Arif Ullah

Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou et al.

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pre-trained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

Jinming Hu, Hassan Nawaz, Yuting Rui et al.

We have developed Aitomia - a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This evolving intelligent assistant platform is equipped with chatbots and AI agents to help experts and guide non-experts in setting up and running atomistic simulations, monitoring their computational status, analyzing simulation results, and summarizing them for the user in both textual and graphical forms. We achieve these goals by exploiting large language models that leverage the versatility of our MLatom ecosystem, supporting AI-enhanced computational chemistry tasks ranging from ground-state to excited-state calculations, including geometry optimizations, thermochemistry, and spectral calculations. The multi-agent implementation enables autonomous executions of the complex computational workflows, such as the computation of the reaction enthalpies. Aitomia is the first intelligent assistant publicly accessible online on a cloud computing platform for atomistic simulations of broad scope (Aitomistic Hub at https://aitomistic.xyz). It may also be deployed locally as described at http://mlatom.com/aitomia. Aitomia is expected to lower the barrier to performing atomistic simulations, thereby democratizing simulations and accelerating research and development in relevant fields.