Article
Chemistry, Multidisciplinary
Sumit Roy, Venkata Sai Sreyas Adury, Anish Rao, Soumendu Roy, Arnab Mukherjee, Pramod P. Pillai
Summary: This study reports the design of a multifunctional bioplasmonic network through the long-range self-assembly ability of ATP in the presence of cations and gold nanoparticles. The coordination of ATP-Ca2+ helps regulate the electrostatic interaction, transforming an uncontrolled precipitation into a kinetically controlled aggregation process. Additionally, ATP and AuNP retain their inherent properties in the multifunctional bioplasmonic network, and the electrostatically directed self-assembly process is applicable to different nucleotide-nanoparticle systems.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2022)
Article
Computer Science, Interdisciplinary Applications
Yifan Peng, Lin Lin, Lexing Ying, Leonardo Zepeda-Nunez
Summary: The efficient treatment of long-range interactions for point clouds is a challenging problem in scientific machine learning applications. This work introduces a novel neural network layer, called the long-range convolutional (LRC)-layer, that incorporates long-range information for point cloud processing.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Chemistry, Physical
Dylan M. Anstine, Olexandr Isayev
Summary: Advances in machine learned interatomic potentials (MLIPs) have allowed for the development of short-range models with near ab initio accuracy and reduced computational cost. However, incorporating long-range physical interactions into MLIP frameworks remains a challenge. Recent research has focused on including nonlocal electrostatic and dispersion interactions to improve model accuracy. This Perspective discusses key methodologies and models for addressing the contributions of nonlocal physics and chemistry in MLIPs.
JOURNAL OF PHYSICAL CHEMISTRY A
(2023)
Article
Chemistry, Physical
Carsten G. Staacke, Hendrik H. Heenen, Christoph Scheurer, Gabor Csanyi, Karsten Reuter, Johannes T. Margraf
Summary: This study explores the use of machine learning potentials in solid-state electrolyte Li7P3S11, finding that ML potentials are effective in describing lithium ion migration but require hybrid models with physical interactions for accurate depiction of defect formation energies.
ACS APPLIED ENERGY MATERIALS
(2021)
Article
Computer Science, Artificial Intelligence
Qing Liu, Yongsheng Dong, Yuanhua Pei, Lintao Zheng, Lei Zhang
Summary: In this paper, a Long and Short-Range Relevance Context Network is proposed to capture global semantic context and local spatial context information. The network utilizes Long-Range Relevance Context Module and Short-Range Relevance Context Module to improve the accuracy of pixel classification and detailed pixel location. A coding and decoding structure is adopted to enhance the segmentation results, and experiments on multiple datasets validate the effectiveness of the network.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Physics, Fluids & Plasmas
M. N. Najafi, S. Tizdast, J. Cheraghalizadeh, H. N. Dashti
Summary: This paper investigates invasion percolation (IP) with impermeable regions and pore structures modeled by different types of site percolation. The critical exponents change considerably only near the critical points for Ising-correlated cases, while remaining robust for ordinary percolation. Long-range interactions show completely different properties from normal IP, with distinct fractal dimensions and time dependencies in the thermodynamic limit.
Article
Physics, Multidisciplinary
Xiu-Lian Xu, Jin-Xuan Shi, Jun Wang, Wenfei Li
Summary: The study revealed that the edge weights of natural protein networks exhibit power law distributions, and the correlation length of fluctuations is proportional to the topological sizes of the proteins, demonstrating scale-free feature of the correlated fluctuations.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Chemistry, Physical
Pei Ge, Linfeng Zhang, Huan Lei
Summary: A key characteristic of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which presents different features at the molecular and continuum scales. Constructing reliable coarse-grained (CG) models poses a challenge due to the multi-scale nature, requiring the CG potential function to accurately capture many-body interactions from unresolved atomistic interactions and account for heterogeneous density distributions at the interface. We construct CG models for single- and two-component polymeric fluid systems using a deep coarse-grained potential scheme, which accurately reproduce the probability density function of void formation in bulk and the spectrum of capillary waves across the fluid interface by solely utilizing training samples of the instantaneous force under thermal equilibrium. Moreover, the CG models accurately predict the volume-to-area scaling transition of apolar solvation energy, demonstrating their effectiveness in probing meso-scale collective behaviors with molecular-level fidelity.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Chemistry, Medicinal
Shiru Wu, Xiaowei Yang, Xun Zhao, Zhipu Li, Min Lu, Xiaoji Xie, Jiaxu Yan
Summary: Force fields are crucial in molecular simulations and have applications in various fields. Machine learning force fields, constructed using machine learning techniques, offer advantages such as high accuracy and low cost compared to traditional force fields.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Physical
Jon Lopez-Zorrilla, Xabier M. Aretxabaleta, In Won Yeu, Inigo Etxebarria, Hegoi Manzano, Nongnuch Artrith
Summary: In this work, a PyTorch-based implementation for training interatomic potentials is presented. This implementation allows for direct training on forces and significantly reduces training time compared to the CPU implementation. The results demonstrate that including between 10% and 20% of the force information is sufficient for achieving accurately optimized interatomic potentials.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Multidisciplinary Sciences
Kathleen K. A. Cho, Jingcheng Shi, Aarron J. Phensy, Marc L. Turner, Vikaas S. Sohal
Summary: Changes in activity patterns in the medial prefrontal cortex allow animals and humans to adapt their behavior to changes in the environment, such as during cognitive tasks. Parvalbumin-expressing inhibitory neurons play a role in learning new strategies during rule-shift tasks, but the circuit interactions involved in switching prefrontal network dynamics remain unknown.
Article
Physics, Applied
M. Nesic, M. N. Popovic, S. P. Galovic, K. Lj Djordjevic, M. Jordovic-Pavlovic, V. V. Miletic, D. D. Markushev
Summary: In this paper, a self-consistent inverse procedure is developed to accurately estimate the linear thermal expansion coefficient and thermal diffusivity of solids from transmission photoacoustic measurements.
JOURNAL OF APPLIED PHYSICS
(2022)
Article
Biology
Pius Kern, Micha Heilbron, Floris P. de Lange, Eelke Spaak
Summary: Expectations have a significant impact on our music experience. This study investigates the internal model that shapes melodic predictions during naturalistic music listening. They used various computational models of music, including a state-of-the-art transformer neural network, to quantify melodic surprise and uncertainty. The results suggest that neural surprise primarily reflects short-range musical contexts.
Article
Neurosciences
Alex T. L. Leong, Xunda Wang, Eddie C. Wong, Celia M. Dong, Ed X. Wu
Summary: The study uncovered the propagation pathways of neural signals in the brain using optogenetics and functional MRI, showing that such propagation can modulate sensory functions. The research indicated that the temporal characteristics of neural activity play a crucial role in determining the pathways of neural signal propagation.
Article
Nanoscience & Nanotechnology
Eirini Myrovali, Kyrillos Papadopoulos, Irene Iglesias, Marina Spasova, Michael Farle, Ulf Wiedwald, Makis Angelakeris
Summary: The study shows that smaller particles form clusters first, then guide chain formation through cluster-cluster interactions, while larger particles readily form chains through particle-particle interactions. In both cases, dipolar interactions between neighboring nanoparticles increase significantly, leading to a substantial enhancement in their collective magnetic features, resulting in an increase in magnetic particle hyperthermia efficiency by up to one order of magnitude.
ACS APPLIED MATERIALS & INTERFACES
(2021)
Article
Chemistry, Physical
Ang Gao, Liang Tan, Mangesh I. Chaudhari, D. Asthagiri, Lawrence R. Pratt, Susan B. Rempe, John D. Weeks
JOURNAL OF PHYSICAL CHEMISTRY B
(2018)
Article
Physics, Mathematical
Ang Gao, Jianbo Xie, Yueheng Lan
JOURNAL OF STATISTICAL PHYSICS
(2012)
Article
Multidisciplinary Sciences
Ang Gao, Krishna Shrinivas, Paul Lepeudry, Hiroshi I. Suzuki, Phillip A. Sharp, Arup K. Chakraborty
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2018)
Article
Multidisciplinary Sciences
Ang Gao, Richard C. Remsing, John D. Weeks
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2020)
Article
Multidisciplinary Sciences
Dariusz K. Murakowski, John P. Barton, Lauren Peter, Abishek Chandrashekar, Esther Bondzie, Ang Gao, Dan H. Barouch, Arup K. Chakraborty
Summary: An effective vaccine against HIV infection has not been developed due to the virus's high mutability and ability to evolve mutations. Researchers designed a long peptide immunogen based on fitness landscapes of HIV proteins, successfully generating T-cell responses in macaques comparable to those induced by vaccines with whole HIV protein inserts. Further research on using this vaccine construct for HIV prevention and treatment is warranted.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Multidisciplinary Sciences
Ang Gao, Zhilin Chen, Assaf Amitai, Julia Doelger, Vamsee Mallajosyula, Emily Sundquist, Florencia Pereyra Segal, Mary Carrington, Mark M. Davis, Hendrik Streeck, Arup K. Chakraborty, Boris Julg
Summary: A physics-based learning model was used to predict the immunogenicity of CTL epitopes derived from SARS-CoV-2, showing that only some epitopes are immunogenic, with spike protein epitopes being less likely to provide broad immune coverage. Additionally, some immunogenic SARS-CoV-2 CTL epitopes were found to be identical to those of seasonal coronaviruses, suggesting existing CTL immunity against COVID-19 in some individuals prior to infection.