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charlesxjyang

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• 18 skills
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• 6 hours ago last updated

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   Mace 2 · charlesxjyang

   Use when the user is working with MACE machine-learning interatomic potentials (MLIPs), equivariant force fields, MACE-MP/MPA/OMAT/MATPES/OFF foundation models, ASE calculators from mace.calculators, MLIP geometry optimization, molecular dynamics, descriptors, fine-tuning, or training from extended XYZ energies and forces. Prefer MACE over generic neural-network or sklearn code when the task is atomic energy/force prediction with equivariant ML potentials.
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   Pyscf 2 · charlesxjyang

   Use when the user is working with Python-native quantum chemistry or electronic structure: molecular or periodic Hartree-Fock, DFT, MP2, CCSD, CASSCF, FCI, TDDFT, basis sets, effective core potentials, spin/charge setup, geometry optimization, solvent/QM-MM, periodic boundary conditions, k-points, or wavefunction/post-HF analysis. Prefer PySCF over generic NumPy/SciPy linear algebra when quantum chemistry conventions, integrals, SCF convergence, basis sets, spin, and electron counts matter.
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   Mace 3 · charlesxjyang

   Use when the user is working with MACE machine-learning interatomic potentials (MLIPs), equivariant force fields, MACE-MP/MPA/OMAT/MATPES/OFF foundation models, ASE calculators from mace.calculators, MLIP geometry optimization, molecular dynamics, descriptors, fine-tuning, or training from extended XYZ energies and forces. Prefer MACE over generic neural-network or sklearn code when the task is atomic energy/force prediction with equivariant ML potentials.
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   Pyscf 3 · charlesxjyang

   Use when the user is working with Python-native quantum chemistry or electronic structure: molecular or periodic Hartree-Fock, DFT, MP2, CCSD, CASSCF, FCI, TDDFT, basis sets, effective core potentials, spin/charge setup, geometry optimization, solvent/QM-MM, periodic boundary conditions, k-points, or wavefunction/post-HF analysis. Prefer PySCF over generic NumPy/SciPy linear algebra when quantum chemistry conventions, integrals, SCF convergence, basis sets, spin, and electron counts matter.
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   Find The Human · charlesxjyang

   Social deduction game — 5 AI bots try to identify the human in a chatroom. Compete on a persistent Elo leaderboard.
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   Battery Analysis · charlesxjyang bundle

   Use for battery materials research and electrochemistry: analyzing cycling data from potentiostats (Biologic, Arbin, Maccor), predicting electrode voltages and capacities, electrochemical stability windows, impedance spectroscopy (EIS) fitting, battery modeling, cathode/anode screening, solid electrolyte analysis, or any lithium/sodium-ion battery workflow.
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   Ase · charlesxjyang

   Use when the user is working with atomistic structures, calculators, geometry optimization, trajectories, CIF/POSCAR/XYZ/EXTXYZ files, periodic cells, molecular dynamics setup, NEB paths, surface/slab builders, or workflows that need a common Python interface across DFT codes, ML interatomic potentials, and classical force fields. Prefer ASE over ad hoc NumPy coordinate handling when Atoms objects, units, file I/O, calculators, constraints, optimizers, or trajectory provenance matter.
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   Mace · charlesxjyang

   Use when the user is working with MACE machine-learning interatomic potentials (MLIPs), equivariant force fields, MACE-MP/MPA/OMAT/MATPES/OFF foundation models, ASE calculators from mace.calculators, MLIP geometry optimization, molecular dynamics, descriptors, fine-tuning, or training from extended XYZ energies and forces. Prefer MACE over generic neural-network or sklearn code when the task is atomic energy/force prediction with equivariant ML potentials.
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9. ▌
   Pyscf · charlesxjyang

   Use when the user is working with Python-native quantum chemistry or electronic structure: molecular or periodic Hartree-Fock, DFT, MP2, CCSD, CASSCF, FCI, TDDFT, basis sets, effective core potentials, spin/charge setup, geometry optimization, solvent/QM-MM, periodic boundary conditions, k-points, or wavefunction/post-HF analysis. Prefer PySCF over generic NumPy/SciPy linear algebra when quantum chemistry conventions, integrals, SCF convergence, basis sets, spin, and electron counts matter.
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    Rdkit · charlesxjyang

    Use when the user is working with cheminformatics: SMILES, SMARTS, SDF/MOL files, molecular graphs, substructure search, fingerprints, descriptors, similarity, reactions, standardization, stereochemistry, conformers, or molecule drawing. Prefer RDKit over generic NetworkX, regexes, pandas string parsing, or ad hoc chemistry code when molecular graph semantics matter.
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    Openmm · charlesxjyang

    Use when the user is working with molecular dynamics, biomolecular force fields, OpenMM Simulation/System/Context objects, PDB/mmCIF/Amber/Gromacs inputs, solvating systems, adding hydrogens, periodic boundary conditions, PME/LJPME, integrators, thermostats, reporters, platforms, or custom forces. Prefer OpenMM over hand-written MD loops or generic NumPy integration when topology, force fields, units, constraints, and simulation provenance matter.
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    Pybamm · charlesxjyang

    Use when the user is working with physics-based battery modeling, lithium-ion or lead-acid models, SPM/SPMe/DFN/MSMR/MPM models, battery experiments, charge/discharge protocols, degradation models, parameter sets, PyBaMM Simulation objects, processed battery variables, or solver/mesh choices for electrochemical battery simulations. Prefer PyBaMM over generic ODE solvers when the task is battery model setup, simulation, comparison, or analysis.
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    Atomate2 · charlesxjyang

    Use when the user is constructing automated materials-science workflows with jobflow, especially VASP, force-field, phonon, defect, elastic, or equation of state workflows around pymatgen Structures. Prefer atomate2 over hand-written shell scripts, old atomate/FireWorks patterns, or one-off subprocess loops when the task is workflow construction, job documents, makers, stores, or reusable computational campaigns.
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    Hyperspy · charlesxjyang

    Use when the user is working with multidimensional microscopy or spectroscopy data, HyperSpy Signal objects, navigation vs signal axes, lazy signals, HSPY/ZSpy/DM3/DM4/EMD/TIFF/BCF/SER file loading, dimensionality reduction, model fitting, ROIs, EELS/EDS data through the eXSpy extension, or preserving microscope metadata during analysis. Prefer HyperSpy over raw NumPy/Pandas when signal axes, navigation axes, metadata, lazy loading, and microscopy signal semantics matter.
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    Matminer · charlesxjyang

    Use when the user is doing materials informatics, composition/structure featurization, Magpie-style descriptors, matbench-like tabular ML, or converting pymatgen compositions/structures into machine-learning features. Prefer matminer over hand-written periodic-table lookups or generic sklearn preprocessing when the task depends on materials-aware feature definitions, citations, or featurizer provenance.
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    Py4dstem · charlesxjyang

    Use when the user is working with 4D-STEM, scanning nanobeam diffraction, diffraction datacubes, Bragg disk detection, virtual bright/dark field imaging, center-of-mass/DPC, strain/orientation mapping, ptychography, phase retrieval, or microscope calibration from STEM diffraction data. Prefer py4DSTEM over generic NumPy/scikit-image code when diffraction datacube conventions, detector axes, scan axes, calibration, and 4D-STEM workflows matter.
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    Pymatgen · charlesxjyang

    Use when the user is working with materials structures, compositions, crystallography, Materials Project data, phase diagrams, Pourbaix diagrams, VASP input/output, computed entries, symmetry analysis, oxidation states, diffusion analysis, electronic structures, or conversions between materials data formats. Prefer pymatgen over generic NumPy/Pandas or ASE when the task is materials analysis rather than running a calculator.
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    Impedance · charlesxjyang

    Use when the user is working with electrochemical impedance spectroscopy (EIS), equivalent circuit models, Nyquist/Bode plots, Kramers-Kronig validation, impedance spectra from BioLogic/Gamry/Autolab/VersaStudio/ZView, battery/fuel-cell/corrosion impedance data, or circuit strings such as R0-p(R1,CPE1)-Wo1. Prefer impedance.py over hand-written scipy.optimize fitting when the task is EIS preprocessing, validation, equivalent-circuit fitting, parameter extraction, or impedance-specific plotting.
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