Theory, Computational Modeling, and Simulation

Theory, computation, and simulation are foundational to modern energy research. Theoretical understanding reveals why materials and systems behave as they do, predicting performance before experiments are conducted and identifying promising directions for exploration. Computational modeling and simulation translate theoretical frameworks into practical tools that can predict material properties, optimize device designs, simulate system behavior, and explore parameter spaces far larger than experimental approaches could address. This close integration of theory, computation, and experiment accelerates discovery: theorists predict what might work, computational models refine and test predictions, experiments validate and provide feedback that improves models, creating a cycle of increasingly accurate understanding and more effective technologies.

RASEI's theoretical and computational work spans an extraordinary range, from quantum mechanical calculations of individual atoms and molecules to grid-scale simulations of electricity systems, from fundamental physics of fusion plasmas to economic models of technology adoption. This breadth reflects energy research's multi-scale nature: atomic-level material properties determine device performance, device characteristics affect system behavior, and system dynamics influence economic and social outcomes.

Fundamental theory: understanding from first principles

Theoretical work develops the mathematical frameworks and physical models that describe how energy systems behave. This includes understanding electronic structure and bonding in materials (what makes certain molecules good catalysts or semiconductors?), developing theories of charge and energy transport (how do electrons, ions, or photons move through materials and devices?), modeling chemical reaction mechanisms and kinetics (what pathways do reactions follow and what limits their rates?), and creating theoretical frameworks for complex systems (how do many interacting components produce emergent behavior?).

Quantum mechanics and electronic structure calculations

At the smallest scales, quantum mechanical calculations reveal how atoms bond together, how electrons are distributed in molecules and materials, and how these electronic structures determine properties. RASEI researchers use density functional theory (DFT) and related quantum mechanical methods to predict material properties before synthesis, calculating bandgaps for new semiconductors and predicting catalytic activity of different molecular structures.

This computational work directly supports RASEI's materials research. Before synthesizing a new catalyst, solar material, or battery electrode, researchers can computationally screen ideas, identifying the most promising options for experimental validation. This can dramatically accelerate materials research.

Molecular dynamics and materials simulation

Molecular dynamics (MD) simulations track how atoms move and interact over time, revealing behavior at scales between quantum mechanics (which treats individual molecules but becomes computationally prohibitive for large systems) and continuum models (which describe bulk properties but miss atomic-scale details).

RASEI uses MD to simulate how polymer chains pack together (affecting conductivity in organic semiconductors), how ions move through battery electrolytes or fuel cell membranes (determining device performance), how molecules interact at catalyst surfaces (revealing reaction pathways), and how materials respond to stress, temperature changes, or chemical exposure (predicting degradation and lifetime).

These simulations provide insights difficult or impossible to obtain experimentally, revealing atomic-scale dynamics that happen too fast for direct observation, exploring extreme conditions, and testing how modifications affect behavior before investing in synthesis.

Device physics modeling

Moving from materials to devices requires models that simulate how components work together. Device physics simulations model charge transport through layers in solar cells (identifying where energy losses occur), current and voltage distributions in batteries (optimizing electrode and electrolyte design), light propagation and absorption in photonic devices (maximizing efficiency), and heat generation and dissipation (preventing overheating and degradation).

These models connect material properties to device performance, revealing how device architecture affects outcomes. Researchers can virtually test device configurations, different layer thicknesses, material combinations, geometries, identifying optimal designs before fabrication.

Grid and energy system modeling

At the largest scales, RASEI researchers model electricity grids and energy systems. Grid simulations track power flows through transmission and distribution networks, test how systems respond to disturbances or equipment failures, optimize placement and operation of distributed resources (solar, storage, flexible loads, storage), and evaluate different grid configurations and control strategies.

Energy system models examine broader questions: how much solar, wind, and storage capacity is needed to meet demand reliably, what happens to grid stability as variable generation increases, how do electricity markets and pricing affect technology deployment, and what infrastructure investments provide greatest benefit. These models inform both technical research priorities and policy analysis.

Control systems and optimization

Many energy systems require active control, essentially adjusting operations in real-time to optimize performance. RASEI researchers develops control algorithms for wind turbine placement and operation (reducing wake effects where one turbine's airflow disrupts downstream turbines, maximizing total energy capture), building heating and cooling systems (minimizing energy use while maintaining comfort), grid-connected batteries (providing grid services while serving primary functions), and power electronics (optimizing voltage conversion efficiency).

These control problems often involve many interacting variables and competing objectives. Computational optimization methods find solutions that balance tradeoffs, such as operating a battery to maximize owner savings while providing grid stability services, or controlling building systems to minimize energy costs across varying electricity prices and weather conditions.

Machine learning and AI applications

Emerging AI and machine learning tools are being integrated into RASEI's computational research in several ways:

Materials discovery acceleration: ML models trained on existing materials data can predict properties of new compositions much faster than quantum mechanical calculations, enabling rapid screening of vast chemical spaces. RASEI researchers are developing ML models that suggest promising catalyst compositions, predict solar cell performance from material properties, and identify stable battery electrode materials.

Pattern recognition in experimental data: ML algorithms can identify patterns in complex datasets from microscopy, spectroscopy, or device testing, finding correlations humans might miss and accelerating analysis of large experimental datasets.

Grid forecasting and control: Machine learning models predict solar and wind generation based on weather forecasts, anticipate electricity demand patterns, and optimize grid operations in real-time based on current conditions and predicted near-term changes.

This is emerging work at RASEI, the tools are powerful but are in development and require careful validation, substantial training data, and understanding of when ML predictions are reliable versus when detailed physics-based calculations are necessary.

Validation and the theory-experiment cycle.

Computational predictions are only useful if accurate. RASEI's computational work maintains close connections with experimental research to validate models and improve accuracy. Experimental measurements test computational predictions, revealing where models succeed and where they need refinement.

Different computational methods face different challenges. Quantum mechanical calculations are highly accurate but computationally expensive, detailed calculations on large systems can require weeks on powerful computers. Molecular dynamics balances accuracy and speed but still becomes prohibitively expensive for very long timescales or large systems. Device and system models must balance detail (which increases accuracy but computational cost) against speed (allowing exploration of many scenarios).

RASEI researchers work at the frontier of these tradeoffs developing more efficient algorithms, leveraging high-performance computing resources, and choosing appropriate methods for each problem. Sometimes a fast approximate calculation that can explore thousands of options provides more insight than a single very accurate calculation.

Theory, computation and simulation aren't isolated activities, they are interwoven into the efforts across the RASEI research enterprise and enable and accelerate efforts across all RASEI impact areas. By providing theoretical understanding and computational tools that predict, explain, and optimize, this work amplifies the impact of the experimental and engineering work throughout the institute.