Reaction exploration

Generate transition states and reaction path

One of the key steps in reaction exploration is to enumerate reaction steps. MLatom supports generating transition states as well as reaction paths given reactants and products with the state-of-the-art diffusion model ECTS:

Xu M, Li B, Dong Z, Dral P, Zhu T, Chen H. ECTS: An ultra-fast diffusion model for exploring chemical reactions with equivariant consistency. ChemRxiv. 2025; doi:10.26434/chemrxiv-2025-f9vdp.

Prerequisites

MLatom 3.17.4 or later
EcTs 1.0 (Installation instructions can be found in https://github.com/AI4Reactions/ECTS. The required setting files in .json and models can also be there)

An example to generate TS

reactant = ml.data.molecule.from_xyz_file('./r.xyz')
product = ml.data.molecule.from_xyz_file('./p.xyz')

results = ml.simulations.gen_ts(
    reactant=reactant, product=product, program='ects',
    program_kwargs_json='./ts_kwargs.json',
    n_ts=8, avg_ts=True,
    working_directory='ects_ts'
)

generated_ts = results.ts
generated_avg_ts = results.avg_ts

The returned generated_ts contain a list of sampled transition states with the same length as n_ts. If avg_ts is set to True, you can obtain the averaged transition state generated_avg_ts based on the all the samples.

An example to generate reaction path

reactant = ml.data.molecule.from_xyz_file('./r.xyz')
product = ml.data.molecule.from_xyz_file('./p.xyz')

results = ml.simulations.gen_ts(
    reactant=reactant, product=product, program='ects',
    program_kwargs_json='./path_kwargs.json',
    path=True, n_path=8
    avg_ts=True,
    working_directory='ects_path')

reaction_path = results.path

The reaction_path contains a list of molecular database and each molecular database consists of one reaction path.