Learning Molecular Mixture Property using Chemistry-Aware Graph Neural Network

11/13/2024 | James Rondinelli and Wei Cheng (Northwestern University)

Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility.

Unveiling the Role of Termination Groups in Stabilizing Mxenes in Contact with Water

11/1/2024 | Konstantin Klyukin (Auburn University)

Here a computational analysis is provided of the degradation processes at the interface between MXene basal planes and water using enhanced sampling ab initio molecular dynamics simulations and symbolic regression analysis. These results indicate that the reactivity of Ti sites toward the water attack reaction depends on both local coordination and chemical composition of the MXene surfaces.

Increasing Computational Protein Design Literacy through Cohort-based Learning for Undergraduate Students

10/25/2024 | David Baker and Neil King (U. Washington)

This program provides a model of structured computational research training opportunities for undergraduate researchers in any field for organizations looking to expand educational access.

Computational Design of Mechanically Coupled Axle-rotor Protein Assemblies

10/25/2024 | Neil King and David Baker (U. Washington)

Here the de novo construction of protein machinery are explored from designed axle and rotor components with internal cyclic or dihedral symmetry. It was found that the axle-rotor systems assemble in vitro and in vivo as designed.

Blueprinting Extendable Nanomaterials with Standardized Protein Blocks

10/25/2024 | David Baker (U. Washington)

Because of the complexity of protein structures and sequence–structure relationships, it has not previously been possible to build up large protein assemblies by deliberate placement of protein backbones onto a blank three-dimensional canvas; the simplicity and geometric regularity of this design platform now enables construction of protein nanomaterials according to ‘back of an envelope’ architectural blueprints.

Integrating Theory, Computation, and Experiments to Robustly Design Complex Protein-based Nanomaterials

10/25/2024 | David Baker (University of Washington) and Todd Yeates (UCLA)

The ability to robustly engineer such structures would be a powerful tool for nanotechnology. Exciting progress has been made toward this goal, including the development and demonstration of new methods for the computational modeling and design of self- and co-assembling proteins in a broad range of symmetric architectures.

Machine Learning Accelerated First-principles Study of the Hydrodeoxygenation of Propanoic Acid

10/7/2024 | G. A. Terejanu (University of North Carolina) and A. Heyden (University of South Carolina)

The study of the usage of biomass as an alternative to fossil fuels has greatly increased due to environmental and climate issues brought about by the combustion of fossil fuels and their derivatives. However, biomass-derived fuels have their drawbacks, such as high viscosity, poor oxidation stability, low energy density, and high cloud point temperature, due to their high oxygen content.

A Neural Network Approach for Catalysis

10/7/2024 | G. A. Terejanu (University of North Carolina) and A. Heyden (University of South Carolina)

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates, even for one active site model, can become prohibitive.

Red Emission in Colloidal Nanocrystals

10/7/2024 | Lee Bassett (U. Pennsylvania) and Michael Flatte (U. Iowa)

Controlled impurity doping of wide-bandgap semiconductors can be used to introduce color centers, which are point defects that activate sub-bandgap, optical photoluminescence (PL).

Room Temperature Dynamics of an Optically Addressable Single Spin in Hexagonal Boron Nitride

10/7/2024 | Lee Bassett (U. Pennsylvania) and Michael Flatte (U. Iowa)

Optically interfaced solid-state spins enable quantum technologies with unprecedented capabilities for sensing, communication, quantum-coherent memories, and exploration of fundamental physics. Hexagonal boron nitride (h-BN) hosts pure single-photon emitters that have shown evidence of optically detected electronic spin dynamics.