2024 AIChE Annual Meeting

Session: Poster Session: Computational Molecular Science and Engineering Forum

Poster Session: Computational Molecular Science and Engineering Forum

Chair

Ferguson, A., University of Chicago

Co-Chairs

Howard, M., University of Texas At Austin
Sampath, J., University of Florida

Presentations

(169ar) Capturing Realistic Double Layer Cation Properties in Classical MD Using Aimd-Guided Potential Fitting
(169bc) Origin of the Stokes-Einstein Deviation in Liquid Al-Si
(169f) Utilizing an Integrated Experimental and in silico Approach to Engineer Cross Reactive Antibodies Against VEGFA and PlGF2 in Pediatric Glioblastoma
(169cy) Unsupervised Computational Analysis of Major Histamine Complex II-Binding Epitope Sequences
(169q) Investigating Phase Separation Behavior of Multi-Domain and Intrinsically Disordered Proteins with a Coarse-Grained Model.
(169aa) A High-Throughput Screening Study to Design Ultra-High Performance Aramid Copolymers
(169m) Using ML to Determine the Optimal Set of Operational Parameters of the RO System in a Regional Water Treatment Plant
(169ax) Using Deep Learning to Accelerate the Molecular Simulations and Predict the Kinetics of RNA Folding
(169dd) Cross-Database Discovery of Metal-Organic Frameworks with Open Cu(II) Sites for Biogas Upgrading through Machine Learning
(169as) Adsorbate Effects on the Mobility of Single Atom Catalysts: Using DFT to Map out Diffusion Potential Energy Surfaces
(169cs) Advancing Molecular Screening: Integrating Operating Conditions into Transformer Models for Chemical Engineering
(169bh) Computational Investigation of Reaction Coordinate Optimality for Ice Nucleation Studies
(169bt) Combining Forward and Inverse Design of Covalent-Organic Frameworks for Methane Storage Via Data-Driven Discovery
(169bg) Can Classical Nucleation Theory Describe Heterogeneous Crystal Nucleation on Non-Uniform Surfaces?
(169cw) Integrating Machine Learning with Evolutionary Algorithms to Design and Discover High-Performing MOFs for Methane Adsorption Using Building Blocks and Crystal Information
(169at) Parametrization of Monatomic Ion-Biomolecular Interactions in the Polarizable Drude Force Field: Application in Protein and Nucleic Acid Systems
(169ac) Active Learning of Density Functionals with Error Control
(169bl) Maxwell-Stefan Diffusivities of Oil-CO2 Mixtures in Nanopores: Physics and Machine Learning Models
(169bp) Theoretical Approach to Elucidating the Dynamics Behavior of PFAS at the Water and Hydrophobic Deep Eutectic Solvents Interphase
(169bq) A Nanoscopic Explanation on Hydrophobic Deep Eutectic Solvents and Their Carbon Dioxide Solubility Performance at High-Pressure.
(169cj) Molecular Design of High Performance Electrolytes with Generative Algorithm
(169ah) Uncovering Residue-Level Driving Forces Underlying the Formation of Biomolecular Condensates
(169ab) Integrating Biophysical Modeling and Machine Learning to Discover Plastic-Binding Peptides for Microplastic Remediation
(169bu) Hydrophobic Deep Eutectic Solvents As Extraction Agents of Nitrophenolic Removel from Water
(169d) Exploring the Role of Functional Groups and Nanoconfinement on the Structural and Dynamical Properties of Water and Ions inside Metal-Organic Frameworks
(169br) Effect of Maturity on the NMR Relaxation of Kerogen Using MD Simulations
(169di) Using Enhanced Sampling Methods to Elucidate the Mechanism of Noncanonical Redox Cofactor Dependent Engineered Enzymes
(169cb) Development of Chimes Machine Learned Interatomic Potentials for All Silica MFI Zeolites
(169ag) Enhanced Shockwave Synthesis through Accurate Silicon Modeling Using Chimes
(169bk) Utilizing Surfactant-Specific Graph Convolutional Networks to Predict Surfactant Adsorption Efficiency
(169z) Exploration of PHA Synthase Mechanism through QM/MM Simulations
(169b) Data Set and Data-Driven Models for Predicting Metal-Organic Framework Stability in Water and Harsh Environments
(169dc) Representation of Stochastic Polymeric Materials for Machine Learning Applications
(169dk) Temperature-transferable Coarse-grained model for Studying Pluronic Triblock Copolymers
(169n) Surface-Centered Approach for Characterization and Prediction of Protein-Membrane Interactions
(169cu) Leveraging Grand Canonical Monte Carlo and Machine Learning for Classification of Solvent Affinity in Functionalized Polymer Membrane Materials
(169dj) Development of a Modifiable Atomistic Cellulose Nanocrystal Model
(169cl) Generative Artificial Intelligence for Property-Guided Design of Co-Polymers
(169af) Modeling H-D Exchange in Supported Catalytically Active Liquid Metal Solutions Using Reactive Molecular Dynamics
(709g) Discovery of New Surfactants Used in Firefighting Foams By Active Learning
(169v) Density Functional Theory (DFT) Analysis of CO2 Adsorption and Dissociation on Reducible Oxides, and Integration into a Microkinetic Model for Dry Reforming of Methane (DRM)
(169cz) AI Based Exploration on Synthesizable Space for Autonomous Laboratory
(169bs) Elucidating the Fluxionality and Dynamics of Zeolite-Confined Au Nanoclusters Using Machine Learning Potentials
(169u) Perdew-Zunger Self-Interaction Correction for Ionization Energies of Transition Metal Atoms