AI-enhanced computational chemistry
MLatom@XACS makes AI-enhanced computational chemistry more accessible and supports both ground- and excited-state simulations with quantum mechanical methods, machine learning, and their combinations. We are happy to announce that we will release the new upgraded version of MLatom 3.3.0 that …
Surface hopping dynamics with MLatom is coming: Join online broadcast! Read more »
MLatom@XACS is a powerful tool for training and using machine learning potentials. It supports a wide variety of representative potentials. These potentials include: ·Equivariant neural network MACE ·Popular ANI with a good cost/performance ratio ·Kernel methods such as KREG and …
Training and using machine learning potentials with MLatom@XACS Read more »
We are happy to announce that MLatom 3.2.0 is released on 19.03.2024. See the full release notes for details. How to get it? Download zip, check this version on PyPI and GitHub. pip install mlatom==3.2.0 What’s new? This is a major release with many new features, …
My review ‘AI in computational chemistry through the lens of a decade-long journey’ was published open access as an invited Feature Article in Chemical Communication. It gives a perspective on the progress of AI tools in computational chemistry through the …
AI-accelerated nonadiabatic dynamics reduces the cost of the ab initio simulations of nonlinear time-resolved spectra. We have developed a robust protocol and demonstrated its feasibility for calculating stimulated emission contributions in transient absorption pump–probe and 2D electronic spectra of pyrazine. …
Check out our tutorial on how to run TS geometry optimizations and also analysis (freq and IRC) with MLatom@XACS.We demonstrate on an example of the Diels–Alder reaction how to use fast GFN2-xTB and AIQM1 methods providing reasonable TS geometries and …
Transition state search and analysis with MLatom@XACS Read more »
Single-point calculations can be performed with various models and methods in MLatom including: For more details on the models in MLatom, please check an overview. Here we will illustrate how to calculate isomerization energy of sugar in ISOL24 with different types of methods …
Tutorial on single-point calculations with MLatom@XACS Read more »
Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Peikun Zheng, Yuxinxin Chen, Mario Barbatti, Olexandr Isayev, Cheng Wang, Bao-Xin Xue, Max Pinheiro Jr, Yuming Su, Yiheng Dai, Yangtao Chen, Lina Zhang, Shuang Zhang, Arif Ullah, Quanhao Zhang, Yanchi Ou. MLatom 3: A …
MLatom 3 for AI-enhanced computational chemistry: JCTC paper and online tutorial Read more »
In this tutorial we show how to optimize molecular geometries with MLatom. Here, only the optimization of the minima is shown and the optimization of the transition states will be shown elsewhere (for now, please check out the manual). For the …
Tutorial on geometry optimizations with MLatom@XACS Read more »
Location: Xiamen University, China Duration: 2 years (with possible 1-year extension) Xiamen University is offering a fully-funded 2-year postdoctoral research position in the field of machine learning-based simulations of condensed matter. This opportunity is available within the research group of Prof. Pavlo …