XtalOpt: Multi-objective Evolutionary Search for Novel Functional Materials

2/4/2025 | Eva Zurek (SUNY-Buffalo)

In the new version of the XtalOpt code, a general platform for multi-objective global optimization is implemented. This functionality is designed to facilitate the search for (meta)stable phases of functional materials through minimization of the enthalpy of a crystalline system coupled with the simultaneous optimization of any desired properties that are specified by the user.

Heating Samples to 2000° C for Scanning Tunneling Microscopy Studies in Ultrahigh Vacuum

2/4/2025 | Michael Trenary (U. Illinois - Chicago)

A simple device for heating single-crystal samples to temperatures ≥2000 °C in ultrahigh vacuum that is compatible with the standard sample plates used in a common commercial scanning tunneling microscope (STM) is described.

Impact of Data Bias on Machine Learning for Crystal Compound Synthesizability Predictions

2/4/2025 | Sara Kadkhodaei (U. Illinois - Chicago) Eva Zurek (SUNY – Buffalo)

Machine learning models are susceptible to being misled by biases in training data that emphasize incidental correlations over the intended learning task. In this study, the impact of data bias is demonstrated on the performance of a machine learning model designed to predict the likelihood of synthesizability of crystal compounds.

A New Ultrathin Conductor for Nanoelectonics

2/3/2025 | Felipe H. da Jorna (Stanford University)

The electrical resistivity of conventional metals such as copper is known to increase in thin films as a result of electron-surface scattering, thus limiting the performance of metals in nanoscale electronics. Here, an unusual reduction of resistivity is found with decreasing film thickness in niobium phosphide (NbP) semimetal deposited at relatively low temperatures of 400°C.

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.

Designing Materials to Revolutionize and Engineer our Future (DMREF)