What scientific domains does Theory2 Physics cover?

It covers a wide range of fields including theoretical physics (Lie algebras), quantum chemistry, bioinformatics, graph theory, and discrete optimization.

How does the skill handle physics-informed machine learning?

It provides specialized tools for training Fourier Neural Operators (FNO), Physics-Informed Neural Networks (PINN), and E3NN Equivariant Networks for molecular systems.

Does it support bioinformatics tasks?

Yes, it includes tools for DNA/RNA sequence analysis, protein molecular weight calculation, isoelectric point computation, and PDB structure analysis.

Can this skill help with formal mathematical proofs?

Yes, it includes a RobustLeanProver that integrates with Lean 4 to perform automated proof searches, tactic selection, and proof verification.

What is the 'cross-check' feature?

The cross-check tool validates scientific claims by comparing results across symbolic, numerical, and experimental methodologies to ensure accuracy and minimize error.

Theory2 Physics & Math Tooling

Name: Theory2 Physics & Math Tooling
Author: slapglif

byslapglif

Data Science & ML

Performs advanced mathematical physics computations, symbolic algebra, and automated theorem proving using the Theory2 suite.

About

Theory2 Physics is a comprehensive computational suite for Claude Code designed for researchers and engineers in mathematical physics, quantum chemistry, and scientific computing. It provides a robust interface for symbolic math including Lie algebra computations, numerical methods for molecular energy calculations, and physics-informed machine learning models like Fourier Neural Operators. With integrated support for Lean 4 theorem proving and a rigorous cross-validation methodology, it enables users to solve complex PDEs, analyze biological sequences, and verify mathematical claims with high scientific integrity.

Key Features

Symbolic mathematics for Lie algebras, calculus, and equation solving
Physics-informed machine learning including FNO, PINN, and E3NN models
Automated Lean 4 theorem proving with intelligent tactic selection
Bioinformatics tools for protein structure analysis and sequence transcription
Quantum chemistry simulations using DFT, CCSD, and Hartree-Fock methods
0 GitHub stars

Use Cases

Analyzing molecular structures and quantum circuits for scientific research
Solving complex partial differential equations (PDEs) using neural operators
Verifying theoretical physics claims through multi-method cross-validation

About

Key Features

Symbolic mathematics for Lie algebras, calculus, and equation solving
Physics-informed machine learning including FNO, PINN, and E3NN models
Automated Lean 4 theorem proving with intelligent tactic selection
Bioinformatics tools for protein structure analysis and sequence transcription
Quantum chemistry simulations using DFT, CCSD, and Hartree-Fock methods
0 GitHub stars

Use Cases

Analyzing molecular structures and quantum circuits for scientific research
Solving complex partial differential equations (PDEs) using neural operators
Verifying theoretical physics claims through multi-method cross-validation