Transition Metal

Transition metals are crucial in materials science due to their variable oxidation states and diverse bonding capabilities, leading to a wide range of properties. Current research heavily utilizes machine learning, particularly deep learning frameworks and symbolic regression, to accelerate the prediction and discovery of transition metal compounds and their properties, including X-ray absorption spectra, crystal structures, and excited-state behavior. This focus on computational methods aims to overcome the limitations of traditional experimental and theoretical approaches, enabling high-throughput screening and the design of novel materials with tailored functionalities for applications in diverse fields like catalysis, lighting, and bioimaging. The development of transferable models and robust predictive algorithms is key to advancing this field.

Papers

October 22, 2024

Multi-modal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions: Performance and Interpretability towards Experimental Design
Tanaporn Na Narong, Zoe N. Zachko, Steven B. Torrisi, Simon J. L. Billinge
Inherent Interpretability X Ray Spectroscopic Data Synthetic Spectrum Transition Metal Multi Modal Machine Learning

October 21, 2024

Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge of Large Language Models
Jieyu Lu, Zhangde Song, Qiyuan Zhao, Yuanqi Du, Yirui Cao, Haojun Jia, Chenru Duan
Knowledge Based Efficient Optimization Generative Design Molecular Generation Evolutionary Optimization Novel Ligand Transition Metal

October 12, 2024

Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes
Ralf Meyer, Daniel Benjamin Kasman Chu, Heather J. Kulik
Machine Learning Model Molecular Graph Spherical Surface Electronic Structure Novel Ligand Candidate Molecule Transition Metal

September 29, 2024

A Universal Deep Learning Framework for Materials X-ray Absorption Spectra
Shubha R. Kharel, Fanchen Meng, Xiaohui Qu, Matthew R. Carbone, Deyu Lu
X Ray Element Level Information Universal Deep Explicit Modelling Machining Feature Transition Metal

April 28, 2023

Machine learning-assisted close-set X-ray diffraction phase identification of transition metals
Maksim Zhdanov, Andrey Zhdanov
New Machine Crystal Structure Transition Metal

October 27, 2022

Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regression
Alberto Hernandez, Tim Mueller
Symbolic Regression Stronger Generalizability Interatomic Potential Functional Theory Machine Learning Potential Transition Metal

September 18, 2022

Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties
Gianmarco Terrones, Chenru Duan, Aditya Nandy, Heather J. Kulik
Human Prediction Constructive Approach Internal State Rich Attribute Low Cost Excited State Transition Metal

August 10, 2022

Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores
Chenru Duan, Aditya Nandy, Gianmarco Terrones, David W. Kastner, Heather J. Kulik
Transition Metal

July 21, 2022

A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery
Chenru Duan, Aditya Nandy, Ralf Meyer, Naveen Arunachalam, Heather J. Kulik
Density Functional Theory Chemical Discovery Wave Function Transition Metal

May 6, 2022

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik
Level Test Material Discovery Electronic Structure Material Design Density Functional Theory Transition Metal Accelerated Discovery

May 5, 2022

Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands
Chenru Duan, Adriana J. Ladera, Julian C. -L. Liu, Michael G. Taylor, Isuru R. Ariyarathna, Heather J. Kulik
Novel Ligand Transition Metal Transferable Time Series Molecular Machine

April 29, 2022

Family of Two Dimensional Transition Metal Dichlorides Fundamental Properties, Structural Defects, and Environmental Stability
Andrey A. Kistanov, Stepan A. Shcherbinin, Romain Botella, Artur Davletshin, Wei Cao
2 Dimensional Material Response Single Parent Family Structural Damage 2 Dimensional Material Transition Metal Fundamental Property

Transition Metal

Papers

Multi-modal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions: Performance and Interpretability towards Experimental Design

Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge of Large Language Models

Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes

A Universal Deep Learning Framework for Materials X-ray Absorption Spectra

Machine learning-assisted close-set X-ray diffraction phase identification of transition metals

Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regression

Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties

Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores

A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands

Family of Two Dimensional Transition Metal Dichlorides Fundamental Properties, Structural Defects, and Environmental Stability