jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sok I. Tam | Pak-Kin Leong | Chi-Pui Tang | Weng-Hang Leong | Toshimori Sekine | Chi-Long Tang | Kuan Vai Tam | U KinTak
Source: Crystals (article, 2023-05-16)
Field: Materials Science — Materials Chemistry

Study of the Electronic Band Structure and Structural Stability of Al(CN)2 and Si(CN)2 by Density Functional Theory By substituting the A site in P21/c-A(CN)2 and varying the lattice parameters a, b, c, and the unit-cell angles, along with using crystal graph convolutional neural networks to calculate their cohesive energy, the candidate compounds, Al(CN)2 and Si(CN)2, were selected from the structure with the lowest cohesive energy. The two candidate structures were then optimized using first-principles calculations, and their phonon, electronic, and elastic properties were computed. As a result, two dynamically stable structures were found: Al(CN)2 with a space group of Cmcm and Si(CN)2 with a space group of R3¯m. Their phonon spectra exhibited no imaginary frequencies; thus, their elastic constants satisfied the mechanical stability criteria. Structurally, Si(CN)2 is similar to 6H-SiC and 15R-SiC. Its elastic constants indicated that it is harder than those SiC materials. Al(CN)2 exhibits metallic properties and the indirect wide-bandgap of Si(CN)2 was calculated by the generalized gradient approximation, the local density approximation, and the screened hybrid functional of Heyd, Scuseria, and Ernzerhof (HSE06) is found to be 3.093, 3.048, and 4.589 eV, respectively. According to this wide bandgap, we can conclude that Si(CN)2 has the potential to be used in high-temperature and high-power environments, making it usable in a broad range of applications. Hybrid functional ; Density functional theory ; Band gap ; Phonon ; Lattice constant ; Materials science ; Electronic band structure ; Structural stability ; Condensed matter physics ; Lattice (music) ; Computational chemistry ; Crystallography ; Chemistry ; Diffraction ; Physics ; Optoelectronics ; Quantum mechanics Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics ; MXene and MAX Phase Materials
Authors: Yilin Qu | Shijie Sun | Dan Zhou
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Thermal Conductivity of Paraffin-Expanded Graphite Composites by Machine Learning Method Thermal conductivity ; Composite material ; Graphite ; Materials science ; Thermal ; Thermodynamics ; Physics Graphite, nuclear technology, radiation studies ; Thermal and Kinetic Analysis ; Machine Learning in Materials Science
Authors: Kamron Fazel | Nima Karimitari | Tanooj Shah | Christopher Sutton | Ravishankar Sundararaman
Source: arXiv (Cornell University) (preprint, 2023-06-01)
Field: Materials Science — Materials Chemistry

Improving the reliability of machine learned potentials for modeling inhomogenous liquids The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical and biological processes at such interfaces. Classical molecular dynamics simulations have been applied extensively to simulate the response of inhomogeneous fluids directly, and as inputs to classical density functional theory, but are limited by the accuracy of the underlying empirical force fields. Here, we deploy neural network potentials (NNPs) trained to ab initio simulations to accurately predict the inhomogeneous response of two widely different fluids: liquid water and molten NaCl. Although NNPs can be readily trained to model complex bulk systems across a range of state points, in order to appropriately model a fluid's response at an interface, inhomogeneous configurations must be included in the training data. We establish protocols based on molecular dynamics simulations in external atomic potentials in order to sufficiently sample the correct configurations of inhomogeneous fluids. We show that NNPs trained to inhomogeneous fluid configurations can predict several properties such as the density response, surface tension and size-dependent cavitation free energies in water and molten NaCl corresponding to ab initio interactions more accurately than empirical force fields. This work therefore provides a first demonstration and framework for extracting the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials. Work (physics) ; Molecular dynamics ; Range (aeronautics) ; Ab initio ; Statistical physics ; Surface tension ; Density functional theory ; Water model ; Computer science ; Mechanics ; Physics ; Materials science ; Chemistry ; Thermodynamics ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Tanooj Shah | Kamron Fazel | Jie Lian | Liping Huang | Yunfeng Shi | Ravishankar Sundararaman
Source: arXiv (Cornell University) (preprint, 2023-06-04)
Field: Materials Science — Materials Chemistry

First-principles molten salt phase diagrams through thermodynamic integration Precise prediction of phase diagrams in molecular dynamics (MD) simulations is challenging due to the simultaneous need for long time scales, large length scales and accurate interatomic potentials. We show that thermodynamic integration (TI) from low-cost force fields to neural network potentials (NNPs) trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid-liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange-correlation functionals for predicting the NaCl phase boundary, and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid-liquid phase boundaries for any material at an ab-initio level of accuracy, with the majority of the computational cost at the level of classical potentials. Thermodynamic integration ; Phase diagram ; Molecular dynamics ; Statistical physics ; Density functional theory ; Boundary (topology) ; Dispersion (optics) ; Interatomic potential ; Phase (matter) ; Phase boundary ; Thermodynamics ; Materials science ; Chemistry ; Physics ; Computational chemistry ; Mathematics ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science
Authors: Duong Thuy Hoang | Paul Rulis
Source: Practice and Experience in Advanced Research Computing (article, 2023-07-23)
Field: Materials Science — Materials Chemistry

Machine Learning Potential Function Generation for ab initio Electronic Structure Calculations First-principles electronic structure calculations based on density functional theory (DFT) are well-known to have a high computational cost that scales algorithmically as O(N3), where N is the number of electrons. Reducing that cost is a key goal of the computational materials physics community and machine learning (ML) is viewed as an essential tool for that task. However, ML model training requires an appropriate match between the input descriptors and the target property as well as copious quantities of training data. Therefore, we present a computer program that is designed to automate the generation of local atomic environment descriptors for single element systems that may be used for training neural networks to predict electronic potential function coefficients, {Ai}, which are used within the DFT based orthogonalized linear combination of atomic orbitals (OLCAO) method [2]. In our approach, the total electronic potential function of a periodic crystal, is expressed as a sum of localized atom-centered Gaussian functions. Each Gaussian function, i, in the set of all Gaussian functions has a fixed αi coefficient. The set of {Ai} coefficients are updated in each cycle of the self-consistent field (SCF) iterations in accordance with the charge density that was computed in the previous SCF step. However, if the choice of coefficients {Ai} can be accurately predicted for a given system, then the SCF process can be skipped entirely, satisfying an important requirement of our goal to reduce the computational cost. The prediction method uses suitable neural networks (NNs) where the input values are a set of local atomic environment descriptors and the output values are the {Ai} coefficients for a targeted system. The descriptors we opted to use are the bispectrum components but other additional descriptors may be incorporated. Bispectrum components are geometric calculations that smoothly capture subtle variations in the local atomic environment and that are invariant under translation, rotation, and permutation of neighborhood atoms. The bispectrum components can also easily incorporate different types and numbers of elements, and they have been used by others for a similar purpose [3, 4]. Those requirements are difficult to achieve using other methods such as a list of bond angles and bond lengths toward nearest neighbor atoms while maintaining a fixed number of NN input features. where are expansion coefficients, Ab initio ; Electronic structure ; Ab initio quantum chemistry methods ; Computer science ; Function (biology) ; Artificial intelligence ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Physical and Chemical Molecular Interactions
Authors: Hossein Tahmasbi | Kushal Ramakrishna | Mani Lokamani | Attila Cangi
Source: arXiv (Cornell University) (preprint, 2023-11-10)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Structure Prediction for Iron Hydrides We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping algorithm. We demonstrate this method by producing a versatile machine-learned interatomic potential for iron hydride via a neural network using an iterative training process to explore its energy landscape under different pressures. To evaluate the accuracy and comprehend the intricacies of the PES, we conducted comprehensive crystal structure predictions using our neural network-based potential paired with the minima hopping approach. The predictions spanned pressures ranging from ambient to 100 GPa. Our results reproduce the experimentally verified global minimum structures such as \textit{dhcp}, \textit{hcp}, and \textit{fcc}, corroborating previous findings. Furthermore, our in-depth exploration of the iron hydride PES at different pressures has revealed complex alterations and stacking faults in these phases, leading to the identification of several new low-enthalpy structures. This investigation has not only confirmed the presence of regions of established FeH configurations but has also highlighted the efficacy of using data-driven, extensive structure prediction methods to uncover the multifaceted PES of materials. Maxima and minima ; Artificial neural network ; Workflow ; Crystal structure prediction ; Computer science ; Algorithm ; Energy (signal processing) ; Interatomic potential ; Potential energy surface ; Crystal structure ; Stacking ; Materials science ; Computational science ; Machine learning ; Molecular dynamics ; Physics ; Crystallography ; Ab initio ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Joan–Emma Shea | T. Daniel Crawford | Martin T. Zanni | Gregory V. Hartland | William Aumiller
Source: The Journal of Physical Chemistry C (review, 2023-01-12)
Field: Chemistry — Physical and Theoretical Chemistry

A Review of 2022 and a Look at 2023 ADVERTISEMENT RETURN TO ISSUEEditorialNEXTA Review of 2022 and a Look at 2023Joan-Emma Shea*Joan-Emma SheaDepartment of Chemistry and Biochemistry and Department of Physics, University of California, Santa Barbara, California 93106, United States*[email protected]More by Joan-Emma Sheahttps://orcid.org/0000-0002-9801-9273, T. Daniel CrawfordT. Daniel CrawfordDepartment of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United StatesMolecular Sciences Software Institute, Blacksburg, Virginia 24060, United StatesMore by T. Daniel Crawfordhttps://orcid.org/0000-0002-7961-7016, Martin T. ZanniMartin T. ZanniDepartment of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United StatesMore by Martin T. Zannihttps://orcid.org/0000-0001-7191-9768, Gregory V. HartlandGregory V. HartlandDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United StatesMore by Gregory V. Hartlandhttps://orcid.org/0000-0002-8650-6891, and William AumillerWilliam AumillerAmerican Chemical Society, 1155 Sixteenth Street N.W., Washington, D.C. 20036, United StatesMore by William Aumillerhttps://orcid.org/0000-0002-9260-3575Cite this: J. Phys. Chem. C 2023, 127, 1, 1–2Publication Date (Web):January 12, 2023Publication History Received30 November 2022Published online12 January 2023Published inissue 12 January 2023https://pubs.acs.org/doi/10.1021/acs.jpcc.2c08411https://doi.org/10.1021/acs.jpcc.2c08411editorialACS PublicationsCopyright © Published 2023 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views512Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-AlertscloseSUBJECTS:Energy,Machine learning,Materials,Physical chemistry,Vinyl Get e-Alerts Environmental science Various Chemistry Research Topics ; Academic Writing and Publishing ; Chemical Reactions and Mechanisms
Authors: Myungjae Kim | Jiho Kim | Hyokyeong Kim | Jiwoong Kim
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

High-Throughput Data-Driven Machine Learning Predictions of Thermal Expansion Coefficient in High-Entropy Solid Solution Carbides Thermal expansion ; Carbide ; Throughput ; Entropy (arrow of time) ; Thermodynamics ; Materials science ; Computer science ; Physics ; Metallurgy Thermal and Kinetic Analysis ; Advanced materials and composites ; Machine Learning in Materials Science
Authors: Joan–Emma Shea | T. Daniel Crawford | Martin T. Zanni | Gregory V. Hartland | William Aumiller
Source: The Journal of Physical Chemistry A (review, 2023-01-12)
Field: Chemistry — Physical and Theoretical Chemistry

A Review of 2022 and a Look at 2023 ADVERTISEMENT RETURN TO ISSUEEditorialNEXTA Review of 2022 and a Look at 2023Joan-Emma Shea*Joan-Emma SheaDepartment of Chemistry and Biochemistry and Department of Physics, University of California, Santa Barbara, California 93106, United States*[email protected]More by Joan-Emma Sheahttps://orcid.org/0000-0002-9801-9273, T. Daniel CrawfordT. Daniel CrawfordDepartment of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United StatesMolecular Sciences Software Institute, Blacksburg, Virginia 24060, United StatesMore by T. Daniel Crawfordhttps://orcid.org/0000-0002-7961-7016, Martin T. ZanniMartin T. ZanniDepartment of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United StatesMore by Martin T. Zannihttps://orcid.org/0000-0001-7191-9768, Gregory V. HartlandGregory V. HartlandDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United StatesMore by Gregory V. Hartlandhttps://orcid.org/0000-0002-8650-6891, and William AumillerWilliam AumillerAmerican Chemical Society, 1155 Sixteenth Street N.W., Washington, D.C. 20036, United StatesMore by William Aumillerhttps://orcid.org/0000-0002-9260-3575Cite this: J. Phys. Chem. A 2023, 127, 1, 1–2Publication Date (Web):January 12, 2023Publication History Received30 November 2022Published online12 January 2023Published inissue 12 January 2023https://pubs.acs.org/doi/10.1021/acs.jpca.2c08409https://doi.org/10.1021/acs.jpca.2c08409editorialACS PublicationsCopyright © Published 2023 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views626Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-AlertscloseSUBJECTS:Energy,Machine learning,Materials,Physical chemistry,Vinyl Get e-Alerts Environmental science Various Chemistry Research Topics ; Academic Writing and Publishing ; Chemical Reactions and Mechanisms
Authors: Joan–Emma Shea | T. Daniel Crawford | Martin T. Zanni | Gregory V. Hartland | William Aumiller
Source: The Journal of Physical Chemistry B (review, 2023-01-12)
Field: Chemistry — Physical and Theoretical Chemistry

A Review of 2022 and a Look at 2023 ADVERTISEMENT RETURN TO ISSUEEditorialNEXTA Review of 2022 and a Look at 2023Joan-Emma Shea*Joan-Emma SheaDepartment of Chemistry and Biochemistry and Department of Physics, University of California, Santa Barbara, California 93106, United States*[email protected]More by Joan-Emma Sheahttps://orcid.org/0000-0002-9801-9273, T. Daniel CrawfordT. Daniel CrawfordDepartment of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United StatesMolecular Sciences Software Institute, Blacksburg, Virginia 24060, United StatesMore by T. Daniel Crawfordhttps://orcid.org/0000-0002-7961-7016, Martin T. ZanniMartin T. ZanniDepartment of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United StatesMore by Martin T. Zannihttps://orcid.org/0000-0001-7191-9768, Gregory V. HartlandGregory V. HartlandDepartment of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United StatesMore by Gregory V. Hartlandhttps://orcid.org/0000-0002-8650-6891, and William AumillerWilliam AumillerAmerican Chemical Society, 1155 Sixteenth Street N.W., Washington, D.C. 20036, United StatesMore by William Aumillerhttps://orcid.org/0000-0002-9260-3575Cite this: J. Phys. Chem. B 2023, 127, 1, 1–2Publication Date (Web):January 12, 2023Publication History Received30 November 2022Published online12 January 2023Published inissue 12 January 2023https://pubs.acs.org/doi/10.1021/acs.jpcb.2c08410https://doi.org/10.1021/acs.jpcb.2c08410editorialACS PublicationsCopyright © Published 2023 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views302Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-AlertscloseSUBJECTS:Energy,Machine learning,Materials,Physical chemistry,Vinyl Get e-Alerts Environmental science Various Chemistry Research Topics ; Academic Writing and Publishing ; Chemical Reactions and Mechanisms
Authors: Yuqi Cao | Yan Shi | Ping Xiang | Yin‐Long Guo
Source: PubMed (review, 2023-08-25)
Field: Chemistry — Spectroscopy

Research Progress on Machine Learning Assisted Non-Targeted Screening Strategy for Identification of Fentanyl Analogs. In recent years, the types and quantities of fentanyl analogs have increased rapidly. It has become a hotspot in the illicit drug control field of how to quickly identify novel fentanyl analogs and to shorten the blank regulatory period. At present, the identification methods of fentanyl analogs that have been developed mostly rely on reference materials to target fentanyl analogs or their metabolites with known chemical structures, but these methods face challenges when analyzing new compounds with unknown structures. In recent years, emerging machine learning technology can quickly and automatically extract valuable features from massive data, which provides inspiration for the non-targeted screening of fentanyl analogs. For example, the wide application of instruments like Raman spectroscopy, nuclear magnetic resonance spectroscopy, high resolution mass spectrometry, and other instruments can maximize the mining of the characteristic data related to fentanyl analogs in samples. Combining this data with an appropriate machine learning model, researchers may create a variety of high-performance non-targeted fentanyl identification methods. This paper reviews the recent research on the application of machine learning assisted non-targeted screening strategy for the identification of fentanyl analogs, and looks forward to the future development trend in this field. Fentanyl ; Identification (biology) ; Artificial intelligence ; Machine learning ; Computer science ; Drug discovery ; Data science ; Biochemical engineering ; Nanotechnology ; Engineering ; Medicine ; Bioinformatics ; Pharmacology ; Materials science ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Forensic Toxicology and Drug Analysis