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

It shortens molecular simulation time to a couple of days which could have taken weeks of pure quantum chemical calculations.

One-year overview: from MLatom 3.0 to 3.10

Here we make a summary of the upgrades in case you missed them!

How to construct and use delta-learning models

Delta-learning is slower than pure ML models but the benefit is typically an increased robustness and accuracy.

Lego-bricks and infrastructure for your own computational chemistry model

MLatom provides you not just with many lego-bricks (both ML models and quantum chemical methods) but also with the powerful and intuitive tools to glue them together in arbitrarily complex workflows!

Estimate two-photon absorption strength online!

We have developed an interpretable machine learning approach based on experimental data which predicts two-photon absorption strength instantaneously with accuracy comparable to DFT.

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