Theoretical chemistry articles from across Nature Portfolio

While clusters in calcium orthophosphate nucleation have long been known, their speciation and mechanistic pathways to hydroxyapatite remain debated. Here the authors report a revision of ion association in the calcium phosphate system and explore the consequences thereof on the early stages of phase separation.

The transformation of CO2 with renewable hydrogen into high-value products presents a sustainable route for net-zero chemical manufacture. Here the authors introduce a LaFeO3 perovskite-mediated tandem conversion of CO2, achieving remarkable performance by separating the CO2 hydrogenation and C-C coupling domains in the catalyst system.

Using machine learning methods to model interatomic potentials enables molecular dynamics simulations with ab initio level accuracy at a relatively low computational cost, but requires a large number of labelled training data obtained through expensive ab initio computations. Cui and colleagues propose a geometric learning framework that leverages self-supervised learning pretraining to enhance existing machine learning based interatomic potential models at a negligible additional computational cost.

AI tools such as ChatGPT can provide responses to queries on any topic, but can such large language models accurately ‘write’ molecules as output to our specification? Results now show that models trained on general text can be tweaked with small amounts of chemical data to predict molecular properties, or to design molecules based on a target feature.

A neural network-based method for advancing orbital-free density functional theory (OFDFT) is developed, which reaches DFT accuracy and maintains lower cost complexity.

Correlating structure and activity is a very important research goal in catalysis. This Editorial reflects on this topic, taking inspiration from examples in the current issue.

We highlight the challenges and opportunities in organic redox flow battery research, underscoring the need for collaborative research efforts. The synergy between computation and experimentation holds the potential to expedite progress in this field and can have far-reaching impacts beyond energy storage applications.

The ab initio atomistic thermodynamics approach, coined by Reuter and Scheffler formally in 2001, remains pivotal for understanding and predicting the stable surfaces of thermal catalysts under technical conditions.

Providing a stable and reliable supply of electrons is crucial for the future of quantum computing processors. Here, electron withdrawing groups are added to species which improve the flow of electrons.