UF3: a python library for generating ultra-fast interatomic potentials

Last update: Nov 13, 2022

Overview

Ultra-Fast Force Fields (UF3)

S. R. Xie, M. Rupp, and R. G. Hennig, "Ultra-fast interpretable machine-learning potentials", preprint arXiv:2110.00624 (2021).

All-atom dynamics simulations have become an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. The UF3 framework is built to address this challenge by combinining effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, and as fast as the fastest traditional empirical potentials.

Documentation: https://uf3.readthedocs.io/

This repository is still under construction. Please feel free to open new issues for feature requests and bug reports.

Setup

conda create --name uf3_env python=3.7
conda activate uf3_env
git clone https://github.com/uf3/uf3.git
cd uf3
pip install wheel
pip install -r requirements.txt
pip install numba
pip install -e .

Getting Started

Please see the examples in uf3/examples/tungsten_extxyz for basic usage.

Overviews for individual modules can be found in uf3/examples/modules (WIP).

Standalone scripts and configuration generators/parsers are in development.

Optional Dependencies

Elastic constants:

pip install setuptools_scm
pip install "elastic>=5.1.0.17"

Phonon spectra:

pip install spglib
pip install seekpath
pip install "phonopy>=2.6.0"

LAMMPS interface:

conda install numpy==1.20.3 --force-reinstall
conda install -c conda-forge lammps --no-update-deps

Dependencies

We rely on ase to handle parsing outputs from atomistic codes like LAMMPS, VASP, and C2PK.
We use Pandas to keep track of atomic configurations and their energies/forces as well as organizing data for featurization and training.
B-spline evaluations use scipy, numba, and ndsplines.
PyTables is used for reading/writing HDF5 files.

Comments

uf3 with VASP

Hi, Stephen, I heard about this project in Prof. Hennig's talk yesterday. I'm wondering is the present version able to train the uf3 potentials with the VASP's forces and energies data? Thank you very much!

Yuewen

opened by yw-fang 6
Unreasonable spline curves

Hi, I am trying to fit a UF2 model for SiC, based on Formation energies and forces, the dataset generated by annealing MD calculation 200~ snapshot configurations. Here is the splines curve I got for 5/200, 20/200, 40/200, and 80/200 training datasets, respectively.

Where, ridge_1b=1e-6, curvature_2b=1e-4, batch_size=5, weight=0.5

Besides, I also tested smaller curvature_2b values, eg. 1e-2, even 1e-1, unfortunately, as shown below (Training:80/200, curvature_2b=1e-1, batch_size=5), it's also the same case. I would appreciate it if you have any clue about that!!

However, the prediction looks perfect, I kinda confused by this.

opened by MengnanCui 6
Multicomponent three-body contributions wrong with PBC
Problem description

When calculating three-body energy and force contributions, results change depending on whether interactions are computed across periodic boundaries or not.

To reproduce, I have created a 10x10x10 cell with four atoms. When the atoms are placed in the middle of the cell, such that activating or deactivating PBC does not change results, contributions are computed correctly. However, if atoms are moved towards a boundary and one atom moves to the top of the box (no atoms have positions outside the box), contributions are incorrect, possibly added multiple times.

The potential file only contains coefficients for three-body, one- and two-body coefficients are 0. All coefficients for three-body contributions are the same.

Potential file: https://pastebin.com/bfD8R0Rb

Atoms in middle of cell (test_m.data)

4 atoms 2 atom types 0.0 10 xlo xhi 0.0 10 ylo yhi 0.0 10 zlo zhi Atoms 1 2 0 4 5 2 1 0 4 6 3 2 0 5 5 4 1 0 4 4

Atoms close boundary (test_b.data)

4 atoms 2 atom types 0.0 10 xlo xhi 0.0 10 ylo yhi 0.0 10 zlo zhi Atoms 1 2 0 4 0.5 2 1 0 4 1.5 3 2 0 5 0.5 4 1 0 4 9.5

To reproduce the problem, use the files above and run:

# Imports from uf3.representation import bspline from uf3.representation import process from uf3.util import json_io from uf3.regression import least_squares from uf3.forcefield import calculator from uf3.forcefield import lammps from uf3.util import plotting from uf3.data import io from uf3.data import composition import ase model = least_squares.WeightedLinearModel.from_json("model.json") calc = calculator.UFCalculator(model) test = ase.io.lammpsdata.read_lammps_data("test_m.data", style="atomic") test.symbols = "OZrOZr" test.pbc=True test.wrap() test.set_calculator(calc) print(test.get_potential_energy()) test = ase.io.lammpsdata.read_lammps_data("test_b.data", style="atomic") test.symbols = "OZrOZr" test.pbc=True test.wrap() test.set_calculator(calc) print(test.get_potential_energy())

The energies should match, since the atoms are simply moved by 5 units in z-direction, but don't:

-43168.42282486064 -37056.21139709316

While this only demonstrates the issue for the UFCalculator, it could potentially affect training if the same issue occurs when generating supercells there.

Possible causes could be symmetries, there was a fix that ensures symmetry when j and k are switched, or interactions that select the wrong central atom, but those are a shot in the dark.
opened by henk789 1
Model coefficients after loading models incorrect

Problem description

After loading a model from a json file, the model's performance is significantly decreased:

| | MAE Energies | MAE Forces | RMSE Energies | RMSE Forces | |----------------------|--------------|------------|---------------|-------------| | Model after training | 0.005 | 0.184 | 0.006 | 0.250 | | Model after loading | 0.026 | 0.245 | 0.027 | 0.317 |

This behavior appears to be because of the compress_3B and decompress_3B methods in bspline.py.

Decompressing coefficients from a model and compressing them again results in different arrays of coefficients and cause different results after loading.

Solution

Changing the decompress method to work correctly results in wrong coefficients for the UFCalculator and the LAMMPS implementation. A possible fix could, therefore, be a distinction between loading coefficients that had flat_weights applied to them during decompressing and multiplying all coefficients by 0.5 and applying the flat_weights in all other cases when compressing coefficients.

This results in correct model loading, but I cannot assess possible side effects, so input on this is appreciated.

opened by henk789 1
Training with a Single Image

When training with a single image in the database, we get an error because the standard deviation is 0. While training with a single image is rare, I believe this should be considered a base case.

opened by EboniWilliams 1
Implementation of Custom Knot Strategy is Broken

"Custom" is an option for knot strategy. However, its implementation is currently broken. That is, it will result in an error when used. The problem seems to be that it has "pass" on the get_spacer function so we get the following error: UnboundLocalError: local variable 'spacing_function' referenced before assignment

opened by EboniWilliams 1

WeightedLinearModel.save JSON precision too small

Problem description

When exporting a trained model using WeightedLinearModel.save, floats are formatted using Python's format with the "g" parameter that has a default precision of 6.

The class that handles this is CompactJSONEncoder:

https://github.com/uf3/uf3/blob/f2ad1a23f5b38695894378803dc54f25139b1a3d/uf3/util/json_io.py#L138-L140

For a model I trained, this precision is too small, resulting in prediction errors when loading the model from the .json file.

Example

I attached the solutions of the model here. The first output shows the solutions directly after training, and the second output shows the solutions after saving and loading.

Solutions after training

{'solution': {'Zr': -20560.33609234794,
'O': -41120.412798809746,
('O',
 'O'): array([ 5.26435439e+00,  5.20311465e+00,  5.08063516e+00,  4.89691593e+00,
        4.65195696e+00,  4.34575824e+00,  3.97831977e+00,  3.54964157e+00,
        3.05972362e+00,  2.50856592e+00,  1.89616849e+00,  1.22868659e+00,
        7.30053245e-01,  4.31556715e-01,  2.46165334e-01,  1.24437004e-01,
        5.93001720e-02,  2.74566934e-02,  1.30984740e-02,  3.16545758e-03,
        8.15071387e-04,  1.64258524e-04, -6.46071392e-03, -9.32398478e-03,
       -1.06880467e-02,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00]),
('Zr',
 'O'): array([ 2.57773141,  2.48379049,  2.29590865,  2.01408589,  1.63832222,
        1.16861763,  0.60497211, -0.05261432, -0.80414166, -1.64960993,
       -2.08524704, -1.9352091 , -1.61253899, -1.27462536, -0.97011593,
       -0.72222357, -0.53059997, -0.37511157, -0.26761948, -0.19630742,
       -0.1364386 , -0.08911125, -0.05464061, -0.02995521, -0.0185196 ,
        0.        ,  0.        ,  0.        ]),
('Zr',
 'Zr'): array([7.63423726, 7.59087987, 7.50416508, 7.37409289, 7.20066331,
       6.98387633, 6.72373196, 6.42023019, 6.07337103, 5.68315447,
       5.24958052, 4.77264917, 4.25236043, 3.68871429, 3.08171076,
       2.43142205, 1.77394657, 1.3332101 , 0.99423793, 0.73174477,
       0.51741219, 0.35220643, 0.2297151 , 0.13608243, 0.05597425,
       0.        , 0.        , 0.        ])},
'knots': {('O',
 'O'): array([1.0e-06, 1.0e-06, 1.0e-06, 1.0e-06, 2.0e-01, 4.0e-01, 6.0e-01,
       8.0e-01, 1.0e+00, 1.2e+00, 1.4e+00, 1.6e+00, 1.8e+00, 2.0e+00,
       2.2e+00, 2.4e+00, 2.6e+00, 2.8e+00, 3.0e+00, 3.2e+00, 3.4e+00,
       3.6e+00, 3.8e+00, 4.0e+00, 4.2e+00, 4.4e+00, 4.6e+00, 4.8e+00,
       5.0e+00, 5.0e+00, 5.0e+00, 5.0e+00]),
('Zr',
 'O'): array([1.0e-06, 1.0e-06, 1.0e-06, 1.0e-06, 2.0e-01, 4.0e-01, 6.0e-01,
       8.0e-01, 1.0e+00, 1.2e+00, 1.4e+00, 1.6e+00, 1.8e+00, 2.0e+00,
       2.2e+00, 2.4e+00, 2.6e+00, 2.8e+00, 3.0e+00, 3.2e+00, 3.4e+00,
       3.6e+00, 3.8e+00, 4.0e+00, 4.2e+00, 4.4e+00, 4.6e+00, 4.8e+00,
       5.0e+00, 5.0e+00, 5.0e+00, 5.0e+00]),
('Zr',
 'Zr'): array([1.0e-06, 1.0e-06, 1.0e-06, 1.0e-06, 2.0e-01, 4.0e-01, 6.0e-01,
       8.0e-01, 1.0e+00, 1.2e+00, 1.4e+00, 1.6e+00, 1.8e+00, 2.0e+00,
       2.2e+00, 2.4e+00, 2.6e+00, 2.8e+00, 3.0e+00, 3.2e+00, 3.4e+00,
       3.6e+00, 3.8e+00, 4.0e+00, 4.2e+00, 4.4e+00, 4.6e+00, 4.8e+00,
       5.0e+00, 5.0e+00, 5.0e+00, 5.0e+00])}}

Solutions after saving and loading

{'solution': {'Zr': -20560.3,
 'O': -41120.4,
 ('O',
  'O'): array([ 5.26435e+00,  5.20311e+00,  5.08064e+00,  4.89692e+00,
         4.65196e+00,  4.34576e+00,  3.97832e+00,  3.54964e+00,
         3.05972e+00,  2.50857e+00,  1.89617e+00,  1.22869e+00,
         7.30053e-01,  4.31557e-01,  2.46165e-01,  1.24437e-01,
         5.93001e-02,  2.74567e-02,  1.30984e-02,  3.16543e-03,
         8.15054e-04,  1.64246e-04, -6.46072e-03, -9.32399e-03,
        -1.06880e-02,  0.00000e+00,  0.00000e+00,  0.00000e+00]),
 ('Zr',
  'O'): array([ 2.57773  ,  2.48379  ,  2.29591  ,  2.01409  ,  1.63832  ,
         1.16862  ,  0.604972 , -0.0526146, -0.804142 , -1.64961  ,
        -2.08525  , -1.93521  , -1.61254  , -1.27463  , -0.970116 ,
        -0.722224 , -0.5306   , -0.375112 , -0.26762  , -0.196307 ,
        -0.136439 , -0.0891113, -0.0546406, -0.0299552, -0.0185196,
         0.       ,  0.       ,  0.       ]),
 ('Zr',
  'Zr'): array([7.63424  , 7.59088  , 7.50417  , 7.37409  , 7.20066  , 6.98388  ,
        6.72373  , 6.42023  , 6.07337  , 5.68316  , 5.24958  , 4.77265  ,
        4.25236  , 3.68872  , 3.08171  , 2.43142  , 1.77395  , 1.33321  ,
        0.994238 , 0.731745 , 0.517412 , 0.352207 , 0.229715 , 0.136083 ,
        0.0559743, 0.       , 0.       , 0.       ])},
'knots': {('O',
  'O'): array([1.0e-06, 1.0e-06, 1.0e-06, 1.0e-06, 2.0e-01, 4.0e-01, 6.0e-01,
        8.0e-01, 1.0e+00, 1.2e+00, 1.4e+00, 1.6e+00, 1.8e+00, 2.0e+00,
        2.2e+00, 2.4e+00, 2.6e+00, 2.8e+00, 3.0e+00, 3.2e+00, 3.4e+00,
        3.6e+00, 3.8e+00, 4.0e+00, 4.2e+00, 4.4e+00, 4.6e+00, 4.8e+00,
        5.0e+00, 5.0e+00, 5.0e+00, 5.0e+00]),
 ('Zr',
  'O'): array([1.0e-06, 1.0e-06, 1.0e-06, 1.0e-06, 2.0e-01, 4.0e-01, 6.0e-01,
        8.0e-01, 1.0e+00, 1.2e+00, 1.4e+00, 1.6e+00, 1.8e+00, 2.0e+00,
        2.2e+00, 2.4e+00, 2.6e+00, 2.8e+00, 3.0e+00, 3.2e+00, 3.4e+00,
        3.6e+00, 3.8e+00, 4.0e+00, 4.2e+00, 4.4e+00, 4.6e+00, 4.8e+00,
        5.0e+00, 5.0e+00, 5.0e+00, 5.0e+00]),
 ('Zr',
  'Zr'): array([1.0e-06, 1.0e-06, 1.0e-06, 1.0e-06, 2.0e-01, 4.0e-01, 6.0e-01,
        8.0e-01, 1.0e+00, 1.2e+00, 1.4e+00, 1.6e+00, 1.8e+00, 2.0e+00,
        2.2e+00, 2.4e+00, 2.6e+00, 2.8e+00, 3.0e+00, 3.2e+00, 3.4e+00,
        3.6e+00, 3.8e+00, 4.0e+00, 4.2e+00, 4.4e+00, 4.6e+00, 4.8e+00,
        5.0e+00, 5.0e+00, 5.0e+00, 5.0e+00])}}

The most apparent difference is in the one-body offsets: After training: 'Zr': -20560.33609234794 After loading: 'Zr': -20560.3

Impact on prediction accuracy

This decreased precision noticeably affects the model's performance:

Model with original precision:

Loaded model with 6 digit precision:

Solution

Increasing the accuracy to 20 digits, using format(0, ".20g") instead of format(0, "g"), solves the issue for my model. However, I cannot assess whether this precision is large enough for other models or if other issues arise from this. Maybe someone has input on this so that this issue can be resolved.

opened by henk789 1

Two-and-three body support for UFCalculator and UFLammps
[WIP] Three-body contributions for UFCalculator for multicomponent systems are experiencing some minor bugs

Three-body contributions for UFLammps will require a tensor-product spline evaluator in C++ for LAMMPS

duplicate invalid
opened by sxie22 1
UF3 PyPi Setup
This PR introduces the required changes to publish the package automatically to PyPi:

Gather all the modules and submodules correctly in setup.py

Link this repository as the project website

Add __init__py to uf3.forcefield.properties

Add MANIFEST.in to upload requirements.txt and readme.rst correctly

Update requirements.txt to include all required and optional dependencies. This breaks pip install -r requirement.txt, but this step can be omitted as pip install . installs requirements automatically.

Update tests to use new install mechanism (No more manual dependency installing)

Add script to automatically publish to PyPi. This step can be extended to fetch the release number from the GitHub release if the pipeline works correctly.

To complete the setup, a PyPi account secret needs to be configured in GitHub. This secret needs to have full account privileges for the first release, but can then be adapted to only allow access to the correct package.

A demo of the pipeline has been tested in https://github.com/henk789/uf3 and is available at https://test.pypi.org/project/uf3/.
opened by henk789 0
Gather changes for v0.3.0 release
Prepare a new release to fix critical issues with model loading and multi-component systems:

#28 Models were not correctly loaded from json files

#30 Interactions across PBCs were assigned wrong element types, causing the model to learn on wrong interactions
opened by henk789 0
Correct element sorting method

Issue description

The current element sorting method returns lists and sorts every element. This leads to runtime exceptions when loading models and incorrect three-body interactions. This issue is fixed by using the sort_interaction_symbols method that returns tuples and fixes central elements.

opened by henk789 0
[WIP] Command line interface
This PR addresses #4 and adds a functional CLI.

Added Features

The following functionalities are implemented:

Generating initial config with specified atoms

[x] Create initial config with degree and atoms

Collecting DFT data

[x] Collecting DFT data from single '.xyz' file/list of '.xyz' files

[x] Automatically searching working directory for '.xyz' files

[x] Converting '.xyz' files to data frame and storing as pickled file

[ ] Loading different DFT files

[ ] Split into train-test-validation

[ ] Adjusting column prefix

Featurization

[x] Loading pickled files

[x] Loading '.xyz' files

[x] Automatic core count detection

[x] Specifying r_min/r_max/res_map as dict

[x] Specifying r_min/r_max/res_map as int/float and apply to all interactions

[x] Exporting features to file

[ ] Exporting features to data frame

[ ] Knot strategies

[ ] Loading and dumping knots

[ ] Only training on energies

Fitting

[x] Same parameters as with Python featurization

[x] Exporting model to json file

[x] Predict with fitted model

[x] Test errors (RMSE and MAE)

[x] Train on subset of featurized data

[x] Predict on subset of featurized data

Misc

[x] Automatic installation for easy use

[ ] Default settings file

[ ] Settings file from bspline_config

YAML parameters not yet used:

seed

data

db_path

max_per_file

min_diff

generate_stats

vasp_pressure

sources

pattern

basis

fit_offsets

mask_trim

knot_strategy

knots_path

load_knots

dump_knots

features

db_path

fit_forces

column_prefix

learning

splits_path

YAML parameters added:

verbose

data

pickle_path

train_pickle

test_pickle

features

batch_size

table_template

learning

batch_size

Usage

The CLI enables quick generation of models. The tungsten example can be executed in three lines:

> uf3_cli config 3 W > uf3_cli collect options.yaml > uf3_cli featurize options.yaml > uf3_cli fit options.yaml

with the required file names and training settings specified in options.yaml

The code is robust towards errors and performs type conversion where possible.
opened by henk789 0

UFCalculator error when model has resolution 0 for an interaction

Problem description

When loading a model that has knot resolution 0 for an interaction, using UFCalculator results in an error message:

Traceback (most recent call last):
  File "/home/***/***/***/.conda/envs/uf3_env/lib/python3.8/concurrent/futures/process.py", line 239, in _process_worker
    r = call_item.fn(*call_item.args, **call_item.kwargs)
  File "/scratch/slurm_tmpdir/job_21050829/ipykernel_1790295/233011300.py", line 22, in eval
    p_f = np.append(p_f, geom.get_forces())
  File "/home/***/***/***/.conda/envs/uf3_env/lib/python3.8/site-packages/ase/atoms.py", line 788, in get_forces
    forces = self._calc.get_forces(self)
  File "/pfs/data5/home/***/***/***/ba/uf3/uf3/forcefield/calculator.py", line 215, in get_forces
    f_3b = self.forces_3b(atoms, supercell)
  File "/pfs/data5/home/***/***/***/ba/uf3/uf3/forcefield/calculator.py", line 288, in forces_3b
    val_l = spline(triangles, nus=np.array([1, 0, 0]))
  File "/home/***/***/***/.conda/envs/uf3_env/lib/python3.8/site-packages/ndsplines/ndsplines.py", line 220, in __call__
    self.compute_basis_coefficient_selector(x, nus)
  File "/home/***/***/***/.conda/envs/uf3_env/lib/python3.8/site-packages/ndsplines/ndsplines.py", line 172, in compute_basis_coefficient_selector
    impl.evaluate_spline(t, k, x[:,i], nu, extrapolate_flag, self.interval_workspace[i], self.basis_workspace[i],)
  File "ndsplines/_bspl.pyx", line 96, in ndsplines._bspl.evaluate_spline
ValueError: Buffer dtype mismatch, expected 'const double' but got 'long'

Solution

The issue arises due to the JSON parsing used when loading models. Since the seven knot positions are all 0, they are loaded as integers. However, the ndspline package requires double. Therefore, the issue can be avoided when converting integer knot positions to doubles.

opened by henk789 0

Feature request: option for filtering configurations with large energies/forces
Currently, parsed data from VASP, LAMMPS, etc is organized into a pandas dataframe by io.DataCoordinator with separate columns for energies (E), force-components (Fx, Fy, and Fz), and atoms per unit cell ("Size")

We can have three additional user-parameters and a function to remove rows from the dataframe that exceed these constraints:

Maximum energy (eV/atom)

Maximum force (eV/angstrom)

Maximum number of atoms per unit cell

Note: force magnitudes should probably be considered per-atom rather than component-wise.
enhancement
opened by sxie22 0
Command line interface and scripts
Standalone scripts from the alpha version are currently incompatible with the latest refactor. They need to be rewritten, preferably with additional support for argparse.

Documentation for options and the input yaml are already complete.

enhancement help wanted
opened by sxie22 0
Two-and-three body support for UFCalculator and UFLammps
[WIP] Three-body contributions for UFCalculator for multicomponent systems are experiencing some minor bugs

Three-body contributions for UFLammps will require a tensor-product spline evaluator in C++ for LAMMPS

enhancement
opened by sxie22 2

Releases(v0.3.2)

v0.3.2(Sep 29, 2022)
This release includes necessary changes to publish uf3 to PyPi.

Changes

Installing uf3 with pip now no longer requires the editable option -e.

Full Changelog: https://github.com/uf3/uf3/compare/v0.3.0...v0.3.2
Source code(tar.gz)
Source code(zip)
v0.3.0(Sep 20, 2022)
This release incorporates all changes from the development branch between July 24, 2022, and September 20, 2022.

The update includes critical bug fixes for models with multi-component systems and is not compatible with previously generated feature data and fitted models.

Bug Fixes

Fix multi-component pair and triplet building (PR by @henk789 in https://github.com/uf3/uf3/pull/31)

Two- and three-body interactions across periodic boundary conditions were occasionally assigned incorrect element species. All multi-component models, therefore, require new featurization and new fitting.

Fix issue with loading wrong model coefficients (PR by @henk789 in https://github.com/uf3/uf3/pull/32)

Loading models from JSON files resulted in wrong coefficients. Stored models are unaffected but are now loading correctly. The bug affected the master branch since v0.2.0.

Sort pair tuples correctly during loading (PR by @henk789 in https://github.com/uf3/uf3/pull/25)

Sort pair hashes by electronegativity (PR by @henk789 in https://github.com/uf3/uf3/pull/27)

Full Changelog: https://github.com/uf3/uf3/compare/v0.2.0...v0.3.0
Source code(tar.gz)
Source code(zip)
v0.2.0(Jul 24, 2022)
This release incorporates all changes from the development branch between Dec 9, 2021 and July 23, 2022. The update includes changes to function names and arguments, so please check the updated examples and changelog.

Additions:

Per-sample weighting during fitting

Enforce repulsion at small distances for pair potentials where basis functions are entirely outside of training data domain

Improved saving/loading classes to/from JSON files (PR #18, PR #20, PR #21 from @henk789)

Three-body visualization with slices

Three-body visualization with Plotly-based rendering

Support for forcing leading (small-distance) basis functions to zero in addition to previously-implemented trailing_trim.

Support for BSplines with negative-valued knots. Note: LAMMPS' pair_style table requires positive-valued knots.

Additional keywords for finer control of plotting functions.

Changes:

Remove unused keywords, such as chemical_system for BasisFeaturizer (PR #9 from @Robert-Schmid)

Serialize uncompressed coefficient array for three-body terms

New default values for regularizers

Fixes:

Increased decimal precision when writing to file

Fit with a list of keys rather than requiring both integer slices and keys

Handle divide-by-zero errors when training with a single atomic configuration

Speed up python-based ASE calculator for single- and multi-component systems using NDSplines package

New default color and line-style settings for plotting pair potentials

Source code(tar.gz)
Source code(zip)

Owner

Ultra-Fast Force Fields

Fast, interpretable machine-learning potentials.

GitHub Repository

This is the core of the program which takes 5k SYMBOLS and looks back N years to pull in the daily OHLC data of those symbols and saves them to disc.

1 Jan 31, 2022

NFT generator for Solana!

Solseum NFT Generator for Solana! Check this guide here! Creating your randomized uniques NFTs, getting rarity information and displaying it on a webp

145 Dec 30, 2022

UF3: a python library for generating ultra-fast interatomic potentials

Related tags

Overview

Ultra-Fast Force Fields (UF3)

Setup

Getting Started

Optional Dependencies

Dependencies

Comments

Problem description

Problem description

Solution

Problem description

Example

Impact on prediction accuracy

Solution

Issue description

Added Features

Generating initial config with specified atoms

Collecting DFT data

Featurization

Fitting

Misc

YAML parameters not yet used:

YAML parameters added:

Usage

Problem description

Solution

Releases(v0.3.2)

v0.3.2(Sep 29, 2022)

Changes

v0.3.0(Sep 20, 2022)

Bug Fixes

v0.2.0(Jul 24, 2022)

Owner

Ultra-Fast Force Fields

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This is the core of the program which takes 5k SYMBOLS and looks back N years to pull in the daily OHLC data of those symbols and saves them to disc.

NFT generator for Solana!

`YAML` parameters not yet used:

`YAML` parameters added: