J. Chem. Inf. Model. 2023, 63, 2, 583–594
In this work, we have developed an efficient automated workflow for identifying compounds with the lowest BFE among thousands of
Chem. Sci., 2023,14, 13392-13401
Here, a Δ2-learning model is introduced that can predict high-level activation energies based on low-level critical-point geometries.
J. Phys. Chem. A 2023, 127, 11, 2417–2431
Perspective focused on key methodologies and ML models being used where the presence of nonlocal physics and chemistry are crucial
J. Chem. Inf. Model. 2022, 62, 22, 5373–5382
We developed the Python-based Auto3D package for generating the low-energy 3D structures using SMILES as the input.
Acc. Chem. Res. 2021, 54, 7, 1575–1585
In this Account, we focus on the out-of-the-box approaches to developing transferable MLIPs for diverse chemical tasks.
J. Chem. Inf. Model. 2020, 60, 7, 3408–3415
This paper presents TorchANI, a PyTorch-based program for training/inference of ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces