Virtual Node Graph Neural Network for Full Phonon Prediction

12/11/2024 | Mingda Li (MIT)

Understanding the structure–property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection.

Machine Learning Detection of Majorana Zero Modes From Zero-bias Peak Measurements

12/11/2024 | Mingda Li (MIT)

A machine learning method has been developed to detect Majorana zero modes (MZMs) from experimental data, achieving significant accuracy. This approach utilizes quantum transport simulations and topological data analysis, providing a simpler and more effective method to identify these quantum states, crucial for the advancement of fault-tolerant quantum computing.

Rational design of redox-responsive materials for critical element separations

11/21/2024 | Xiao Su (University of Illinois Urbana-Champaign)

To develop a new redox material with higher PGM uptake and selectivity, we must understand the effect of metallopolymers for PGMs selectivity more deeply. We plan to combine the spectroscopy and chemical calculation to understand the binding mechanism and the structure effect of redox polymers for PGMs separations. Moreover, to make the recovery system more economical, our goal is to synthesize new redox metallopolymers and immobilized ligands based on the results of computational simulations in an iterative fashion.

Chemical Engineering Summer Camp: Separation Science & Water Filtration

11/21/2024 | Xiao Su (University of Illinois Urbana-Champaign)

For undergrad and graduate chemical engineering education, Su and Calabrese taught Mass Transfer and Separations courses at UIUC (ChBE 422) and UMN (ChEn3006), providing a unique opportunity to train future engineers on emerging separations technologies.

Selective Recovery of Platinum Group Metals

11/21/2024 | Xiao Su (University of Illinois Urbana-Champaign)

In this work, we applied four different metallopolymers with tunable redox-potentials and demonstrated their molecular selectivity in multicomponent PGM mixtures. Results showed that lower redox potential of the metallopolymer had higher uptake at OCP.

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.