Center Projects: Advancing Science Through Computation
At the Center for Advanced Computational Molecular Science (CACMS), our research focuses on accelerating scientific discovery across chemical, biological, and materials systems. We are fundamentally transforming how problems are solved by combining high-level theory with cutting-edge computational power.
Our work is organized into four major thematic areas, each driving innovation and solving critical challenges in health and engineering.
1. Core Theoretical Foundations: Quantum & Electronic Structure
This pillar represents the bedrock of our scientific accuracy. It involves using the most rigorous, fundamental laws of physics and quantum mechanics to model molecules and reactions with atomic precision.
The Focus: We generate first-principles knowledge, making predictions about molecular behavior, stability, and reactivity that are impossible to obtain through traditional laboratory means. This foundational research informs all other activities within the Center.
- Key Expertise: Quantum Dynamics, Electronic Structures.
- Impact: Unlocking the mechanisms of complex chemical reactions, predicting the properties of novel materials, and establishing the true stability of new compounds.
2. Applied Design and Optimization: Multiscale & CAMD
This area focuses on applying our theoretical understanding to practical problems, translating atomic-level data into usable, macro-level insights for development. This is where we execute the rational design process.
The Focus: We use large-scale simulations and modeling techniques to bridge vast differences in size and time—from picosecond bond breaking to long-term drug movement within the body. This approach is essential for Computer-Aided Molecular Design (CAMD).
- Key Expertise: Multiscale Modeling, Computer-Aided Molecular Design (CAMD).
- Impact: Optimizing the therapeutic efficacy of potential drugs, predicting how pharmaceuticals interact with biological systems (ADME properties), and designing molecules with tailored functions.
3. Discovery and Data: Informatics & AI/ML
Our third pillar integrates modern data science to accelerate the entire discovery pipeline. By leveraging Chemical Informatics and sophisticated AI, we can navigate the massive landscape of possible molecules more efficiently than ever before.
The Focus: We use machine learning (ML) and artificial intelligence (AI) not only to analyze massive databases of chemical information but also to generate entirely new molecular structures that meet specific design criteria.
- Key Expertise: Chemical Informatics, AI-assisted Biomolecular Structures.
- Impact: Rapid lead compound discovery, high-throughput virtual screening, and creating novel therapeutic agents through generative chemistry.
4. Applied Mathematics and Statistics
Applied mathematics is a mainstay of computational molecular science, touching most areas of computing in traditional chemistry departments as well as affiliated areas like biology and engineering amongst others. Applied mathematics transforms abstract concepts into concrete testable models, is inextricably connected with data science and computational science and is a bedrock of all work being done in CACMS.
Statistics similarly embraces all science, engineering and technology disciplines. It provides guard rails allowing us to better understand natural phenomena in a meaningful way, even if some concepts are not intuitive. By dealing with variability, managing uncertainty, predicting future outcomes or forecasting trends, it is a principled way to stay centered in our thinking of the natural phenomena we study.
The Focus: Mathematics and statistics constitute the lingua franca connecting the research subdisciplines represented in this Center and allows us to communicate effectively with scientists, engineers and technologists external to CACMS.
- Key Expertise: New mathematics for: quantum dynamics, generalizing Nosé’s thermostat, tensor network theory; new implementations of statistics in: molecular dynamics simulations, molecular modeling, machine learning and AI.
- Impact: Influences all our research; enforces rationality for inherently irrational human interpretations; imposes constraints on what we consider valid or not, and provides a reality check on both our biases and our proclivity to over interpret our results. It has been of utmost importance for our prior successes and is a guiding beacon for future work.