Article
Chemistry, Physical
Logan J. Augustine, Ali Abbaspour Tamijani, Jennifer L. Bjorklund, Hind A. Al-Abadleh, Sara E. Mason
Summary: The interactions between organic molecules and mineral surfaces are influenced by various factors, including adsorbate speciation, surface atomic and electronic structure, and environmental conditions. This study used Density Functional Theory (DFT) to model the inner-sphere adsorption of oxalate and pyrocatechol on different alpha-Fe2O3 surfaces. The results revealed that each surface facet has a unique factor that determines the site preference. The findings provide insights into understanding the adsorption processes occurring at the surface-aqueous interface.
JOURNAL OF COLLOID AND INTERFACE SCIENCE
(2022)
Article
Chemistry, Physical
David W. Kastner, Aditya Nandy, Rimsha Mehmood, Heather J. Kulik
Summary: Non-heme iron halogenases and hydroxylases catalyze the functionalization of diverse biological products by activating inert C-H bonds. Crystallographic and spectroscopic data analysis shows that hydroxylases and halogenases exhibit different substrate positioning preferences. Molecular dynamics simulations guided by experimental information reveal the key substrate interaction partners and the impact of the protein environment on the substrate approach angles.
Article
Chemistry, Physical
David W. Kastner, Aditya Nandy, Rimsha Mehmood, Heather J. Kulik
Summary: Non-heme iron halogenases and hydroxylases regulate the formation of diverse biological products under physiological conditions. Crystallographic and spectroscopic data showed that hydroxylases prefer an acute angle while halogenases prefer a more obtuse angle. Molecular dynamics simulations revealed that the protein environment in halogenases prevents the sampling of acute angles observed in hydroxylases and vice versa.
Article
Chemistry, Physical
Jared R. Williams, Nicolas Tancogne-Dejean, Carsten A. Ullrich
Summary: Time-dependent density-functional theory (TDDFT) is an efficient method for calculating optical spectra, providing insight into exciton dynamics by obtaining exciton wave functions and understanding the formation and dissociation of excitons in real time.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2021)
Article
Biochemistry & Molecular Biology
Xiaoji Zhao, Yanlu Li, Xian Zhao
Summary: This study investigates the structure, stability, and electronic structure of hydrogen and oxygen vacancy defects on the (100) and (101) growth surfaces of KDP crystals using density functional theory. The effects of acidic and alkaline environments on surface defects are also discussed. The results show that different vacancy defects have varying properties on different surfaces, and acidic environments are conducive to repairing surface defects.
Article
Construction & Building Technology
Chongchong Qi, Xinhang Xu, Daolin Wang, Yan Feng, Qinli Zhang, Qiusong Chen
Summary: This study used first-principle density functional theory (DFT) calculations to analyze the adsorption of water molecules on C2S surfaces. The results showed that the adsorption energy increased with the number of adsorbed water molecules, and multi-water molecules adsorption was generally anti-cooperative. Electrons were transferred from the surface to water molecules after adsorption, and water molecules became more stable upon adsorption.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Chemistry, Multidisciplinary
Matteo De Santis, Valerie Vallet, Andre Severo Pereira Gomes
Summary: In this work, the performance of real-time time-dependent block-orthogonalized Manby-Miller embedding (rt-BOMME) approach is investigated in reproducing X-ray absorption spectra (XAS) obtained with standard real-time frozen density embedding time-dependent density functional theory (rt-TDDFT-in-DFT FDE) simulations. Model systems of solvated fluoride and chloride ions ([X@ ( H2O)(8)](-) , X = F, Cl) are considered. The results show that the BOMME approach provides significantly better agreement with supermolecular results in ground-state quantities compared to FDE for the strongly interacting fluoride system, while for chloride the two methods show similar results. For excited states, the BOMME approach provides a faithful qualitative representation of the spectra in all energy regions considered, but it induces non-negligible shifts in peak positions for the excitations from the halide to the environment due to its lower-accuracy exchange-correlation functional. The study concludes that QM/QM embedding approaches are viable alternatives for real-time simulations of X-ray absorption spectra of species in complex or confined environments.
FRONTIERS IN CHEMISTRY
(2022)
Article
Chemistry, Physical
Wei Shang, Jiaduo Zhu, Xinhao Wang, Shengrui Xu, Jincheng Zhang, Yue Hao
Summary: BAlN-based digital alloy (DA) arranged along [10-10] and [11-20] directions overcomes the phase separation issue of solid solution alloy (SSA) along [0001] direction and shows improved growth feasibility. DA exhibits a wide range of electric, optical, and mechanical properties due to cation orders at the same composition, some of which are even superior to conventional SSA. Additionally, DA demonstrates excellent thermodynamic stability and adjustability, making it a suitable material for high-quality ultra-wide bandgap BAlN alloy growth and applications.
JOURNAL OF ALLOYS AND COMPOUNDS
(2023)
Article
Biochemistry & Molecular Biology
Austin Biaggne, William B. Knowlton, Bernard Yurke, Jeunghoon Lee, Lan Li
Summary: The properties of dye monomers greatly influence their aggregation ability and exciton dynamics. By engineering dyes with specific substituents, optimal key properties like hydrophobicity and dipole moments can be achieved. This study found that electron withdrawing substituents significantly affect the solvation energy of the dye, while various pairs of substituents can enhance the static dipole difference.
Article
Materials Science, Multidisciplinary
Man Jiang, Hui Du, Ao Gan, Muyi Ni, Bin Zhao
Summary: Radiotoxic Po, mainly formed as PbPo, is produced during normal operation of lead-bismuth eutectic in lead-bismuth fast reactors and accelerator-drive systems. Some hazardous PbPo molecules evaporate and accumulate in the cover gas. This study investigated the adsorption and dissociation of PbPo on Pd surfaces using density functional theory. The results showed that PbPo strongly chemisorbs on Pd(100), Pd(110), and Pd(111) surfaces, with adsorption energies ranging from -1.14 eV to -5.36 eV.
JOURNAL OF NUCLEAR MATERIALS
(2023)
Article
Physics, Fluids & Plasmas
M. A. Osipov, A. A. Antonov, M. Gorkunov
Summary: A molecular-statistical theory of the orientational elasticity of nematic liquid crystals has been developed, with explicit expressions for the elasticity tensor and Frank elastic constants obtained. The study shows that the elastic constants are more sensitive to the details of the intermolecular interaction potential, and a relatively weak polarity of the molecular shape may lead to unusual behavior in the splay constant, potentially causing instability in the homogeneous nematic phase.
Article
Nanoscience & Nanotechnology
Shyama Charan Mandal, Biswarup Pathak
Summary: The study explored the mechanistic pathways for Cu-NC catalyst in CO2 hydrogenation reactions, finding that Cu-NC is selective towards C-2 based products with lower limiting potential compared to periodic surfaces. The findings suggest that Cu-NC based catalysts may be more promising for C-2 based products.
ACS APPLIED NANO MATERIALS
(2021)
Review
Chemistry, Physical
Neepa T. Maitra
Summary: Time-dependent density functional theory is a preferred method for calculating spectra and response properties in physics, chemistry, and biology. Its ability to scale to larger systems has made computations possible that were not previously achievable. While simple functional approximations have been successful in handling increasingly complex and interesting systems, there is a growing awareness that these approximations may fail for certain classes of problems. This review discusses the challenges and progress in describing double excitations and charge-transfer excitations, two common obstacles to the theory's application.
ANNUAL REVIEW OF PHYSICAL CHEMISTRY
(2022)
Review
Chemistry, Multidisciplinary
Nicholas A. Besley
Summary: The availability of X-ray light sources with increased resolution and intensity has provided a foundation for increasingly sophisticated experimental studies exploiting the spectroscopy of core electrons to probe fundamental chemical, physical, and biological processes. Quantum chemical calculations, particularly density functional theory (DFT) and time-dependent density functional theory (TDDFT), play a critical role in the analysis of experimental measurements. Current developments in applying DFT and TDDFT to study key X-ray spectroscopy techniques are presented, along with insights into the achievable accuracy and challenges in modeling core electron spectroscopy with DFT.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2021)
Article
Biochemistry & Molecular Biology
Mikhail S. Kuklin, Kim Eklund, Jarno Linnera, Artturi Ropponen, Nikolas Tolvanen, Antti J. Karttunen
Summary: In this study, the structural properties, magnetic ground states, and fundamental electronic properties of 100 binary d-metal oxides were systematically investigated using hybrid density functional methods and localized basis sets. The PBE0 hybrid functional method was found to accurately describe the structural properties of most d-metal oxides, except for molecular oxides with weak intermolecular forces. A database of optimized geometries and magnetic ground states was provided for future studies on the more complex properties of binary d-metal oxides.
Article
Multidisciplinary Sciences
Sichao Li, Amanda S. Barnard
Summary: This study introduces a new approach to inverse design that utilizes the multi-functionality of nanomaterials to predict unique nanoparticle structures through multi-target regression. The workflow is general, can rapidly predict property/structure relationships, and guide further research and development without the need for additional optimization or high-throughput sampling.
ADVANCED THEORY AND SIMULATIONS
(2022)
Article
Multidisciplinary Sciences
George Opletal, Amanda S. Barnard
Summary: The functionality of many nanomaterials involves the collective properties of aggregates and self-assembled superstructures. Using the mesostructure as a design parameter requires predictive capabilities, but this is challenging when nanoparticle samples present a diverse mixture of shapes and surface facets. It is found that polydispersed samples have different aggregation behavior than monodispersed samples, particularly in regard to the interparticle coordination and degree of long-range order, but mixing nanoparticle shapes does not affect aggregate porosity.
ADVANCED THEORY AND SIMULATIONS
(2022)
Article
Multidisciplinary Sciences
Benyamin Motevalli, Lachlan Hyde, Bronwyn L. Fox, Amanda S. Barnard
Summary: This study utilizes supervised machine learning methods to predict the stability of graphene oxide nanostructures, achieving perfect accuracy based on a limited set of controllable structural features. A decision tree is used to illustrate how features determine stability, while a neural network provides an equation for predicting thermodynamic stability quickly. This research allows machine learning to be used as a research planning tool and for analyzing results from microanalysis.
ADVANCED THEORY AND SIMULATIONS
(2022)
Article
Chemistry, Physical
Sichao Li, Amanda S. Barnard
Summary: This paper presents a method to predict the chemical formula of MXenes based on battery performance criteria. By using a new categorical descriptor and multiple target regression and classification, specific MXene formulas with desired electrochemical properties are identified.
CHEMISTRY OF MATERIALS
(2022)
Article
Biotechnology & Applied Microbiology
Jonathan Y. C. Ting, Amanda Barnard
Summary: The field of nanocatalysis has benefited from traditional machine learning methods, but purely correlational studies lack actionability. By utilizing causal inference, deeply obscured causal relationships between variables can be discovered and verified, providing more actionable insights. Collaborative usage of correlational and causal analysis in catalysis has been discussed, as well as challenges and future directions in the application of inference techniques.
CURRENT OPINION IN CHEMICAL ENGINEERING
(2022)
Article
Materials Science, Multidisciplinary
Benyamin Motevalli, Bronwyn L. Fox, Amanda S. Barnard
Summary: In this study, machine learning methods were used to investigate the relationship between the structure of graphene oxide and the Fermi energy. The results showed that the ionic charge is the main determinant and three accurate charge-dependent structure/property relationships were defined.
COMPUTATIONAL MATERIALS SCIENCE
(2022)
Article
Chemistry, Physical
Jonathan Y. C. Ting, Amanda J. Parker, Amanda S. Barnard
Summary: In this study, a machine learning pipeline is proposed to group nanoparticles, classify their classes, and identify the relevant features. The results show that this method achieves accurate classification on a simulated ruthenium nanoparticles dataset and confirms the key features underlying different classes.
CHEMISTRY OF MATERIALS
(2023)
Article
Nanoscience & Nanotechnology
Inga C. Kuschnerus, Haotian Wen, Juanfang Ruan, Xinrui Zeng, Chun-Jen Su, U-Ser Jeng, George Opletal, Amanda S. Barnard, Ming Liu, Masahiro Nishikawa, Shery L. Y. Chang
Summary: Understanding the polydispersity of nanoparticles is crucial for their application as drug delivery carriers in biomedical field. Detonation nanodiamonds (DNDs), synthesized through detonation process, have great potential for drug delivery due to their stability and biocompatibility. However, their aggregate formation is poorly understood. In this study, we propose a novel characterization method using machine learning and cryo-transmission electron microscopy to characterize the unique colloidal behavior of DNDs, and we explain the differences in aggregation behavior between positively and negatively charged DNDs using small-angle X-ray scattering and mesoscale simulations. This method can be applied to other complex particle systems, providing essential knowledge for safe implementation of nanoparticles in drug delivery.
ACS NANOSCIENCE AU
(2023)
Article
Chemistry, Physical
Amanda S. Barnard, Bronwyn L. Fox
Summary: The application of machine learning to materials chemistry can accelerate the design process and guide future research. Shapley value analysis provides a comprehensive analysis of the underlying reasons behind structure/property relationships. In this study, ML models trained on graphene oxide nanomaterials data accurately predicted the formation energy and Fermi energy, and Shapley value analysis was used to understand the results.
CHEMISTRY OF MATERIALS
(2023)
Article
Chemistry, Physical
Zixin Zhuang, Amanda S. Barnard
Summary: This article introduces a structure-free encoding method called Mendeleev encoding for materials. The evaluation of Mendeleev encoding on three data sets for battery applications shows that it is more accurate, stable, and reliable than alternative structure-free encoding methods, and consistently provides superior clustering results.
CHEMISTRY OF MATERIALS
(2023)
Article
Chemistry, Physical
Zixin Zhuang, Bronwyn L. Fox, Amanda S. Barnard
Summary: Considerable effort has been made to control the physicochemical structure of graphene and graphene oxide. In this study, a workflow is proposed and demonstrated for predicting how to modify the properties of these materials by adjusting their structural features. The approach uses accurate multitarget regressors to predict ionization potential and electron affinity, and identifies the most important structural features. This general approach can guide experimental design and sample separation for specific applications.
JOURNAL OF PHYSICAL CHEMISTRY C
(2023)
Article
Chemistry, Multidisciplinary
Seungyeol Lee, Jizhe Cai, Shiyun Jin, Hiromi Konishi, Dongzhou Zhang, Amanda S. Barnard, Ramathasan Thevamaran, Huifang Xu
Summary: The phase relationships of TiO2 polymorphs are important in the study of earth and planetary science. This study used the LIPIT technique to investigate the shock metamorphism of TiO2 polymorphs and utilized various characterization techniques to analyze the phase transformations. The results provide insights into shock metamorphism in minerals and rocks and help expand our understanding of the process on planetary bodies.
ACS EARTH AND SPACE CHEMISTRY
(2023)
Article
Computer Science, Artificial Intelligence
W. Huang, A. S. Barnard
Summary: This paper discusses the challenges of property analysis and prediction in fields like chemistry, nanotechnology, and materials science, especially due to the lack of data. It introduces federated learning (FL) as a machine learning framework that encourages privacy-preserving collaborations between data owners, addressing the need for combining data while preserving proprietary information. The paper proposes the use of horizontal FL and FedRed, a new dimensionality reduction method, to mitigate data limitation issues and improve collaboration efficiency. Experimental results on metallic nanoparticles data sets demonstrate the effectiveness of FL in reducing the negative impact of insufficient data.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Sichao Li, Jonathan Y. C. Ting, Amanda S. Barnard
Summary: This paper investigates the challenges in inverse design of nanomaterials and proposes the use of multi-target machine learning and aggressive feature selection to improve the accuracy and efficiency of predictions.
COMPUTATIONAL SCIENCE, ICCS 2022, PT II
(2022)
Article
Chemistry, Multidisciplinary
Amanda S. Barnard
Summary: In this study, a re-usable model is developed using machine learning to predict the stability of specific defect complexes at different depths. A neural network is used to generate equations, and explainable artificial intelligence methods are utilized to identify the structural features and defect configurations responsible for the model's predictions.
CELL REPORTS PHYSICAL SCIENCE
(2022)