5 skills · sorted by relevance
Check physical units, propagate measurement uncertainty through calculations, and format results with meaningful precision. Local tools also inspect Python code for unit and uncertainty mistakes and assess whether calculated magnitudes are physically plausible.
Query physics databases — NIST CODATA constants, NIST Chemistry WebBook, Materials Project, Particle Data Group (PDG), OEIS sequences. Always use these instead of hardcoding physical constants.
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Chemical equilibrium, chemical kinetics, and electrochemistry — the quantitative core of physical chemistry. Covers equilibrium constants (Keq, Kc, Kp), Le Chatelier's principle, ICE tables, buffers and the Henderson-Hasselbalch equation, solubility product (Ksp) and the common-ion effect, rate laws, reaction order, integrated rate laws, half-life, the Arrhenius equation and activation energy, catalysis, redox reactions, galvanic and electrolytic cells, standard cell potentials, and the Nernst equation. Use when solving quantitative problems about how far a reaction goes (equilibrium), how fast it goes (kinetics), or how it converts chemical energy to electrical energy (electrochemistry).
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Generate correct MATLAB code using the Symbolic Math Toolbox. Use when the user asks for symbolic computations, analytical solutions, symbolic differentiation/integration, equation solving, or converting symbolic results to numeric MATLAB functions. Also use when converting differential equations to transfer functions or state-space form, extracting PDE coefficients (pdeCoefficients), symbolic-to-C code generation (matlabFunction + codegen), symbolic matrix variables (symmatrix), physical units and constants (symunit, newUnit), or rewriting/combining algebraic expressions (rewrite, combine).
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Physical applications of mathematics. Constants, quantum mechanics, measurement — where abstract meets embodied.
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