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Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify…

Materials Science · Physics 2022-11-18 Mohammad Tohidi Vahdat , Kumar Agrawal Varoon , Giovanni Pizzi

Accurate molecular property prediction is central to drug discovery, catalysis, and process design, yet real-world applications are often limited by small datasets. Molecular foundation models provide a promising direction by learning…

Machine Learning · Computer Science 2026-04-21 Karim K. Ben Hicham , Jan G. Rittig , Martin Grohe , Alexander Mitsos

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and…

Machine Learning · Computer Science 2023-03-17 Thulasi Bikku , Rubén A. Fritz , Yamil J. Colón , Felipe Herrera

The emergent behavior of quantum materials is governed by their electronic structure, which can be experimentally probed by photoemission spectroscopy techniques that generate a four-dimensional dataset of energy and momentum. However, the…

Strongly Correlated Electrons · Physics 2026-03-18 Yu Zhang , Yong Zhong , Nhat Huy Tran , Shuyi Li , Kyuho Lee , Yonghun Lee , Tiffany C. Wang , Harold Y. Hwang , Zhi-Xun Shen , Chunjing Jia

Owing to its high scalability and computational efficiency, machine learning methods have been increasingly integrated into various scientific research domains, including ab initio-based materials design. It has been demonstrated that, by…

Materials Science · Physics 2025-10-16 Feng Chen , Shu Li , Xin Chen , Dennis Wong , Biplab Sanyal , Duo Wang

This paper proposes a machine learning (ML) method to predict stable molecular geometries from their chemical composition. The method is useful for generating molecular conformations which may serve as initial geometries for saving time…

Recently, the remarkable capabilities of large language models (LLMs) have been illustrated across a variety of research domains such as natural language processing, computer vision, and molecular modeling. We extend this paradigm by…

Machine Learning · Computer Science 2023-09-04 Hongshuo Huang , Rishikesh Magar , Changwen Xu , Amir Barati Farimani

Leveraging scanning tunneling microscopy (STM) for atomic-scale fabrication has led to many advancements such as the creation of atomic electron-spin qubit structures on surfaces. However, the time-consuming and tedious nature of this…

Mesoscale and Nanoscale Physics · Physics 2024-10-18 Angéline Lafleur , Soo-hyon Phark

The application of molecular dynamics (MD) simulations to the interpretation of Raman scattering spectra is hindered by inability of atomistic simulations to account for the dynamic evolution of electronic polarizability, requiring the use…

Materials Science · Physics 2023-04-18 Atanu Paul , Anthony Ruffino , Stefan Masiuk , Jonathan Spanier , Ilya Grinberg

Molecules have seemed like a natural fit to deep learning's tendency to handle a complex structure through representation learning, given enough data. However, this often continuous representation is not natural for understanding chemical…

Machine Learning · Computer Science 2021-03-12 Austin Clyde , Arvind Ramanathan , Rick Stevens

We present a complete set of chemo-structural descriptors to significantly extend the applicability of machine-learning (ML) in material screening and mapping energy landscape for multicomponent systems. These new descriptors allow…

Materials Science · Physics 2018-08-08 Kamal Choudhary , Brian DeCost , Francesca Tavazza

Two-dimensional transition metal dichalcogenides (TMDs) can adopt one of several possible structures, with the most common being the trigonal prismatic and octahedral symmetry phases. Since the structure determines the electronic…

Materials Science · Physics 2022-09-20 Pankaj Kumar , Vinit Sharma , Sharmila Shirodkar , Pratibha Dev

Metal-organic frameworks (MOFs) are porous, crystalline materials with high surface area, adjustable porosity, and structural tunability, making them ideal for diverse applications. However, traditional experimental and computational…

Biomolecules · Quantitative Biology 2024-12-17 Dong Chen , Chun-Long Chen , Guo-Wei Wei

Material scientists are increasingly adopting the use of machine learning (ML) for making potentially important decisions, such as, discovery, development, optimization, synthesis and characterization of materials. However, despite ML's…

Computational Physics · Physics 2019-03-12 Bhavya Kailkhura , Brian Gallagher , Sookyung Kim , Anna Hiszpanski , T. Yong-Jin Han

Representation learning often plays a critical role in reinforcement learning by managing the curse of dimensionality. A representative class of algorithms exploits a spectral decomposition of the stochastic transition dynamics to construct…

Machine Learning · Computer Science 2023-03-08 Tongzheng Ren , Tianjun Zhang , Lisa Lee , Joseph E. Gonzalez , Dale Schuurmans , Bo Dai

With the rapid advancement of technology, 3D data acquisition and utilization have become increasingly prevalent across various fields, including computer vision, robotics, and geospatial analysis. 3D data, captured through methods such as…

Computer Vision and Pattern Recognition · Computer Science 2025-12-09 Siming Yan

Crystal graph neural networks are widely applicable in modeling experimentally synthesized compounds and hypothetical materials with unknown synthesizability. In contrast, structure-agnostic predictive algorithms allow exploring previously…

Materials Science · Physics 2025-11-06 Ivan Rubtsov , Ivan Dudakov , Yuri Kuratov , Vadim Korolev

Machine learning approaches have become popular for molecular modeling tasks, including molecular force fields and properties prediction. Traditional supervised learning methods suffer from scarcity of labeled data for particular tasks,…

Chemical Physics · Physics 2022-11-29 Xiang Gao , Weihao Gao , Wenzhi Xiao , Zhirui Wang , Chong Wang , Liang Xiang

Identifying local structural motifs and packing patterns of molecular solids is a challenging task for both simulation and experiment. We demonstrate two novel approaches to characterize local environments in different polymorphs of…

Materials Science · Physics 2024-04-02 Daisuke Kuroshima , Michael Kilgour , Mark E. Tuckerman , Jutta Rogal

The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property space. The use of these powerful…

Materials Science · Physics 2018-11-12 Tian Xie , Jeffrey C. Grossman
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