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El Agente: An Autonomous Agent for Quantum Chemistry

Yunheng Zou, Austin H. Cheng, Abdulrahman Aldossary, Jiaru Bai, Shi Xuan Leong, Jorge Arturo Campos-Gonzalez-Angulo, Changhyeok Choi, Cher Tian Ser, Gary Tom, Andrew Wang, Zijian Zhang, Ilya Yakavets, Han Hao, Chris Crebolder, Varinia Bernales, Alán Aspuru-Guzik

arXiv:2505.02484v2cs.AIcs.LGcs.MAphysics.chem-ph

TL;DR

Computational chemistry tools remain difficult for non-specialists and demanding for experts. El Agente Q generates and executes quantum chemistry workflows from natural-language prompts, achieving high success across university-level exercises.

  • Problem

    Computational chemistry software has a steep learning curve, requiring expert knowledge to configure tools, set parameters, interpret results, and handle technical challenges.

  • Method

    El Agente Q uses a hierarchical multi-agent architecture with shared and agent-specific memory to decompose tasks, route tools, execute workflows, and support long-term planning.

  • Results

    Across university-level exercises, El Agente Q achieved high success rates across difficulty levels, averaging >88.8% at first-level difficulty.

  • Takeaways & Limitations

    El Agente Q generalized to new quantum chemistry problems while enabling human inspection through molecular visualization and exportable action traces.

  • Takeaways & Limitations

    When information is incomplete, El Agente Q may make unverified assumptions instead of rerunning calculations at the specified level of theory.

Abstract

from arXiv · show

Computational chemistry tools are widely used to study the behaviour of chemical phenomena. Yet, the complexity of these tools can make them inaccessible to non-specialists and challenging even for experts. In this work, we introduce El Agente Q, an LLM-based multi-agent system that dynamically generates and executes quantum chemistry workflows from natural language user prompts. The system is built on a novel cognitive architecture featuring a hierarchical memory framework that enables flexible task decomposition, adaptive tool selection, post-analysis, and autonomous file handling and submission. El Agente Q is benchmarked on six university-level course exercises and two case studies, demonstrating robust problem-solving performance (averaging >87% task success) and adaptive error handling through in situ debugging. It also supports longer-term, multi-step task execution for more complex workflows, while maintaining transparency through detailed action trace logs. Together, these capabilities lay the foundation for increasingly autonomous and accessible quantum chemistry.

INTRODUCTION

Computational chemistry offers broad scientific benefits but remains difficult to use because complex tools, runtime errors, and fixed workflows burden users. El Agente Q addresses these limitations with a hierarchical LLM-based multi-agent system that coordinates diverse quantum chemistry tasks and software tools.

  • Motivation: Computational chemistry supports systematic study of molecular systems and streamlines drug, catalyst, and materials design, but its complex tools remain difficult for users.Simulation can reduce experimental workload by efficiently screening target molecules.
  • Motivation: Runtime errors can challenge even experienced users, while integrated pipelines often depend on fixed tools and predefined workflows.These limitations can divert time from scientific discovery toward troubleshooting calculations.
  • Motivation: LLMs offer a potential solution by encoding chemistry knowledge and interpreting human expertise expressed in textual formats.The passage attributes these capabilities to pre-training and in-context learning.
  • Contribution: El Agente Q applies a hierarchical network of specialized LLM-based agents to automate complex computational and quantum chemistry tasks.The hierarchy filters irrelevant context for each agent and is designed to improve decision-making performance.
  • Novelty: Unlike earlier computational-chemistry agents, El Agente Q coordinates several computational chemistry tasks simultaneously within a multi-agent system.The work builds on prior rule-based, knowledge-graph, and LLM-based agent systems while focusing on coordinated task execution.
  • Capabilities: The system performs geometry optimization, electronic structure analysis, and thermochemical evaluation through software packages, databases, and SLURM cluster submission.Named integrations include RDKit, OpenBabel, xTB, ORCA, Architector, and the SLURM job scheduler.

RESULTS

El Agente Q combines hierarchical multi-agent planning, specialized memory, dynamic tool routing, and action-trace transparency to execute quantum chemistry workflows. It achieved success rates above 88.8% and 87.6% across university exercises and demonstrated adaptive debugging in complex case studies.

  • Cognitive architecture: El Agente Q’s cognitive architecture combines global and agent-specific memory, environmental grounding, and long-term procedural, semantic, and episodic memory.Its LLM reasoning core uses this working memory to plan, reason, act, critique, and report execution in real time.
  • Hierarchical workflow execution: Hierarchical agents dynamically select tools, decompose tasks, route subtasks, and manage execution details according to domain-specific knowledge.The system supports choices such as RDKit versus OpenBabel and sequences such as geometry optimization followed by frequency analysis.
  • Benchmark performance: >88.8% average success rate was achieved for first-level university exercises, while >87.6% was achieved for second-level exercises across 10 trials.The results indicate generalization across a wide diversity of chemical problems.
  • Adaptive error recovery: El Agente Q performed in situ debugging by correcting invalid inputs, hallucinated ORCA keywords, batch-submission errors, imaginary frequencies, and energy-convergence issues.It autonomously removed or replaced DOSINGLET, DOTRIPLET, and NROOTS_TRIPLET and repeatedly re-optimized affected structures.
  • Case studies: In case studies, El Agente Q completed alanine–water and f-block workflows with minimal human intervention, including model-specific resource allocation and convergence handling.It allocated 8, 16, and 24 cores across gas-phase, CPCM, and explicit-CPCM hybrid models, while a dispersion-data extraction omission remained a limitation.

DISCUSSION

El Agente Q dynamically constructs complex quantum-chemistry workflows, supports long-term autonomous execution, and lowers access barriers through natural-language interaction. Its performance varies because of incomplete reporting, unverified assumptions, geometry-generation limitations, and coordination failures, motivating infrastructure and interoperability improvements.

  • Strengths: El Agente Q automatically designs workflows for specified scientific targets, averaging 64.5 actions and 103.9 agent interactions per benchmark exercise.Dynamic decision-making explores different action sequences while accommodating software-specific input requirements and missing workflow details.
  • Strengths: Hierarchical task decomposition enables El Agente Q to execute extended, multi-step tasks involving numerous calculations, tool calls, and iterative decisions.The architecture maintains coherence when total task context exceeds an individual model’s native token limits.
  • Strengths: A natural-language prompt can specify compounds, reactions, and calculation types, making computational chemistry more accessible to students and experimental chemists without programming knowledge.The system lowers the knowledge barrier for planning workflows with computational chemistry and cheminformatics packages.
  • Strengths: Performance can improve when stronger reasoning-capable models are used, while the hierarchical architecture reduces cognitive load by focusing each expert node on relevant context.The architecture is described as orthogonal to the underlying LLM and external reasoning frameworks.
  • Limitations: Performance variability arises from missing reports or requested properties, unverified assumptions, unreliable geometry generation, and manually designed coordination constraints.These issues were especially associated with larger parallel batches, incomplete information, current geometry tools, and early multi-agent coordination failures.
  • Future roadmap: Future development includes broader accessible calculations, user-friendly structure editing, cloud-enabled access, and interoperability through MCP and A2A communication.A shared executable ontology is identified as a central challenge for unified data exchange across connected systems and databases.

Conclusion

El Agente Q executes quantum chemistry tasks successfully, generalizes to previously untaught problems, and supports expert inspection through visualization and action-trace export. The authors envision increasingly autonomous computational chemistry that lets scientists focus on scientific problems and democratizes access to the field.

  • The agent system understands and executes quantum chemistry tasks with a high degree of success.
  • It generalizes to previously untaught problems, including simulating a lanthanoid complex and an IR spectrum.
  • Molecular visualization and exported action traces enable human expert inspection and expedite workflow construction for virtual high-throughput applications.
  • Future computational chemistry workflows are envisioned as completely autonomous and high-level as LLM-based agents become cheaper and smarter.
  • Scientists could focus on scientific problems and high-level strategic thinking instead of writing scripts and handling exceptions, helping democratize computational chemistry.

METHODS

El Agente Q combines specialized agents, structured quantum-chemistry knowledge, and persistent memory to generate and execute ORCA workflows. Its interface supports real-time coordination, reproducible session management, and dynamic error recovery.

  • Agent architecture and memory: Agents receive specialized contexts and task-specific semantic memories, while episodic histories are stored in MongoDB and global memory is shared through a session-specific file.This memory design supports provenance tracking, retrospective analysis, and access to dynamically generated shared content.
  • Expert-guided workflow knowledge: Subject-matter experts teach agents standard quantum-chemistry procedures, including geometry optimization before energy differences and frequency calculations after optimization.The expert-guided procedures ensure that workflows follow required computational sequencing and optimization checks.
  • ORCA input generation: Structured markdown context files encode ORCA block syntax, functions, and decision logic for keyword-line experts that synthesize complete input files.The files specify block roles, upstream triggering conditions, mandatory requirements, key parameters, and relevant defaults.
  • ORCA input generation: The input file expert assembles partial contents from block experts, which evaluate system characteristics and available HPC resources to recommend calculation settings.For example, the %pal block expert determines parallelization settings, including the number of cores.
  • Error handling: ORCA error feedback enables correction of syntax or configuration errors after submission, supporting dynamic error recovery and exception handling.This implements a generate–then–verify workflow for submitted jobs.
  • User interface: The web-based GUI provides real-time chat with agent nodes, node-specific query routing and response filtering, session switching, and workflow export to Jupyter notebooks.It was developed with Flask v3.1.0 and Uvicorn v0.32.1.

RESOURCE AVAILABILITY

The study generated no new materials, and all data needed to evaluate its conclusions are available through the provided DOI repository.

  • The study did not generate new materials.
  • All data required to evaluate the presented conclusions are available via the provided DOI repository.https://doi.org/10.5683/SP3/JU2BQK

Supporting Information · A Agents and Tools

This section summarizes El Agente Q’s agents and tools, including ORCA input configuration, file and operating-system operations, calculation execution, cluster submission, and frequency-error handling.

  • A Agents and Tools: The supporting information summarizes the agents and tools developed for El Agente Q.It presents Table S1 as a summary of agents and Table S2 as a summary of tools.
  • A Agents and Tools: ORCA configuration agents set implicit solvation, geometry-specific parameters, parallel execution, and specialized SCF settings.These agents configure the ‘cpcm’, ‘geom’, ‘pal’, and ‘scf’ blocks; specialized SCF settings are typically unnecessary when ORCA defaults suffice.
  • A Agents and Tools: The global_memory_expert determines ORCA global memory allocation to optimize performance and avoid exceeding system limits.The description gives MaxCore as an example of the allocation it determines.
  • A Agents and Tools: The generate_geometry_line tool creates ORCA geometry sections from external XYZ files or coordinates embedded in input files.It supports either referencing an external XYZ file or including coordinates directly.
  • A Agents and Tools: The interact_with_os_and_files tool reads files, parses output data, and performs operations such as moving, deleting, and copying files.Its responsibilities include file handling and other operating-system operations.
  • A Agents and Tools: The run_orca tool runs ORCA calculations using claude-3-7sonnet-latest.The passage explicitly associates this tool with the listed LLM model.
  • A Agents and Tools: The submit_slurm_job tool submits one or more Slurm jobs to a compute cluster and supports parallelization when multiple jobs are provided.It requires the job or folder name(s) and can submit multiple jobs in parallel.
  • A Agents and Tools: The remove_imaginary_frequency tool perturbs existing geometries and resubmits jobs to remove detected imaginary frequencies, including across multiple systems in parallel.It operates on frequency-calculation output files and supports parallel handling of multiple systems.

B Supplemental note 1: Benchmark Studies

The section presents six university-level benchmark studies, including representative user queries and agent interactions at Levels 1 and 2. It reports that the agent identifies each task and initiates workflows through specialized agents, with human experts applying a rubric for marking.

  • Benchmark Studies: Six university-level benchmarks are presented with overall statistics and representative examples covering user queries and agent interactions at Levels 1 and 2.The examples include both interaction levels for each benchmark.
  • Benchmark Studies: The agent correctly identifies the nature of all six benchmark tasks from user input and initiates computational workflows by calling specialized agents.This describes the workflow initiation mechanism used for the benchmark tasks.
  • Benchmark Studies: Human experts execute the marking according to the rubric defined in Table S8.The rubric provides the stated basis for expert evaluation.

B.1 Overall statistics … computational_chemist

Across the evaluated exercises, El Agente Q reports success rates across categories and difficulty levels while sustaining performance as interaction depth grows. The workflow example shows coordinated geometry generation, ORCA optimization, property extraction, visualization, and report compilation for three organic molecules.

  • B.1 Overall statistics: Success rates were measured across ten runs for each exercise category and difficulty level, including averages and standard deviations.The supplied passage identifies Table S3 as the aggregate success-rate evaluation.
  • B.1 Overall statistics: Agent interactions comprise reporting, commanding, and acting, with Table S4 defining these actions and Table S5 averaging them by question type and difficulty.Reporting is lower-level-to-higher-level communication, commanding is delegation, and acting is tool calling.
  • B.1 Overall statistics: Total interactions and tokens were uncorrelated with success rate across 120 runs, with correlations r = 0.05 and −0.08.This suggests performance remained resilient as task depth grew for the considered tasks.
  • Step 1: Generate initial geometries for all three molecules: The Level 1 workflow begins by requesting parallel initial geometries for caffeine, theobromine, and acetylsalicylic acid from SMILES representations.The geometry-generation step prepares all three initial geometries.
  • Step 2: Organize files and perform geometry optimizations: The system organizes each initial geometry into a separate molecular folder before launching parallel ORCA geometry optimizations.The optimization uses Hartree-Fock with the def2-SVP basis in the gas phase, with neutral singlet specifications for all three molecules.
  • Step 3: Generate reports for each molecule: For each molecule, the workflow extracts Cartesian coordinates, total energy, symmetry, dipole moment, molecular orbitals, HOMO-LUMO gap, and atomic charges.The requested charge analyses are Mulliken, Löwdin, and Hirshfeld.
  • Step 4: Create Comprehensive Reports: The workflow visualizes the optimized caffeine, theobromine, and acetylsalicylic acid structures before combining the extracted information into comprehensive individual reports.Visualization uses each molecule’s optimized XYZ file, and the final step consolidates the extracted results.

Task Completion Report … computational_chemist

The computational chemistry task was completed using parallel ORCA HF/def2-SVP gas-phase optimizations, stable-minimum verification, and comprehensive per-molecule reporting. The workflow produced reports containing structural, energetic, electronic, charge-analysis, and visualization data for the requested compounds.

  • Task Completion Report: Caffeine, theobromine, and acetylsalicylic acid were successfully optimized with ORCA using HF/def2-SVP in the gas phase, producing individual reports.The workflow also organized molecule-specific files and began from SMILES-derived geometries with charge 0 and multiplicity 1.
  • Task Completion Report: All three reported structures reached stable minima after preliminary xtb and full HF/def2-SVP optimizations with frequency calculations, showing no imaginary frequencies.The completed workflow included geometry generation, file organization, optimization, frequency analysis, and data extraction.
  • Task Completion Report: Caffeine had a total energy of -675.83732469 Hartrees, a HOMO-LUMO gap of 11.81 eV, and a dipole moment of 4.33 Debye.These values are reported in the task-completion summary table.
  • Task Completion Report: Theobromine had a total energy of -636.83781921 Hartrees, a HOMO-LUMO gap of 11.83 eV, and a dipole moment of 4.94 Debye.These values are reported in the task-completion summary table.
  • Task Completion Report: The requested reports were stored in the reports directory for caffeine, theobromine, and acetylsalicylic acid, and the requested analysis was completed successfully.The reports contained coordinates, energies, symmetry, dipole moments, molecular-orbital analysis, charge analysis, and structure images.
  • Step 1: Organize files and create folders for each molecule: The workflow was organized into molecule-specific directories and report-generation steps, including structure visualization and extraction of orbital, gap, coordinate, energy, symmetry, dipole, and charge data.The recorded process covered the initial compounds and continued systematically across the remaining molecules, including charged species and corrected acetaminophen geometry.
  • computational_chemist: Parallel HF/def2-SVP gas-phase geometry optimizations were completed for 20 organic compounds, with comprehensive reports generated for each molecule.Reports included final Cartesian coordinates, total energy, point-group symmetry, dipole moment, molecular-orbital analysis, atomic charge analysis, and optimized-structure images.

Summary of Completed Tasks … Summary of Results

The completed workflows optimized inorganic compounds in parallel with ORCA and generated detailed molecular reports, while a separate DFT workflow calculated carbocation formation thermodynamics and saved the results to a summary file.

  • Summary of Completed Tasks: Three inorganic compounds had initial geometries generated from SMILES, organized into molecule-specific folders, and optimized successfully.Chlorine trifluoride required an additional optimization to eliminate imaginary frequencies.
  • Summary of Completed Tasks: Fluorophosphoric acid (deprotonated) showed a 3.19288 Debye dipole moment and a 15.95 eV (0.58657 Hartree) HOMO-LUMO gap.Reports were saved as markdown files, and optimized structures were visualized as image files.
  • Performing Parallel Geometry Optimization and Analysis: The inorganic-compound workflow organized files, performed parallel HF/def2-SVP optimizations, generated individual reports, and visualized optimized structures.Reports were produced one molecule at a time and stored in a reports directory.
  • Step 3: Generating Individual Reports: The reporting workflow extracted coordinates, total energies, point-group symmetry, dipole moments, molecular orbital energies, and atomic charge analyses for the listed molecules.Charge analyses included Mulliken, Löwdin, and Hirshfeld values where requested.
  • Summary of Completed Tasks: 16 inorganic compounds were optimized in parallel using ORCA with HF/def2-SVP in the gas phase, with frequency analysis confirming true energy minima.Detailed reports included final Cartesian coordinates, total energies, symmetry, dipole moments, molecular orbital information, and charge analyses.
  • B.4 Relative Stability of Carbocations: Carbocation formation thermodynamics were calculated for the reaction R-H -> R+ + H- after optimizing neutral molecules and carbocations with DFT using B3LYP/6-31G*.The hydride received a separate B3LYP/6-31G* calculation with frequency analysis, and imaginary frequencies were addressed for relevant carbocations.
  • Summary of Results: The carbocation formation enthalpy and Gibbs free energy results were compiled into a table and saved in “carbocation_formation_energies.txt”.Thermodynamic data were extracted from the optimized neutral, carbocation, and hydride output files.

Carbocation Formation Thermodynamics Results … Summary of Carbocation Formation Energy Calculations

The workflow calculates carbocation formation enthalpies and Gibbs free energies for nine neutral molecules, their carbocations, and hydride using DFT, with trimethylamine requiring the least formation energy among the reported examples. El Agente Q completed geometry generation, optimization, energy extraction, calculation, and structural verification, including in situ treatment of imaginary frequencies.

  • Carbocation Formation Thermodynamics Results: The reported values for 2-methylpropane are 270.17 and 261.30.These values are listed in the carbocation formation thermodynamics results.
  • Carbocation Formation Thermodynamics Results: The reported values for dimethyl ether are 276.87 and 269.46.These values are listed in the carbocation formation thermodynamics results.
  • Carbocation Formation Thermodynamics Results: Trimethylamine has the lowest reported carbocation formation energy, at 239.54 kcal/mol for ∆H, while methane has the highest, at 354.26 kcal/mol for ∆H.Lower formation energies indicate more stable carbocations; the results suggest trimethylamine forms carbocations more readily than the studied molecules, whereas methane is least likely.
  • B.4.2 Level 2: The task requests carbocation formation enthalpies and Gibbs free energies for methane, ethane, propane, 2-methylpropane, toluene, benzene, dimethyl ether, trimethylamine, and propene.The reaction is specified as R-H -> R+ + H-.
  • Plan for Carbocation Formation Energy Calculations: The completed workflow extracted electronic energy, enthalpy, and Gibbs free energy values, calculated formation quantities, saved results to a text file, and visualized optimized structures for verification.For propene, ethylcation, and propylcation, values after imaginary-frequency removal were taken from distorted output files.
  • Summary of Carbocation Formation Energy Calculations: The workflow generated and optimized nine R-H molecules, nine R+ carbocations, and one hydride ion using DFT with the B3LYP/6-31G* basis set.Neutral molecules used charge 0 and multiplicity 1; carbocations used charge +1 and multiplicity 1; hydride used charge -1 and multiplicity 1.

Key Results … Introduction

The paper reports carbocation formation-energy trends and an autonomous workflow for computing cycloalkane ring-strain energies. The workflow uses B3LYP/6-31G(d), optimized geometries, frequency calculations, thermochemical analysis, reference recalculation, and final report generation.

  • Key Results: Trimethylamine has the lowest reported carbocation formation energy, at 239.54 kcal/mol, while methane has the highest, at 354.26 kcal/mol.The reported trend attributes trimethylamine’s low value to nitrogen stabilizing adjacent positive charge.
  • B.5.1 Level 1: The ring-strain exercise computes ΔH and ΔG for reactions across n=4 to 8 to approximate relative ring-strain energies for cycloalkanes of sizes 3 to 8.The task requires reporting ring size alongside ring-strain enthalpy and free energy.
  • Plan for Ring Strain Energy Calculations: The workflow generates geometries, optimizes structures, calculates frequencies, extracts thermochemical values, computes reaction energies, and derives ring-strain energies using alternative references.Cyclooctane is used initially as the reference, followed by recalculation with cyclohexane.
  • computational_chemist: The computational workflow generates ten cycloalkane and methylcycloalkane geometries from specified SMILES strings and names files according to molecule names.The listed molecules span cyclobutane through cyclooctane and their methylated counterparts.
  • computational_chemist: All ten molecules are optimized and frequency calculations are performed at B3LYP/6-31G(d), with separate folders and charge 0, multiplicity 1.The calculations use the generated initial geometries for each molecule.
  • Ring Strain Energies of Cycloalkanes: The post-analysis stage calculates reaction energies and ring-strain energies from the thermochemical data, first using cyclooctane and then cyclohexane as zero-strain references.The requested reactions are cyclo(CnH2n) → cyclo(Cn-1H2n−3)-CH3 for n=4 to 8.
  • Introduction: The final report covers cycloalkanes from n=3 to n=8 and is based on fully optimized B3LYP/6-31G(d) calculations with frequency analysis.The workflow also creates a file named report.md containing the calculated ring-strain energies.

Computational Methods … Predicted pKa of Chlorofluoroacetic Acid

The study uses B3LYP/6-31G* with PCM water solvation and a thermodynamic-cycle calibration to predict chlorofluoroacetic acid’s pKa. The predicted pKa is -2.40, with reference-acid estimates of -2.35 and -0.08.

  • Computational Methods: B3LYP/6-31G(d) geometry optimizations and frequency calculations were used to estimate cycloalkane ring-strain energies relative to cyclooctane and cyclohexane.The calculations used isodesmic reactions and treated cyclohexane as a zero-strain reference in the later analysis.
  • Discussion: The ring-strain analysis found a minimum at n=6, supporting cyclohexane’s use as a near strain-free reference.The reported trend attributes high strain to small rings and moderate strain recovery to cycloheptane and cyclooctane.
  • Ring Strain Analysis of Cycloalkanes via Reaction Energies: 13.86 kcal/mol and 13.63 kcal/mol were the greatest reported relative strain energies for cyclopropane and cyclobutane, respectively.Cyclopentane was reported at -2.20 kcal/mol, while strain decreased toward n=6 and increased again for larger rings.
  • Method: B3LYP/6-31G* with PCM water solvation was used to optimize the acids, conjugate bases, and proton, with Gibbs free energies obtained from frequency calculations.The workflow included acetic, fluoroacetic, chloroacetic, and chlorofluoroacetic acid species and their conjugate bases.
  • Calculated Gibbs Free Energies (Hartree): -787.66794756 Hartree and -787.21699482 Hartree were the calculated Gibbs free energies for chlorofluoroacetic acid and its conjugate base.The proton electronic energy was -1.09744548 Hartree.
  • Experimental Reference Values: 402.40 kcal/mol was the average correction for proton solvation derived from acetic, fluoroacetic, and chloroacetic reference acids.The reference corrections were 399.17, 402.47, and 405.58 kcal/mol, respectively.
  • Predicted pKa of Chlorofluoroacetic Acid: -2.35 was the predicted pKa using fluoroacetic acid as the reference acid, whereas -0.08 was obtained using chloroacetic acid.These values reflect the reference-acid calibration approach used for chlorofluoroacetic acid.
  • Predicted pKa of Chlorofluoroacetic Acid: -2.40 was the predicted pKa of chlorofluoroacetic acid.The prediction was obtained after calibrating proton solvation free energy from known reference acids.

Analysis … computational_chemist

The section reports an autonomous computational-chemistry workflow that calculates chlorofluoroacetic acid acidity and performs TDDFT analyses of three molecular structures. The workflow organizes files, configures singlet/triplet calculations, corrects input settings, and compiles the requested excited-state properties.

  • Analysis: The calculated pKa of -2.40 indicates chlorofluoroacetic acid is substantially more acidic than fluoroacetic acid (pKa = 2.586) and chloroacetic acid (pKa = 2.86).The result is attributed to chlorine and fluorine jointly stabilizing the conjugate base through strong electron-withdrawing effects.
  • B.7 Absorption Spectrum of Organic Molecules; B.7.1 Level 1: The Level 1 task requested S1 energy, S1–T1 energy difference, and oscillator strength for structures 2.xyz, 3.xyz, and 5.xyz using single-point TDDFT.The specified method was RI-wB2PLYP/def2-mSVP with AutoAux, including excited triplet states.
  • Plan for TDDFT Calculations and Analysis: The planned workflow organized XYZ files, ran TDDFT calculations, extracted requested properties, and compiled a summary report.The files were placed into separate molecule-specific folders before calculation.
  • computational_chemist: The calculations treated the molecules as neutral singlets and targeted S1 energy, T1 energy, and oscillator strength for molecule 2.The molecule 2 job used RI-wB2PLYP/def2-mSVP with AutoAux and charge 0, multiplicity 1.
  • computational_chemist; TDDFT Calculation Results Summary: The workflow corrected the TDDFT setup by explicitly enabling triplet states and requesting at least 5 roots for both singlet and triplet states.The updated molecule 2 calculation used ‘TripletFlag true’, while subsequent calculations used ‘Triplets true’.
  • TDDFT Calculation Results Summary; Calculation Details: TDDFT calculations were completed for all three molecules and compiled into a comprehensive summary report.The report included S1 energies, T1 energies, S1–T1 differences, and oscillator strengths to S1.
  • B.7.2 Level 2; Plan for TDDFT Calculations on Multiple Structures: The Level 2 workflow organized structures 2, 3, and 5 into separate directories and planned parallel single-point TDDFT calculations.The setup again used RI-wB2PLYP/def2-mSVP with AutoAux for neutral systems and sought sufficient singlet and triplet states.
  • computational_chemist: The Level 2 execution inspected the molecular XYZ files, ran the three TDDFT calculations, and prepared a summary report containing the requested data.The workflow also read the completed report to verify its contents.

TDDFT Calculation Results … Step 2: DFT Geometry Optimization with ORCA

El Agente Q completed TDDFT analyses, solvent-dependent alanine vibrational studies, and Ce(III) complex workflows across multiple computational models. The workflows achieved successful calculations while using iterative error correction to resolve ORCA input incompatibilities.

  • TDDFT Calculation Results: Structure 3 had the lowest S1 energy at 1.698 eV, while Structure 5 had the highest at 3.196 eV.Structure 3 also had the smallest S1-T1 gap, 0.256 eV, whereas Structure 2 had the largest, 0.348 eV; Structure 5 had the strongest oscillator strength, 0.077525.
  • C.1 Influence of Solvent on IR Spectra: The alanine study modeled gas phase, CPCM implicit water, and 12 explicit water molecules plus CPCM using PBE0/def2-TZVP.Each system underwent geometry optimization and frequency calculation, followed by IR-spectrum extraction and comparison.
  • Plan for Investigating Solvent Effects on Alanine Vibrational Frequencies: All three alanine optimizations converged successfully, with frequency calculations confirming no imaginary frequencies and output files stored in their respective folders.The optimized structures were visualized, and the calculation data were organized for subsequent spectral analysis.
  • Key Findings: 141 vibrational modes were obtained for explicit solvent, compared with 33 modes for both gas-phase and CPCM systems.The carbonyl stretching frequency was highly sensitive to solvation, with larger red shifts for the explicit model; C-H stretching modes were less affected.
  • Conclusions: Solvent environments significantly affected alanine vibrational frequencies, especially for hydrogen-bonding groups, and shift magnitudes correlated with hydrogen-bond strength.Combining explicit water molecules with a continuum model provided a compromise between computational efficiency and accurate solvation representation.
  • C.2 Lanthanoid Complex (full chat history): The Ce(III) complex workflow generated 5 conformers for a neutral, multiplicity-2 complex with coordination number 9, containing 3 nitrate_bi and 3 water ligands.The planned workflow used PBE0/def2-SVP optimization followed by ωB97M-V/def2-SVPD single-point calculations and extracted conformer energies.
  • Step 2: DFT Geometry Optimization with ORCA: All five initial ORCA geometry-optimization and frequency jobs failed because the installed version rejected TightSCF and then ConvCriteria in the SCF block.The errors were identified as syntax incompatibilities in the ORCA input files.
  • Step 2: DFT Geometry Optimization with ORCA: All five corrected ORCA jobs completed normally, with successful geometry optimization and frequency calculations for the Ce(III) conformers.The final workflow used PBE0/def2-SVP, def2-ECP for Ce, D4 dispersion, RIJCOSX, and default SCF convergence after removing the unsupported ConvCriteria line.

Step 3: Check for and Remove Imaginary Frequencies … Summary of Optimization Process

Imaginary-frequency checks identified two problematic Ce conformers, which were reoptimized from distorted structures using preserved ORCA settings. After iterative corrections, all five conformers had no imaginary frequencies or only negligible values below 15 cm−¹.

  • Step 3: Check for and Remove Imaginary Frequencies: Two of five Ce conformers initially had imaginary frequencies: cn9_YICLED at -131.99 cm−¹ and capped_square_antiprismatic_1 at -81.23 cm−¹, both in mode 6.The other three conformers had no imaginary frequencies.
  • Step 3: Check for and Remove Imaginary Frequencies: The two affected structures were distorted along their imaginary modes and resubmitted for geometry optimization and frequency calculations.New input files preserved the original computational configuration while replacing only the geometry files.
  • Geometry Optimization Report for Ce Complex Conformers: Both replacement calculations completed normally and converged for geometry optimization and frequency steps using 16 cores per job.The calculations retained PBE0/def2-SVP, D4 dispersion, RIJCOSX, and def2-ECP settings.
  • Summary of Imaginary Frequency Removal: After the first reoptimization, cn9_YICLED retained -85.19 cm−¹, whereas capped_square_antiprismatic_1 had -14.79 cm−¹, below the 15 cm−¹ threshold and therefore ignorable.The remaining cn9_YICLED frequency required another optimization round.
  • Step 3: Check for and Remove Imaginary Frequencies: A second distorted structure was generated and optimized for cn9_YICLED using the same configuration, and the calculation completed with “ORCA TERMINATED NORMALLY.”The second calculation used 16 cores and generated the requested output files.
  • Summary of Imaginary Frequency Removal: The final cn9_YICLED structure had no imaginary frequencies after the second optimization round.Its SCF procedure converged after 5 cycles, and geometry optimization reached convergence without critical errors.
  • Summary of Optimization Process: All five Ce complex conformers ultimately had no imaginary frequencies or only negligible values below 15 cm−¹ in absolute value.The resulting optimized structures were reported as suitable for further analysis.

Final Optimized Geometries … Relative Energies (in kcal/mol)

Five Ce(III) conformers were optimized and evaluated with single-point calculations, with all final structures representing local minima and all corrected calculations converging successfully. The relative-energy results identify capped_square_antiprismatic_0 and tri_tri_mer_capped as the reported stability references in their respective energy entries.

  • Final Optimized Geometries: All five Ce(III) conformers were optimized, and their final structures represented true local minima with no imaginary frequencies or only negligible frequencies below 15 cm−¹.cn9_YICLED required two rounds of imaginary-frequency removal, while the other conformers reached minima after initial optimization or one removal.
  • Final Optimized Geometries: Five corrected ORCA single-point calculations completed normally after the incompatible VV10 keyword was removed while other settings remained unchanged.The calculations used optimized geometries, ωB97M-V, def2-SVPD with def2-ECP for Ce, 16 cores, 500 maximum SCF iterations, charge 0, and multiplicity 2.
  • Final Optimized Geometries: SCF convergence was achieved for all five single-point calculations in 60-76 cycles.The reported convergence counts were 60, 75, 76, 71, and an additional value for the five conformers, with energy or gradient checks confirming convergence.
  • Absolute Energies (in Hartree, Eh): Single-point energies were computed at ωB97M-V/def2-SVPD for the optimized conformers, while the optimization energies used PBE0/def2-SVP.The reported absolute-energy comparison includes both optimization and single-point values for the conformers.
  • Summary of Ce Complex Computational Study: The study generated and optimized 5 Ce complex conformers with coordination number 9, neutral charge, and multiplicity 2.Optimization used PBE0/def2-SVP, D4 dispersion, RIJCOSX, DEFGRID2, and def2-ECP for Ce.
  • Relative Energies (in kcal/mol): capped_square_antiprismatic_0 had the reported relative-energy entry 1.10 0.00 (most stable).The passage presents this conformer’s values under the relative-energies section without additional column labels.
  • Relative Energies (in kcal/mol): tricapped_trigonal_prismatic had the reported relative-energy entry 0.43 0.10.The values are reproduced as presented in the supplied passage.
  • Relative Energies (in kcal/mol): tri_tri_mer_capped had the reported relative-energy entry 0.00 (most stable) 0.28.The values are reproduced as presented in the supplied passage.
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