Skip to main content

Documentation

Introduction

What is TD-SCHA?

TD-SCHA is a theory to simulate quantum nuclear motion in materials with strong anharmonicity.
TD-SCHA stands for Time-Dependent Self-Consistent Harmonic Approximation, and it is the dynamical extension of the SCHA theory, that can describe equilibrium properties of materials accounting for both quantum and dynamical nuclear fluctuations.

The tdscha Python library allows performing dynamical linear response calculations on top of the equilibrium results (computed with the python-sscha package).

Why would I need tdscha?

A Tool for Advanced Material Simulations

Enhanced Simulation Capabilities
TD-SCHA is an essential tool for researchers and professionals in material science, particularly for simulating transport or thermal properties of materials, phase diagrams, and phonon-related properties.

Integration with python-sscha
Seamless integration with python-sscha allows for the inclusion of both thermal and quantum phonon fluctuations in ab initio simulations.

Leveraging the SSCHA Method

Quantum and Thermal Fluctuations
The Stochastic Self-Consistent Harmonic Approximation (SSCHA) is a full-quantum method optimizing the nuclear wave-function or density matrix to minimize free energy, crucial for simulating highly anharmonic systems.

Efficiency and Cost-Effectiveness
Despite its full quantum and thermal nature, the computational cost is comparable to classical molecular dynamics, enhanced by the algorithm's ability to exploit crystal symmetries.

User-Friendly and Versatile

Python Library and Stand-alone Software
Available both as a Python library and stand-alone software, with input scripts sharing syntax with Quantum ESPRESSO.

Broad Compatibility
Can couple with any ab initio engine and interacts through the Atomic Simulation Environment (ASE) with an interface for automatic job submission on remote clusters.

Getting Started

Easy to Use
User-friendly with short, readable input files and comprehensive tutorials.

Download and Explore
Download and install python-sscha, and start exploring the tutorials to enhance your material simulation projects.

How to install

You need to have installed python-sscha and CellConstructor to work with tdscha.
Please read the installation guide for those packages before proceeding further.
You may find all instructions on the official website.

To install from PyPI, simply type:

pip install tdscha

You can alternatively clone the repository from GitHub and install the package with:

pip install .

Quick start

Go to the tutorials

Metadata

Release files for tdscha 1.6.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for tdscha 1.6.2
File Size Uploaded Provenance
tdscha-1.6.2.tar.gz 5.4 MB Without provenance Details

Release files / tdscha-1.6.2.tar.gz

Download URL tdscha-1.6.2.tar.gz
Size 5.4 MB
Tags Source
SHA-256 checksum
How to use checksums
d45ef880d294ef9f358b4d040cc98677f0bd7d0627dc02dc4c0932931b9b7b35
BLAKE2b-256 checksum
How to use checksums
d21a96cbb33d894d51f03cf284a024aa686c0e2a34ee0043109ccaa242d2d17e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.13.7

Security for tdscha 1.6.2

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page