Features

Data access

Dataset is an abstraction of the local file system. Users can add their local dirs into this system then load data that under these dirs in a convenient way.

XenonPy also uses this system to provide some built-in data. Currently, two sets of element-level property data are available out-of-the-box (elements and elements_completed (imputed version of elements)). These data were collected from mendeleev, pymatgen, CRC Hand Book and Magpie.

elements contains 74 element-level properties of 118 elements. Their missing values were statistically imputed by performing the multiple imputation method 1 and stored as elements_completed. Because of the statistical unreliability of the imputation for a subset of properties and heavier atoms that contains many missing values in elements, the elements_completed data set provides only 58 properties of 94 elements (from H to Pu). The following table shows the currently available elemental information.

Element-level properties

feature

description

period

Period in the periodic table

atomic_number

Number of protons found in the nucleus of an atom

mendeleev_number

Atom number in mendeleev’s periodic table

atomic_radius

Atomic radius

atomic_radius_rahm

Atomic radius by Rahm et al

atomic_volume

Atomic volume

atomic_weight

The mass of an atom

icsd_volume

Atom volume in ICSD database

lattice_constant

Physical dimension of unit cells in a crystal lattice

vdw_radius

Van der Waals radius

vdw_radius_alvarez

Van der Waals radius according to Alvarez

vdw_radius_batsanov

Van der Waals radius according to Batsanov

vdw_radius_bondi

Van der Waals radius according to Bondi

vdw_radius_dreiding

Van der Waals radius from the DREIDING FF

vdw_radius_mm3

Van der Waals radius from the MM3 FF

vdw_radius_rt

Van der Waals radius according to Rowland and Taylor

vdw_radius_truhlar

Van der Waals radius according to Truhlar

vdw_radius_uff

Van der Waals radius from the UFF

covalent_radius_bragg

Covalent radius by Bragg

covalent_radius_cordero

Covalent radius by Cerdero et al

covalent_radius_pyykko

Single bond covalent radius by Pyykko et al

covalent_radius_pyykko_double

Double bond covalent radius by Pyykko et al

covalent_radius_pyykko_triple

Triple bond covalent radius by Pyykko et al

covalent_radius_slater

Covalent radius by Slater

c6

C_6 dispersion coefficient in a.u

c6_gb

C_6 dispersion coefficient in a.u

density

Density at 295K

proton_affinity

Proton affinity

dipole_polarizability

Dipole polarizability

electron_affinity

Electron affinity

electron_negativity

Tendency of an atom to attract a shared pair of electrons

en_allen

Allen’s scale of electronegativity

en_ghosh

Ghosh’s scale of electronegativity

en_pauling

Mulliken’s scale of electronegativity

gs_bandgap

DFT bandgap energy of T=0K ground state

gs_energy

Estimated FCC lattice parameter based on the DFT volume

gs_est_bcc_latcnt

Estimated BCC lattice parameter based on the DFT volume

gs_est_fcc_latcnt

Estimated FCC lattice parameter based on the DFT volume

gs_mag_moment

DFT magnetic momenet of T=0K ground state

gs_volume_per

DFT volume per atom of T=0K ground state

hhi_p

Herfindahl−Hirschman Index (HHI) production values

hhi_r

Herfindahl−Hirschman Index (HHI) reserves values

specific_heat

Specific heat at 20oC

gas_basicity

Gas basicity

first_ion_en

First ionisation energy

fusion_enthalpy

Fusion heat

heat_of_formation

Heat of formation

heat_capacity_mass

Mass specific heat capacity

heat_capacity_molar

Molar specific heat capacity

evaporation_heat

Evaporation heat

linear_expansion_coefficient

Coefficient of linear expansion

boiling_point

Boiling temperature

brinell_hardness

Brinell Hardness Number

bulk_modulus

Bulk modulus

melting_point

Melting point

metallic_radius

Single-bond metallic radius

metallic_radius_c12

Metallic radius with 12 nearest neighbors

thermal_conductivity

Thermal conductivity at 25 C

sound_velocity

Speed of sound

vickers_hardness

Value of Vickers hardness test

Polarizability

Ability to form instantaneous dipoles

youngs_modulus

Young’s modulus

poissons_ratio

Poisson’s ratio

molar_volume

Molar volume

num_unfilled

Total unfilled electron

num_valance

Total valance electron

num_d_unfilled

Unfilled electron in d shell

num_d_valence

Valance electron in d shell

num_f_unfilled

Unfilled electron in f shell

num_f_valence

Valance electron in f shell

num_p_unfilled

Unfilled electron in p shell

num_p_valence

Valance electron in p shell

num_s_unfilled

Unfilled electron in s shell

num_s_valence

Valance electron in s shell

For more details on this system, see tutorial/1-dataset.

Access https://github.com/yoshida-lab/XenonPy/blob/master/samples/dataset_and_preset.ipynb to get a runnable script.

Reference

1

Rubin DB. Multiple imputation for nonresponse in surveys. New York: Wiley; 1987.

Descriptor calculation

Compositional descriptors

XenonPy can calculate 290 compositional features for a given chemical composition. This calculation uses the information of the 58 element-level property data recorded in elements_completed. For example, let us consider a binary compound, \(A_{w_A}B_{w_B}\), whose element-level features are denoted by \(f_{A,i}\) and \(f_{B,i} (i = 1, …, 58)\). Then, the 290 compositional descriptors are calculated: for \(i = 1, …, 58\),

  • Weighted average (abbr: ave): \(f_{ave, i} = w_{A}^* f_{A,i} + w_{B}^* f_{B,i}\),

  • Weighted variance (abbr: var): \(f_{var, i} = w_{A}^* (f_{A,i} - f_{ave, i})^2 + w_{B}^* (f_{B,i} - f_{ave, i})^2\),

  • Geometric mean (abbr: gmean): \(f_{gmean, i} = \sqrt[w_A + w_B]{f_{A,i}^{w_A} * f_{V,i}^{w_B}}\),

  • Harmonic mean (abbr: hmean): \(f_{hmean, i} = \frac{w_A +w_B}{\frac{1}{f_{A,i}}*w_A + \frac{1}{f_{B,i}}*w_B}\),

  • Max-pooling (abbr: max): \(f_{max, i} = max{f_{A,i}, f_{B,i}}\),

  • Min-pooling (abbr: min): \(f_{min, i} = min{f_{A,i}, f_{B,i}}\),

  • Weighted sum (abbr: sum): \(f_{sum, i} = w_{A} f_{A,i} + w_{B} f_{B,i}\),

where \(w_{A}^*\) and \(w_{B}^*\) denote the normalized composition summing up to one.

Structural descriptors

Currently, XenonPy implements RDF (radial distribution function) and OFM (orbital field matrix 2) descriptors of crystalline structures. We also provide a compatible API to use the structural descriptors of matminer. You may check the summary table of featurizers in matminer here.

RDKit descriptors

XenonPy also supports molecular descriptors available in the RDKit python package, including 6 sets of fingerprints, each contains corresponding options.

The tutorials at tutorial/2-descriptor demonstrate how to calculate descriptors using XenonPy.descriptor classes.

Access https://github.com/yoshida-lab/XenonPy/blob/master/samples/calculate_descriptors.ipynb to get a runnable script.

Reference

2

Pham et al. Machine learning reveals orbital interaction in materials, Sci Technol Adv Mater. 18(1): 756-765, 2017.

Visualization of descriptor-property relationships

Descriptors on a set of given materials could be displayed on a heatmap plot in order to facilitate the understanding of overall patterns in relation to their properties. The following figure shows an example:

_images/heatmap.jpg

Heatmap of 290 compositional descriptors of 69,640 compounds in Materials Project (upper: volume Å3, lower: density g/cm3 ).

In the heatmap of the descriptor matrix, the 69,640 materials are arranged from the top to bottom by the increasing order of formation energies. Plotting the descriptor-property relationships in this way, we could visually recognize which descriptors are relevant or irrelevant to the prediction of formation energies. Relevant descriptors, which are linearly or nonlinearly dependent to formation energies, might exhibit certain patterns from top to bottom in the heatmap. For example, a monotonically decrease or increase pattern would appear in a linearly dependent descriptor. On the other hand, irrelevant descriptors might exhibit no specific patterns.

See the tutorials for visualization of descriptor-property relationships at tutorial/3-visualization.

Access https://github.com/yoshida-lab/XenonPy/blob/master/samples/visualization.ipynb to get a runnable script.

XenonPy.MDL

XenonPy.MDL is a library of pre-trained models that were obtained by feeding diverse materials data on structure-property relationships into neural networks and some other supervised learning algorithms. The current release (version 0.1.0.beta) contains more than 140,000 models (include private models) on physical, chemical, electronic, thermodynamic, or mechanical properties of small organic molecules (15 properties), polymers/polymer composites (18), and inorganic compounds (12). Pre-trained neural networks are distributed as either the R (MXNet) or Python (PyTorch) model objects. Detailed information about XenonPy.MDL, such as a list of models, properties, source data used for training, and so on, are prepared in this paper 3.

The following lists contain the information of current available pre-trained models and properties.

Information on model sets

id

name

description

1

Stable inorganic compounds
in materials project (MP)
Models in this set are trained on ~20,000 stable inorganic
compounds selected from the materials project.

2

All inorganic compounds
in materials project (MP)
Models in this set are trained on ~70,000 inorganic compounds
selected from the materials project.

3

QM9 Dataset from
Quantum-Machine website
Quantum-Machine project can be access

4

PHYSPROP Dataset

PHYSPROP database contains chemical structures,
names and physical properties for over 41,000 chemicals.

5

Jean-Claude Bradley Open
Melting Point Dataset
Jean-Claude Bradley’s dataset of Open Melting Points.

6

Polymer Genome Dataset (PG)
Polymer Genome is an informatics platform for polymer property
prediction and design using machine learning.
It can be accessed via https://www.polymergenome.org/.
Information of properties

name

system

querying name

Melting Temperature

Organic Polymer

organic.polymer.melting_temperature

Ionization Energy

Organic Polymer

organic.polymer.ionization_energy

Ionic Dielectric Constant

Organic Polymer

organic.polymer.ionic_dielectric_constant

Hildebrand Solubility Parameter

Organic Polymer

organic.polymer.hildebrand_solubility_parameter

Glass Transition Temperature

Organic Polymer

organic.polymer.glass_transition_temperature

Molar Volume

Organic Polymer

organic.polymer.molar_volume

Electron Affinity

Organic Polymer

organic.polymer.electron_affinity

Dielectric Constant

Organic Polymer

organic.polymer.dielectric_constant

Density

Organic Polymer

organic.polymer.density

Cohesive Energy

Organic Polymer

organic.polymer.cohesive_energy

Bandgap

Organic Polymer

organic.polymer.bandgap

Atomization Energy

Organic Polymer

organic.polymer.atomization_energy

Refractive Index

Organic Polymer

organic.polymer.refractive_index

Molar Heat Capacity

Organic Polymer

organic.polymer.molar_heat_capacity

Electronic Dielectric Constant

Organic Polymer

organic.polymer.electronic_dielectric_constant

U0 Hartree

Organic Nonpolymer

organic.nonpolymer.u0_hartree

R2 Bohr2

Organic Nonpolymer

organic.nonpolymer.r2_bohr2

Mu Debye

Organic Nonpolymer

organic.nonpolymer.mu_debye

Lumo Hartree

Organic Nonpolymer

organic.nonpolymer.lumo_hartree

Homo Hartree

Organic Nonpolymer

organic.nonpolymer.homo_hartree

Gap Hartree

Organic Nonpolymer

organic.nonpolymer.gap_hartree

Alpha Bohr3

Organic Nonpolymer

organic.nonpolymer.alpha_bohr3

U Hartree

Organic Nonpolymer

organic.nonpolymer.u_hartree

Zpve Hartree

Organic Nonpolymer

organic.nonpolymer.zpve_hartree

Bp

Organic Nonpolymer

organic.nonpolymer.bp

Cv Calmol-1K-1

Organic Nonpolymer

organic.nonpolymer.cv_calmol-1k-1

Tm

Organic Nonpolymer

organic.nonpolymer.tm

G Hartree

Organic Nonpolymer

organic.nonpolymer.g_hartree

H Hartree

Organic Nonpolymer

organic.nonpolymer.h_hartree

Density

Inorganic Crystal

inorganic.crystal.density

Volume

Inorganic Crystal

inorganic.crystal.volume

Refractive Index

Inorganic Crystal

inorganic.crystal.refractive_index

Band Gap

Inorganic Crystal

inorganic.crystal.band_gap

Dielectric Const Electron

Inorganic Crystal

inorganic.crystal.dielectric_const_elec

Fermi Energy

Inorganic Crystal

inorganic.crystal.efermi

Total Magnetization

Inorganic Crystal

inorganic.crystal.total_magnetization

Dielectric Const Total

Inorganic Crystal

inorganic.crystal.dielectric_const_total

Final Energy Per Atom

Inorganic Crystal

inorganic.crystal.final_energy_per_atom

Formation Energy Per Atom

Inorganic Crystal

inorganic.crystal.formation_energy_per_atom

XenonPy.MDL provides a rich set of APIs to give users the abilities to interact with the pre-trained model database. Through the APIs, users can search for a specific subset of models by keywords and download them via HTTP. The tutorials at tutorial/5-mdl will show you how to interact with the database in XenonPy (via the API querying).

Access https://github.com/yoshida-lab/XenonPy/blob/master/samples/pre-trained_model_library.ipynb to get a runnable script.

Transfer learning

Transfer learning is an increasingly popular framework in machine learning that covers a broad range of methodologies for which a model trained for one task is re-purposed to another related task 4 5. In general, the need for transfer learning occurs when there is a limited supply of training data, but there are many other promising applications in materials science as described in 3.

XenonPy offers a simple-to-use toolchain to seamlessly perform transfer learning with the given pre-trained models. Given a target property, by using the transfer learning module of XenonPy, a source model can be treated as a generator of machine learning acquired descriptors, so-called the neural descriptors, as demonstrated in 3.

See tutorials at tutorial/6-transfer-learning for learning how to do the frozen feature transfer learning in XenonPy.

Access https://github.com/yoshida-lab/XenonPy/blob/master/samples/transfer_learning.ipynb to get a runnable script.

Reference

3(1,2,3)

Yamada, H., Liu, C., Wu, S., Koyama, Y., Ju, S., Shiomi, J., Morikawa, J., Yoshida, R. Transfer learning: a key driver of accelerating materials discovery with machine learning, in preparation.

4

Karl, W.; Khoshgoftaar, T. M.; Wang, D. J. of Big Data 2016, 3, 1–40.

5

Chuanqi, T.; Fuchun, S.; Tao, K.; Wenchang, Z.; Chao, Y.; Chunfang, L. arXiv 2018, abs/1808.01974.