Thermo-ML

Thermodynamics powered by Machine Learning.

Author:	Kota Matsuo ( Linkedin )
Version:	0001

Contents

• What is Thermo-ML?
• Goal of this project
• Road map
• Installation
• Examples
  • 1. Parse chemical formula into atoms
  • 2. Retrieve physical & chemical properties of atoms
  • 3. Retrieve fundamental constants of physics & chemistry
  • 4. Retrieve properties of compounds
• Who’s the author?
• Citation

What is Thermo-ML?

Thermo-ML is a python library for scientists in the field of thermodynamics, who want to tap into the power of machine learning to make accurate predictions. (If you have heard of CALPHAD, ChemSage, FactSage, Thermochem, this project might interest you.)

Goal of this project

There are two goals to this project:

1. Make physical & chemical data of atoms & compounds accessible with once click.
2. Develop an AI that accurately predicts unknown properties of elements or even unknown compounds, by learning the hidden relationships between the properties of compounds and its constituent atoms.

Road map

• Mar 2021
  • ✅ Started the project
  • ✅ Added “parse.py” module to parse chemical formula into its constituent atoms
• April 2021
  • ✅ Added "database.get_fundamental_constants" module to get major physical/chemical fundamental constants
• May 2021
  • ✅ Refactored “parse.py” module & added test code
  • ✅ Added "database.get_atoms" module to get properties of atoms (e.g. ionization energy, electronegativity, atomic radius, etc).
  • 🛠 Add module to get thermodynamic properties of compounds (e.g. enthalpy, entropy, heat capacity, etc).
    • Idea 1: Convert JANAF database to ML readable format
    • Idea 2: Convert open Thermo-Calc Database Format (TDB) to ML readable format
• June 2021
  • 🛠 Add AI module for predicting enthalpy of formation of oxide compounds just from its chemical formula, using properties of its constituent atoms.
    • Note: The reason why I focus on oxides is; (1) oxides are common, and (2) to keep the charge on cations rather constant. If the charge changes, electronegativity changes, and if electronegativity changes, so does enthalpy. For more info see Kaufman, George B. "Inorganic chemistry: principles of structure and reactivity" (1993) P.184 equation 5.62 and Fig. 5.32.
    • Idea 1: Multilinear regression w/ constraints
    • Idea 2: Quadratic programming w/ constraints
    • Idea 3: Symbolic regression + genetic programming
    • Idea 4: Deep Learning
• July 2021
  • 🛠 Add AI module for predicting electronegativity of elements, including transition metals.
    • Note: Leland C. Allen mentioned that electronegativity of transition metals are difficult to obtain, in his paper "Electronegativity Is the Average One-Electron Energy of the Valence-Shell Electrons in Ground-State Free Atoms".
• Aug 2021
  • 🛠 Add AI module for predicting percentage ionic character of bonds.
    • Note: Linus Pauling said in his book that "We cannot hope to formulate an expression for the partial ionic character of bonds that will be accurate".
• Sept 2021
  • 🛠 Add AI module for predicting entropy of formation of compounds just from its chemical formula, using properties of its constituent atoms.
• Oct 2021
  • Hmm what else can I do...

Installation

1. Clone the repository using

git clone https://github.com/soap-tastes-ok/thermo-ml.git

2. Install all required dependencies using

pip install -r /your/directory/thermo-ml/requirements.txt

3. Append library path to system path

import sys
package = '/your/directory/thermo-ml'
if package not in sys.path:
    sys.path.append(package)

Examples

1. Parse chemical formula into atoms

To parse a chemical formula into it's constituent atoms, use the ChemParser module.

>>> from thermo_ml import parse
>>>
>>> CP = parse.ChemParser()
>>> CP.atoms("Ca2SiO3(OH)2")

[{'Ca': 2.0, 'Si': 1.0, 'O': 5.0, 'H': 2.0}]

2. Retrieve physical & chemical properties of atoms

To retrieve atomic properties data, use the database.get_atoms module.

>>> from thermo_ml import database
>>>
>>> atoms = ['H', 'C', 'Ca', 'Si', 'Li']
>>> properties = [
>>>     "Z", "Symbol", "Group",
>>>     "Atomic radii (pm)",
>>>     "Atomic weight (a.m.u.)",
>>>     "Valence electrons"
>>> ]
>>> df = database.get_atoms(atoms, properties)

Z	Symbol	Group	Atomic radii (pm)	Atomic weight (a.m.u.)	Valence electrons
1	H	1	25	1.00794	1
3	Li	1	145	6.941	1
6	C	14	70	12.0107	4
14	Si	14	110	28.0855	4
20	Ca	2	180	40.078	2

3. Retrieve fundamental constants of physics & chemistry

To retrieve fundamental constants, use the database.get_fundamental_constants module.

>>> from thermo_ml import database
>>> df = database.get_fundamental_constants()

	quantity	symbol	value	unit	formula	Definition
0	Speed of light	c	2.99792e+08	ms^(-1)	nan	Speed of photon in vacuum
1	Magnetic constant	μ_0	1.25664e-06	NA^(-2)	nan	Magnetic permeability in vacuum
2	Electric constant	ε_0	8.85419e-12	Fm^(-1)	nan	Electric field permittivity in vacuum
...	...	...	...	...	...	...
19	Stefan-boltzman constant	σ	5.6704e-08	W m^(-2) K^(-4)	((π^2 / 60) k^4) / (ℏ^3 c^2)	Constant of proportionality in Stefan-Boltzmann law of Blackbody radiation. Used to measure the amount of heat radiated from the black body, and to convert temperature (K) to units for intensity (W.m-2) which is basically Power per unit area.
20	Electron volt	eV	1.60218e-19	J	e/C	Energy gained by the charge of a single electron moved across an electric potential difference of 1 volt. Thus it is 1 volt (1 J/C) multiplied by the electron charge (1.602176565(35)×10−19 C)
21	Unified atomic mass unit	u	1.66054e-27	kg	(10^(-3) kg/mol ) / N_A	The dalton or unified atomic mass unit is a unit of mass widely used in physics and chemistry. It is defined as 1/12 of the mass of an unbound neutral atom of carbon-12 in its nuclear and electronic ground state and at rest

4. Retrieve properties of compounds

TBD

Who’s the author?

I’m currently a machine learning engineer in Tokyo, who was previously doing research in computational thermodynamics & developing FactSage @McGill University. (Linkedin)

I will work on this during weekends, so please wait patiently. If you are interested to follow this project, please hit the star to let me know you are there and I’ll try to work faster ;)

Citation

To cite Thermo-ML in publications, please use:

Kota Matsuo and Contributors (2021-). Thermo-ML: Thermodynamics powered with Machine learning.
https://github.com/soap-tastes-ok/thermo-ml.git.