jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Takahiro Ohkubo | Akihiro Takei | Yukio Tachi | Yuta Fukatsu | Kenzo Deguchi | Shinobu Ohki | Tadashi Shimizu
Source: The Journal of Physical Chemistry A (article, 2023-01-19)
Field: Chemistry — Spectroscopy

New Approach To Understanding the Experimental <sup>133</sup>Cs NMR Chemical Shift of Clay Minerals via Machine Learning and DFT-GIPAW Calculations Structural determination of adsorbed atoms on layered structures such as clay minerals is a complex subject. Radioactive cesium (Cs) is an important element for environmental conservation, so it is vital to understand its adsorption structure on clay. The nuclear magnetic resonance (NMR) parameters of <sup>133</sup>Cs, which can be determined from solid-state NMR experiments, are sensitive to the local neighboring structures of adsorbed Cs. However, determining the Cs positions from NMR data alone is difficult. This paper describes an approach for identifying the expected atomic positions on clay minerals by combining machine learning (ML) with experimentally observed chemical shifts. A linear ridge regression model for ML is constructed from the smooth overlap of atomic position descriptor and gauge-including projector augmented wave (GIPAW) ab initio data. The constructed ML model predicts the GIPAW data to within a 3 ppm root-mean-squared error. At this stage, the <sup>133</sup>Cs chemical shifts can be instantaneously calculated from the Cs positions on any clay layers using ML. The inverse analysis, which derives the atomic positions from experimentally observed chemical shifts, is developed from the ML model. The input data for the inverse analysis are the layer structure and the experimentally observed chemical shifts. The Cs positions for the targeted chemical shifts are then output. Inverse analysis is applied to montmorillonite, and the resultant Cs positions are found to be consistent with previous results (Ohkubo, T.; et al. <i>J. Phys. Chem. A</i> <b>2018</b>, <i>122</i>, 9326-9337). The Cs positions on saponite clay are also clarified from experimentally observed chemical shifts and inverse analysis. Chemistry ; Chemical shift ; Adsorption ; Clay minerals ; Solid-state nuclear magnetic resonance ; Inverse ; Analytical Chemistry (journal) ; Mineralogy ; Nuclear magnetic resonance ; Physical chemistry ; Physics ; Geometry ; Mathematics Advanced NMR Techniques and Applications ; Chemical Synthesis and Characterization ; X-ray Diffraction in Crystallography
Authors: Qiuxiang Yao | Lei He | Duo Ma | Linyang Wang | Li Ma | Huiyong Chen | Qingqing Hao | Ming Sun
Source: Energy (article, 2023-12-15)
Field: Chemistry — Inorganic Chemistry

Cracking of heavy-inferior oils with different alkane-aromatic ratios to aromatics over MFI zeolites：Structure-activity relationship derived by machine learning Alkane ; Cracking ; Fluid catalytic cracking ; Environmental chemistry ; Chemistry ; Organic chemistry ; Hydrocarbon ; Chemical engineering ; Engineering Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Minqi Shao | Jiahui Chen | Tian Wang | Fei Tao | Juan Du | Dashun Zhang | Xueqian Wang | Xingling Tang
Source: Advanced Intelligent Systems (article, 2023-01-18)
Field: Materials Science — Materials Chemistry

Accelerating Analysis for Structure Design via Deep Learning Surrogate Models Using computer simulation tools such as finite element analysis (FEA) to perform material stress analysis is a common design method in engineering practice. In order to model more realistic real‐world systems, simulation models have become more complex, and calculation becomes more expensive as a result. The rise of artificial intelligence technologies has made it possible to integrate deep learning methods and material stress analysis. Herein, FEA software is employed to obtain a large number of analysis cases as training samples and uses a fully connected neural network and long‐short‐term memory neural network as surrogate models, which can predict the stress distribution and stress sequence in the process of the bullet impacting target plates with different materials. These models can give results similar to FEA with 92.19% and 92.41% accuracy, respectively. The experimental results show that the deep learning surrogate models have great potential in material stress analysis. Surrogate model ; Artificial neural network ; Finite element method ; Computer science ; Artificial intelligence ; Deep learning ; Stress (linguistics) ; Process (computing) ; Machine learning ; Engineering ; Structural engineering Machine Learning in Materials Science ; Advanced machining processes and optimization ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Junjie Zhang | Hao Zhang | Jing Wu | Xin Qian | Bai Song | Cheng‐Te Lin | Te‐Huan Liu | Ronggui Yang
Source: Cell Reports Physical Science (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

Vacancy-induced phonon localization in boron arsenide using a unified neural network interatomic potential Boron arsenide, considered an ideal semiconductor, inevitably introduces arsenic defects during crystal growth. Here, we develop a unified neural network interatomic potential with quantum-mechanical precision that accurately describes phonon transport properties in both perfect and defective boron arsenides. Through molecular dynamics simulations, we quantitatively explore the degree of phonon localization in boron arsenide caused by arsenic vacancies. We confirm that this localization primarily affects vibration modes within the frequency range of 2.0–4.0 THz, which is a challenge for conventional first-principles approaches. In addition, we examine the fluctuation of the heat flux autocorrelation function, which reveals the extent of phonon phase disruption resulting from arsenic voids and lattice anharmonicity from a more fundamental perspective. Our study highlights the applicability of molecular dynamics simulations in conjunction with neural network interatomic potential for defective systems, laying the theoretical groundwork for phonon engineering in real semiconductor crystals. Phonon ; Anharmonicity ; Condensed matter physics ; Materials science ; Gallium arsenide ; Semiconductor ; Boron nitride ; Interatomic potential ; Boron ; Molecular dynamics ; Chemistry ; Physics ; Optoelectronics ; Nanotechnology ; Computational chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Song Xia | Dongdong Zhang | Yingkai Zhang
Source: Journal of Chemical Theory and Computation (article, 2023-01-06)
Field: Materials Science — Materials Chemistry

Multitask Deep Ensemble Prediction of Molecular Energetics in Solution: From Quantum Mechanics to Experimental Properties The past few years have witnessed significant advances in developing machine learning methods for molecular energetics predictions, including calculated electronic energies with high-level quantum mechanical methods and experimental properties, such as solvation free energy and logP. Typically, task-specific machine learning models are developed for distinct prediction tasks. In this work, we present a multitask deep ensemble model, sPhysNet-MT-ens5, which can simultaneously and accurately predict electronic energies of molecules in gas, water, and octanol phases, as well as transfer free energies at both calculated and experimental levels. On the calculated data set Frag20-solv-678k, which is developed in this work and contains 678,916 molecular conformations, up to 20 heavy atoms, and their properties calculated at B3LYP/6-31G* level of theory with continuum solvent models, sPhysNet-MT-ens5 predicts density functional theory (DFT)-level electronic energies directly from force field-optimized geometry within chemical accuracy. On the experimental data sets, sPhysNet-MT-ens5 achieves state-of-the-art performances, which predict both experimental hydration free energy with a RMSE of 0.620 kcal/mol on the FreeSolv data set and experimental logP with a RMSE of 0.393 on the PHYSPROP data set. Furthermore, sPhysNet-MT-ens5 also provides a reasonable estimation of model uncertainty which shows correlations with prediction error. Finally, by analyzing the atomic contributions of its predictions, we find that the developed deep learning model is aware of the chemical environment of each atom by assigning reasonable atomic contributions consistent with our chemical knowledge. Solvation ; Density functional theory ; Force field (fiction) ; Work (physics) ; Mean squared error ; Atom (system on chip) ; Computer science ; Chemistry ; Statistical physics ; Molecule ; Machine learning ; Artificial intelligence ; Thermodynamics ; Computational chemistry ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Alejandro Santana‐Bonilla | Raquel López-Ríos de Castro | Peike Sun | Robert M. Ziolek | Christian D. Lorenz
Source: Journal of Chemical Information and Modeling (article, 2023-06-08)
Field: Materials Science — Materials Chemistry

Modular Software for Generating and Modeling Diverse Polymer Databases Machine learning methods offer the opportunity to design new functional materials on an unprecedented scale; however, building the large, diverse databases of molecules on which to train such methods remains a daunting task. Automated computational chemistry modeling workflows are therefore becoming essential tools in this data-driven hunt for new materials with novel properties, since they offer a means by which to create and curate molecular databases without requiring significant levels of user input. This ensures that well-founded concerns regarding data provenance, reproducibility, and replicability are mitigated. We have developed a versatile and flexible software package, PySoftK (Python Soft Matter at King's College London) that provides flexible, automated computational workflows to create, model, and curate libraries of polymers with minimal user intervention. PySoftK is available as an efficient, fully tested, and easily installable Python package. Key features of the software include the wide range of different polymer topologies that can be automatically generated and its fully parallelized library generation tools. It is anticipated that PySoftK will support the generation, modeling, and curation of large polymer libraries to support functional materials discovery in the nanotechnology and biotechnology arenas. Workflow ; Python (programming language) ; Modular design ; Computer science ; Software ; Extensibility ; Software engineering ; Data science ; World Wide Web ; Database ; Programming language Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Sergei Manzhos | Manabu Ihara
Source: Artificial Intelligence Chemistry (article, 2023-08-15)
Field: Materials Science — Materials Chemistry

Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization Orders-of-coupling representations (representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates corresponding to different orders of coupling) are useful in many applications, for example, in computational chemistry and other applications, especially where integration is needed. Examples include N-mode approximations and many-body expansions. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Comm. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces. Artificial neural network ; Coupling (piping) ; Representation (politics) ; Nonlinear system ; Gaussian ; Computer science ; Energy (signal processing) ; Multivariate statistics ; Gaussian process ; Algorithm ; Process (computing) ; Applied mathematics ; Mathematics ; Artificial intelligence ; Biological system ; Physics ; Machine learning ; Quantum mechanics ; Engineering Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods
Authors: Mehdi Khashei | Erfan Nazgouei | Negar Bakhtiarvand
Source: Journal of Chemical Information and Modeling (article, 2023-02-10)
Field: Chemistry — Analytical Chemistry

Intelligent Discrete Deep Learning Based Classification Methodology in Chemometrics In recent years, deep learning models have attracted much attention for classification purposes in chemometrics. The popularity of deep learning models in this field comes from their unique features like universal approximation capability with the desired accuracy. Deep learning classifiers use several intelligent processing layers to model mixed, complex, and nonlinear patterns in the underlying data sets, which is why the development of deep learning based models has never been stopped in the chemometrics literature. Despite the variety of deep learning classification models used in this field, they all use a continuous distance-based cost function in their learning processes. Although using a continuous cost function for learning deep classifiers is a common approach, it conflicts with the discrete nature of the classification problem. In fact, applying a continuous cost function for inherently discrete classification problems can reduce the performance of the classification. In this research, a novel discrete learning based classification approach is proposed and implemented on a deep feed-forward neural network as one of the most commonly used deep learning models to develop a different learning process for deep classification models. The basis of the proposed learning approach is maximizing a discrete matching function of the actual and fitted values instead of minimizing a continuous distance-based cost function. The proposed classification approach is evaluated on five benchmark data sets in the chemistry field. The empirical results indicated the superiority of the proposed discrete deep learning approach over its classic continuous form. The results of this study demonstrate the important effect of discrete learning processes on the performances of deep learning classification models. Therefore, the proposed methodology can be a powerful alternative to common classification approaches to analyze chemical data in the chemometrics field. Artificial intelligence ; Deep learning ; Machine learning ; Computer science ; Chemometrics ; Artificial neural network ; Field (mathematics) ; Benchmark (surveying) ; Function (biology) ; Pattern recognition (psychology) ; Mathematics Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Water Quality Monitoring and Analysis
Authors: Kaixuan Gu | Sen Lin
Source: Angewandte Chemie (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

Sustained Hydrogen Spillover on Pt/Cu(111) Single‐Atom Alloy: Dynamic Insights into Gas‐Induced Chemical Processes Abstract Hydrogen spillover, involving the surface migration of dissociated hydrogen atoms from active metal sites to the relatively inert catalyst support, plays a crucial role in hydrogen‐involved catalytic processes. However, a comprehensive understanding of how H atoms are driven to spill over from active sites onto the catalyst support is still lacking. Here, we examine the atomic‐scale perspective of the H spillover process on a Pt/Cu(111) single atom alloy surface using machine‐learning accelerated molecular dynamics calculations based on density functional theory. Our results show that when an impinging H 2 dissociates at an active Pt site, the Pt atom undergoes deactivation due to the dissociated hydrogen atoms that attach to it. Interestingly, collisions between H 2 and sticking H atoms facilitate H spillover onto the host Cu, leading to the reactivation of the Pt atom and the realization of a continuous H spillover process. This work underscores the importance of the interaction between gas molecules and adsorbates as a driving force in elucidating chemical processes under a gaseous atmosphere, which has so far been underappreciated in thermodynamic studies. Hydrogen spillover ; Hydrogen ; Catalysis ; Chemical physics ; Chemistry ; Density functional theory ; Hydrogen atom ; Molecule ; Atom (system on chip) ; Spillover effect ; Molecular dynamics ; Metal ; Inert gas ; Nanotechnology ; Materials science ; Computational chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Kiran K. Yalamanchi | Sahil Kommalapati | Pinaki Pal | Nursulu Kuzhagaliyeva | Abdullah S. AlRamadan | Balaji Mohan | Yuanjiang Pei | S. Mani Sarathy | Emre Cenker | Jihad Badra
Source: Applications in Energy and Combustion Science (article, 2023-09-24)
Field: Chemistry — Organic Chemistry

Uncertainty quantification of a deep learning fuel property prediction model Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. However, the uncertainties of the developed models are seldom quantified, which is primarily attributed to the huge incremental cost of adding UQ to already computationally expensive deep learning models. In this work, UQ methods with different ranges of computational cost are applied in the context of fuel property prediction. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed on one such multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprint is encoded via Extractor 1 and Extractor 2, the mixing operator generates mixture fingerprints based on linear operation, and the predictor maps fingerprints to the target properties. Two different classes of UQ methods, monte-Carlo ensemble methods and Bayesian neural networks, are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. Although this study is confined to UQ of fuel property prediction, the methodologies and findings are applicable to other deep learning frameworks that are being widely used in the combustion community. Uncertainty quantification ; Artificial intelligence ; Deep learning ; Computer science ; Context (archaeology) ; Surrogate model ; Machine learning ; Artificial neural network Chemical Thermodynamics and Molecular Structure ; Advanced Combustion Engine Technologies ; Heat transfer and supercritical fluids
Authors: Junji Zhang | Joshua Pagotto | Tim Gould | Timothy T. Duignan
Source: arXiv (Cornell University) (preprint, 2023-10-19)
Field: Materials Science — Materials Chemistry

Scalable molecular simulation of electrolyte solutions with quantum chemical accuracy Electrolyte solutions play critical role in a vast range of important applications, yet an accurate and scalable method of predicting their properties without fitting to experiment has remained out of reach, despite over a century of effort. Here, we combine state-of-the-art density functional theory and equivariant neural network potentials to demonstrate this capability, reproducing key structural, thermodynamic, and kinetic properties. We show that neural network potentials (NNPs) can be recursively trained on a subset of their own output to enable coarse-grained/continuum-solvent molecular simulations that can access much longer timescales than possible with all atom simulations. We observe the surprising formation of Li cation dimers along with identical anion-anion pairing of chloride and bromide anions. Finally, we reproduce simulate the crystal phase and infinite dilution pairing free energies despite being trained only on moderate concentration solutions. This approach should be scaled to build a greatly expanded database of electrolyte solution properties than currently exists. Electrolyte ; Aqueous solution ; Lithium (medication) ; Density functional theory ; Statistical physics ; Lithium chloride ; Meaning (existential) ; Computer science ; Diffusion ; Quantum chemistry ; Range (aeronautics) ; Chemistry ; Thermodynamics ; Chemical physics ; Computational chemistry ; Materials science ; Physics ; Electrochemistry ; Physical chemistry ; Inorganic chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies