Osvaldo Yáñez Osses

TITLE:

Hybrid Computational Algorithms for Molecular Structure Optimization and Global Minimum Search

ABSTRACT:

The exponential growth of potential energy surface (PES) complexity presents a fundamental challenge in computational chemistry, with local minima scaling dramatically as molecular size increases. This presentation introduces a revolutionary hybrid algorithmic framework that addresses Levinthal’s paradox through innovative minimum hunting strategies. Our methodology uniquely combines probabilistic cellular automata [1] with intelligent agents and genetic algorithms, enabling systematic exploration of complex molecular conformational spaces. The framework integrates Moore and Von Neumann neighborhood concepts from cellular automata theory with memetic algorithms and agent-based models to achieve robust global optimization. Novel reactivity descriptors including Kick-Fukui [2] and Kick-MEP [3] algorithms leverage topological analysis of Fukui functions and molecular electrostatic potential surfaces to guide intelligent structure sampling. These constraint algorithms demonstrate remarkable efficiency in identifying global minima for diverse systems, from silicon and water clusters to organometallic complexes and catalytic systems. Applications span atomic clusters, molecular assemblies, and environmental pollutant adsorption studies, showcasing the methodology’s versatility. This integrative approach represents a paradigm shift from traditional deterministic methods toward statistically-driven molecular design, offering unprecedented capabilities for high-throughput PES exploration and realistic structure prediction in computational chemistry. References: [1] J. Chem. Theory Comput. 2019, 15, 2. [2] J. Chem. Inf. Model. 2021, 61, 8. [3] J. Mol. Model. 2024, 30, 369.

BIO:

Osvaldo Yáñez Osses is an associate professor at the University of Las Américas, Faculty of Engineering and Business. He, is currently a member of the Center for Environmental Modeling and System Dynamics (CEMADIS). He is a programmer and developer of different algorithms for exploring the potential energy surface in atomic and molecular clusters. Osvaldo obtained his Bioinformatics Engineering degree in 2013 from the University of Talca, and his PhD in Molecular Physical Chemistry in 2018 from Andrés Bello University. He completed his postdoctoral studies at the Center for New Drugs for Hypertension and Heart Failure in 2020. Osvaldo’s main lines of research are computational chemistry, evolutionary algorithms, and machine learning for molecular engineering.