Skip to main content

PicoMats logo

Use the material. Sustain the assumptions.
Reduce uncertainty by making every assumption explicit.

Overview

Version License Python Version PyPI Downloads

PicoMats is an assumption-driven material ontology that sustains assumptions throughout your pipeline. It provides unit-informed material definitions with accompanying assumptions.

This is the demo/alpha release of PicoMats. The library and its ontology will be expanded.

- Follows a computational ontology called `Abstract-Fundamental Ontology`.
- Uses `UnitValues` and `PicoUnits` for encoding typed numerical definitions.
- Tracks assumptions alongside material definitions to reduce model uncertainty.

What is the Abstract-Fundamental Ontology?

It is a computational abstraction for both reductionist and pragmatist applications.

The model is based on two categories:

Abstract:       Defined by what it does     (characteristics).
Fundamental:    Defined by what it is       (structure/state).

Why does it exist?

The ontology emerges from this simple series:

Let's model a ball rolling down a ramp.
        ↓   
What forces act on the ball?
        ↓
Gravity, electromagnetic repulsion, and friction.
        ↓
How do we model friction?
        ↓
A coefficient? Isn't that arbitrary?
        ↓
Why not just model it?
        ↓
What exactly is friction?
        ↓
Oh, random microscopic interactions...

It's possible to model but computationally impractical for most applications.
Hence, the abstract section exists for empirical measurements.

But abstract isn't always the right model...

I want to research superconductors.
        ↓
Oh, my temperature range is 0 K to 200 K.
        ↓
Where do I get material definitions for that range?
        ↓
I'll just interpolate the standard Niobium definition.
        ↓
Actually, how were these measurements obtained?

When asking a fundamental question, it's better to build from fundamental properties.
Hence the fundamental section.


Quick Start

A step-by-step introduction is available in example/.

from picomats import m
from picomats import Materials

# Pulls materials into the simulation
copper = Materials.abstract.pure.copper

density = copper.physical.density
volume = 0.1 * m ** 3

mass = volume * density
# > 893.0 (kg)

Installation

To install:

pip install PicoMats

Documentation

Full documentation is available in the docs/ folder, including API reference, changelog, and contributors.


Metadata

Release files for picomats 0.1.0

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

Source distribution (sdist)

Source distribution for picomats 0.1.0
File Size Uploaded
picomats-0.1.0.tar.gz 8.0 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for picomats 0.1.0
File Interpreter ABI Platform
picomats-0.1.0-py3-none-any.whl Python 3 none any Details

Total release size: 12.3 kB

Release files / picomats-0.1.0.tar.gz

Download URL picomats-0.1.0.tar.gz
Size 8.0 kB
Tags Source
SHA-256 checksum
How to use checksums
5dc55506972fd4fe5e1c84c59b84c429b909656cdafe0cc6402615d2c0dfdfe9
BLAKE2b-256 checksum
How to use checksums
db7e10d6df9b3f810f0be417335614df2a7a726b01513806fc8db36641be4495
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 20, 2026.

Transparency log

Release files / picomats-0.1.0-py3-none-any.whl

Download URL picomats-0.1.0-py3-none-any.whl
Size 4.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
93fe8954c3db60fb027707aaf96bbccc31f41c4b4b95da275c1afa90bb16095a
BLAKE2b-256 checksum
How to use checksums
66bdda1d3661ae151746be05c60df8a14ed7ef954ee4c9385f364448cf4bebdf
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 20, 2026.

Transparency log

Release history Release notifications | RSS feed

0.1.3

2 release files

0.1.2

2 release files

0.1.1

2 release files

This release

0.1.0 This release

2 release files

0.0.0

2 release files

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