Stephan Irle

Stephan Irle

Senior R&D Staff Member and Group Leader, Computational Chemistry and Nanomaterials Sciences Group

Contact

IRLES@ORNL.GOV

All Publications

Acidities of MgO surface sites: implications for the formation mechanism of Mg(OH)2...

Inverse mapping of properties to composition through generative modeling for designing molten salts

Recent Developments in DFTB+, a Software Package for Efficient Atomistic Quantum Mechanical Simulations

Collection: TD-DFT and EOM-CCSD Calculations for the GDB-9-Ex Dataset

Evaluation of Density-Functional Tight-Binding Methods for Simulation of Protic Molecular Ion Pairs

Molecular Origin of Viscoelasticity and Influence of Methylation in Mesophase Pitch

Variation in Cation Adsorption Mechanism Controlled by Chemical and Structural Heterogeneities at the Quartz (101)–Water Interface

Variations in proton transfer pathways and energetics on pristine and defect-rich quartz surfaces in water: Insights into the...

Insights into the Properties of MXenes and MXene Analogs from Atomistic Simulation...

Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms

Large-scale atomistic model construction of subbituminous and bituminous coals for solvent extraction simulations with reactive molecular dynamics

On the role of methyl groups in the molecular architectures of mesophase pitches

Deep learning workflow for the inverse design of molecules with specific optoelectronic properties...

Gene Expression Programming for Quantum Computing

Multipole Expansion of Atomic Electron Density Fluctuation Interactions in the Density-Functional Tight-Binding Method

Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering

Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules

Toward Quantum Chemical Free Energy Simulations of Platinum Nanoparticles on Titania Support

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals **

Adaptive language model training for molecular design...

Effect of surface functional groups on MXene conductivity

Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases

Accelerating the density-functional tight-binding method using graphical processing units

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models