time warping, tree‑based models, gradient‑boosting methods, and neural networks to support automated interpretation.
    • Geologic Interpretation – learning how lithologic variation, stratigraphic architecture, and fluid‑alteration signatures relate to mineral resource distribution within solution‑mined systems.
    • Model Evaluation & Visualization – generating correlation panels, prediction plots, and zonation maps to assess machine‑learning results in a geologic context.

    Students will collaborate with INL staff and gain experience applying AI/ML techniques to real‑world subsurface problems relevant to critical materials, energy security, and advanced resource extraction technologies.

    Skills & Techniques

  • High Temperature Molten Salts Chemistry and Electrochemistry

    Zhao- University of Idaho

    Research Overview

    High temperature molten salt systems find extensive application in energy storage and conversion, materials processing, and reprocessing. Molten salt reactors (MSRs) were developed at Oak Ridge National Laboratory in the 1950s–1970s as one of the advanced high temperature nuclear reactor systems offering a potentially safer, more efficient and sustainable form of nuclear power.

    The eutectic alkaline metal fluoride salt mixture LiF-NaF-KF is identified as a promising candidate for molten salt reactors. The corrosion rate and mechanism are key to understanding, selecting, and designing new metal alloys for MSRs. Developing Ni-ODS (oxide dispersion strengthened) alloys can alleviate problems related to swelling and irradiation damage.

    Student Research Experience

    CAES provides a collaborative research environment with a strong safety culture that will nurture undergraduate students for strong leadership in research. Students will be trained following safety procedures and learn how to use a glovebox and handle hot molten salt safely. They will learn how to prepare samples and characterize corrosions.

    Students will learn how to record experimental work and report results professionally and effectively. This project provides a window for REU students to examine molten salt reactors and energy storage systems. Students are expected to review and learn the Working Control Document (WCD) as part of the lab’s rigorous safety culture.

    Skills & Techniques

  • Self-Healing Materials for Extreme Environments

    K.S. Raja – University of Idaho

    Research Overview

    Nuclear and power plant components are exposed to aggressive service conditions — high temperatures, high stresses, irradiation, and corrosion — which lead to damage accumulation and restricted service lifetime. If structural materials can self-heal microscopic damages induced by irradiation, creep, and other degradation mechanisms, their lifetime can be extended considerably.

    In this research, metal-core oxide-shell (MCOS) nano-capsules will be prepared by controlled oxidation of nano-particles of low melting point metals such as Sn and Zn. Using a powder metallurgy approach, the MCOS nano-capsules will be distributed in a metal matrix (preferably low-carbon steel). Test samples will be prepared using different manufacturing techniques, and self-healing behavior will be demonstrated by conducting tensile testing at high temperatures.

    Student Research Experience

    Students will be trained in materials processing, advanced manufacturing, materials characterization, and materials testing. Processing techniques include controlled oxidation of nanomaterials, high-energy ball milling, and high-pressure compaction. Manufacturing processes include conventional sintering and laser powder bed fusion.

    Material characterization techniques include optical and electron microscopy, and confocal Raman spectroscopy. Testing techniques are micro-hardness survey and tensile testing at different temperatures. Students will be trained by working with graduate students and faculty, will learn to critically analyze experimental data using a statistical approach, write reports, and participate in weekly research progress meetings.

    Skills & Techniques

  • Sustainability Modeling of Integrated Energy-Environment Systems

    Cheng – University of Idaho

    Research Overview

    Potential environmental impacts, carbon footprints, and economic feasibility are critical parameters for establishing a sustainable energy system. Securing resource and energy sustainability requires combining various resources for energy conversions. This approach reduces environmental impacts while enhancing efficiency and feasibility of integrated energy systems.

    Computation tools will evaluate carbon footprints, environmental impacts, and economic feasibility of energy production from various resources based on their conversion technologies. Results will be extended to inform energy policy analysis as well as addressing related environmental and energy justice issues — helping discover sustainable energy solutions across energy, resources, economics, and environment perspectives.

    Student Research Experience

    Students will learn the correlation among natural resources, energy conversion, and environmental burden. They will learn the principle of sustainability modeling in the “Environmental Life Cycle Assessment” course and be mentored to use several simulation tools, including SuperPro Designer, GREET, and Open LCA to analyze different energy conversion pathways and interpret results.

    Working with scientists in INL’s Digital Reactor Technology and Development Department using FORCE (Framework of Optimization of ResourCes and Economics) software will enrich students’ experiences. The participant will also be engaged in brainstorming sessions to extrapolate potential solutions to future energy systems.

    Skills & Techniques

  • Biomass-to-Fuel Pipeline for Sustainable Energy-Environment Nexus

    Cheng – University of Idaho

    Research Overview

    The demand for sustainable energy is driving innovations in biorefining technologies to establish biomass-to-fuel pipelines. More than 690 million dry tons of biomass per year are available for fuel conversions, with an estimated value of $70 per dry ton. Integrating various feedstocks — agricultural residues, forest waste, municipal solid waste, algae, and energy crops — is crucial to the resource-energy-environmental nexus.

    In Idaho, MSW, agricultural and forest residues are considered the major resources for energy conversions. Based on the chemical and physical properties of biomass and feedstock available in Idaho, this project aims to define a viable biomass valorization strategy that will facilitate implementation of renewable and biobased energy and thrust the regional circular economy.

    Student Research Experience

    Students will be introduced to the basic science of biorefining technologies, including lignocellulose chemistry, principles of biomass deconstruction, fundamentals of biomass and energy conversions, and innovations of advanced sustainable products.

    Students will work in collaboration with INL’s Biomass Feedstock National User Facility (BFNUF) to perform biomass characterization — understanding chemical composition and physical properties such as structural carbohydrate, lignin, ash, and extractive determinations. Students will also gain experience performing different biomass pretreatment and conversion processes through hands-on experiments at CAES.

    Skills & Techniques

  • Investigating Powder Bed Printing Parameters on Metal Parts Thermal/Mechanical Properties

    Ali – Idaho State University

    Research Overview

    In laser powder bed fusion (LPBF) additive manufacturing processes, layers of powder are raked or rolled on a build plate and selectively melted using energy from a laser beam. The process can create metal parts with intricate geometries that are difficult or impossible to make using conventional techniques. LPBF is employed in aerospace and defense, and has recently been proposed for making advanced nuclear reactor parts.

    Several challenges induce multi-scale flaw formation, including surface inhomogeneous microstructure, porosity, cracking and distortion, residual stress, poor surface roughness, and geometric flaws. Understanding the effects of printing parameters such as laser beam power and scanning speed on surface defects is crucial. Surface defects will be quantified and related to these parameters through measurements of surface energy, wettability, thermal conductivity, and mechanical tests.

    Research Overview

    Working in collaboration with staff at Idaho National Laboratory and Boise State University, students will participate in three key areas: (1) Additive manufacturing — learning from sample design and optimization through down-selection of printing parameters; (2) Surface Characterization using state-of-the-art tools including Goniometer (contact angle), surface profilometer (roughness), and SEM imaging; (3) Performing thermal conductivity and mechanical testing for a series of samples, comparing results to traditionally machined samples.

    Students will also perform data analysis to understand the relationship between surface flaw formation, wettability measurements, and printing parameters to fine-tune and optimize the fabrication process.

    Skills & Techniques

  • Effect of Pressure Resistance Welded Molybdenum Alloy’s Response to Helium Ion Irradiation

    Charit – University of Idaho

    Research Overview

    The overarching goal is to explore and understand the irradiation response of pressure resistance welding (PRW) technology on two molybdenum-based materials: commercially pure molybdenum and an oxide (La₂O₃) dispersion strengthened molybdenum alloy, MoLa. The project aims to understand the effect of helium ion irradiation on the microstructural, microchemical and mechanical properties of these materials.

    There has been significant interest in the potential use of molybdenum-based materials among high melting refractory metals/alloys to produce fuel elements for the nuclear thermal rocket engine as well as accident-tolerant fuel cladding applications in advanced light water reactors. The benefits of molybdenum include its high melting point, high thermal conductivity, elevated temperature mechanical properties, and excellent corrosion resistance against liquid metals and aggressive environments.

    Student Research Experience

    The student will have the unique opportunity to learn about the potential use of refractory metals in terrestrial and extra-terrestrial nuclear reactors, and will participate in hands-on characterization of ion irradiated samples and data analysis. They will gain hands-on experience in scanning electron microscopy, energy dispersive spectroscopy and nanoindentation during the project period.

    They will also have the opportunity to observe operation of a high resolution transmission electron microscope by an instrument scientist. Near the end of the project, the student will present findings in microstructure-properties correlations via a presentation.

    Skills & Techniques

  • Classical Molecular Dynamics Simulations of Nuclear Materials

    Russell – University of Idaho

    Research Overview

    Atomistic simulations are increasingly valuable for industrial needs to predict thermodynamic and kinetic properties of systems at the nanoscale. Design and fitting of interatomic potentials for application to nuclear materials is also an area of fundamental interest. Simulation and modeling can provide understanding of mechanisms that occur far from equilibrium, which can be difficult to directly observe by experiment.

    Accurate prediction of melting temperature and elastic properties at finite temperatures are challenging because interatomic potentials are typically fit to zero temperature electronic structure results. New interatomic potentials based on explainable artificial intelligence concepts are needed that combine physics based functional forms with advanced machine learning techniques to fit parameter sets.

    Student Research Experience

    Working in collaboration with INL’s Collaborative Computing Center, students will learn fundamentals of computational atomistic simulation and modeling. Students will obtain a user account on the NSF ACCESS cyberinfrastructure program and utilize the Bridges-2 supercomputer hosted by the Pittsburgh Supercomputing Center (PSC).

    Students will learn to use high performance computers, the LAMMPS code, visualization with VMD code, scripting, and post processing techniques. They will also learn fundamentals of machine learning to fit parameters and the physics behind interatomic potential design. Advanced students may learn programming languages and parallel computing techniques.

    Skills & Techniques