Themed collection Most popular 2019-2020 physical and theoretical chemistry articles

The search is on for effective specific inhibitors for PI3Kα mutants.

This review focuses on the available experimental and theoretical investigations on noble gas (Ng) endohedral fullerenes, addressing the effects of confinement of one or more Ng atoms into the electronic structure and reactivity of fullerenes.

Integrating automation with artificial intelligence will enable scientists to spend more time identifying important problems and communicating critical insights, accelerating discovery and development of materials for emerging and future technologies.

A robust system for automatic processing and assignment of raw ¹³C and ¹H NMR data DP4-AI has been developed and integrated into our computational organic molecule structure elucidation workflow.

A nonfullerene acceptor, TACIC, showed efficient light-harvesting, exciton diffusion, and charge transfer.

We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention.

Indoor light harvesters enable machine learning on fully autonomous IoT devices at 2.72 × 10¹⁵ photons per inference.

Target identification and drug repurposing could benefit from network-based, rational deep learning prediction, and explore the relationship between drugs and targets in the heterogeneous drug–gene–disease network.

Relative ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design.

The IMPRESSION machine learning system can predict NMR parameters for 3D structures with similar results to DFT but in seconds rather than hours.

Computer Assisted Synthesis Planning (CASP), datasets and their performance.

Machine learning model of the electron densities for analyzing non-covalent interaction patterns in peptides.

Machine learning enables excited-state molecular dynamics simulations including nonadiabatic couplings on nanosecond time scales.

Two-body and three-body energies, modulated by higher-body terms and nuclear quantum effects, determine the structure of liquid water and require sub-chemical accuracy that is achieved by the MB-pol model but not by existing DFT functionals.

A predictive approach for driving down machine learning model errors is introduced and demonstrated across discovery for inorganic and organic chemistry.

The unexpected covalent contribution in the DOTADy-OH₂ bond revealed by ab initio calculations of the easy axis of magnetization through simple H₂O rotations.

Enantiospecific crystallization of the three amino acids asparagine (Asn), glutamic acid hydrochloride (Glu·HCl) and threonine (Thr), induced by ferromagnetic (FM) substrates, is reported.

Disubstituted odd-carbon cumulenes are linear carbon wires with helical π-orbitals, which results in circular current around the wire.

A family of network algorithms allows the Chematica retrosynthetic platform to plan both cost-effective and chemically diverse syntheses.

Efficient photolabile protecting groups: how to achieve exceptional photo-triggered amino-acid delivery upon irradiation in the NIR.

The development of robust and efficient water splitting photocatalysts overcomes a long-standing barrier to sustainable large-scale solar hydrogen evolution systems.

This contribution explains the origin of dramatic rate accelerations in chemical reactions taking place in/on aqueous electrosprays. We combine experiments with electrosprays and proton-nuclear magnetic resonance with quantum mechanics to systematically decouple genuine interfacial effects from non-equilibrium conditions.

Machine learning models, trained to reproduce molecular dynamics results, help interpreting simulations and extracting new understanding of chemistry.

The density matrix renormalization group (DMRG) is a powerful method to treat static correlation.

Translation between semantically equivalent but syntactically different line notations of molecular structures compresses meaningful information into a continuous molecular descriptor.

The geometry and bonding of N-heterocyclic carbenes to metal surfaces depends on the substituents on the N-atoms.

The nature of orbital interactions in a carbonyl–carbonyl short contact is determined by the relative orientation of the two interacting carbonyl groups.

We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).

A new approach to increasing the faradaic efficiency of dye-sensitised photocathodes for H₂ evolution from water is described, using integrated photocatalysts based on a ruthenium 4,4′-diethoxycarboxy-2,2′-bipyridine chromophore linked via terpyridine or triazole to a Pd or Pt-based H⁺ reduction catalyst.

By blending a chiral acceptor and a sensitizer into a nematic liquid crystal, a chiral nematic liquid crystal showing amplified upconverted circularly polarized luminescence could be obtained.