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.

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.

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.

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.

Research Highlights