01:05

MLST: Review on data sets for molecular ML potentials

01:05

ML-enhanced Fast and Interpretable Simulation of IR Spectra

02:34

Adv. Sci.: The Best DFT Functional Is the Ensemble of Functionals

01:22

All-in-one: Learning across quantum chemical levels. Better than transfer learning!

01:36

JOC: Surprising dynamics phenomena in the Diels–Alder reaction of C60 uncovered with AI

03:02

AIQM2 is out: better and faster than B3LYP for reaction simulations!

04:02

Phyiscally consistent simulation of quantum dissipative dynamics with neural networks

02:00

JCTC: Physics-informed active learning for accelerating quantum chemical simulations

04:09

One-year overview: from MLatom 3.0 to 3.10

01:03

How to construct and use delta-learning models

00:52

Lego-bricks and infrastructure for your own computational chemistry model

00:57

Estimate two-photon absorption strength online!

39:05

Active learning for surface hopping dynamics

01:42

Data in MLatom’s Python API

00:56

Not sure which DFT functional to choose? Choose them all!

00:54

Finally! Periodic boundary conditions in MLatom

00:40

Molecular Raman spectra simulations online!

02:09

Directly learning molecular dynamics!

03:31

Simulating ambimodal reactions online!

01:20

Active learning for building data-efficient machine learning potentials

02:30

Supercharge your computational chemistry with the universal and updatable AI models

00:24

Molecular IR spectra simulations online!

01:00

JCTC: Surface hopping dynamics with QM and ML methods

00:29

DFT calculations online on XACS cloud!

00:31

Transfer learning for better AI models with less data

01:18

Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by AIQM1

00:40

Easy-to-use universal AI models for modern computational chemistry

00:54

Quasi-classical trajectories to study reaction mechanisms like in PNAS and JACS papers!

00:55

Optimize molecular geometries easier with MLatom 3.4.0

00:54

JPCL | Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training

00:56

Faster & more accurate than DFT: AIQM1 in MLatom@XACS

01:16

Training and using machine learning potentials with MLatom@XACS

04:55

MLatom 3.2.0 is released!

02:23

Chem. Commun. : “AI in computational chemistry through the lens of a decade-long journey”

03:42

Tutorial on calculating vibrational spectra from molecular dynamics trajectories with MLatom@XACS

02:18

XACS Jupyter Lab is released

08:03

XACS Research Highlight: AI-enhanced nonlinear time-resolved spectra assisted by MLatom@XACS

07:23

Transition state search and analysis with MLatom@XACS

17:12

Tutorial on single-point calculations with MLatom@XACS

21:38

MLatom@XACS for AI-enhanced computational chemistry: JCTC paper and online tutorial

02:01

Tutorial on molecular dynamics with MLatom@XACS

02:53

A brief overview of what is XACS

02:33

Tutorial on frequency and thermochemistry calculations with MLatom@XACS

02:05

Tutorial on geometry optimizations with MLatom@XACS

02:59

Bringing the power of equivariant NN potential through the interface of MACE to MLatom@XACS

02:07

Installation Tutorial of MLatom

00:51

December 2023 Update of XACS Cloud Computing