jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xue Zong | Dionisios G. Vlachos
Source: Journal of Chemical Information and Modeling (article, 2022-09-12)
Field: Materials Science — Materials Chemistry

Exploring Structure-Sensitive Relations for Small Species Adsorption Using Machine Learning Accurate prediction of adsorption energies on heterogeneous catalyst surfaces is crucial to predicting reactivity and screening materials. Adsorption linear scaling relations have been developed extensively but often lack accuracy and apply to one adsorbate and a single binding site type at a time. These facts undermine their ability to predict structure sensitivity and optimal catalyst structure. Using machine learning on nearly 300 density functional theory calculations, we demonstrate that generalized coordination number scaling relations hold well for oxygen- and high-valency carbon-binding species but fail for others. We reveal that the valency and the electronic coupling of a species with the surface, along with the site type and its coordination environment, are critical for small species adsorption. The model simultaneously predicts the adsorption energy and preferred site and significantly outperforms linear scalings in accuracy. It can expose the structure sensitivity of chemical reactions and enable enhanced catalyst activity via engineering particle shape and facet defects. The generality of our methodology is validated by training the model with transition metal data and transferring it to predict adsorption energies on single-atom alloys. Valency ; Adsorption ; Scaling ; Density functional theory ; Generality ; Sensitivity (control systems) ; Generalization ; Coupling (piping) ; Linear scale ; Catalysis ; Materials science ; Atom (system on chip) ; Binding energy ; Chemical physics ; Chemistry ; Computer science ; Computational chemistry ; Physics ; Mathematics ; Physical chemistry ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Stephan N. Steinmann | Angga Hermawan | Mohammed Bin Jassar | Zhi Wei Seh
Source: Chem Catalysis (article, 2022-03-14)
Field: Materials Science — Materials Chemistry

Autonomous high-throughput computations in catalysis Autonomous atomistic computations are excellent tools to accelerate the development of heterogeneous (electro-)catalysts. In this perspective, we critically review the achieved progress to accelerate high-throughput screening aimed at identifying promising catalyst materials via databases, workflow managers, and machine-learning techniques. Outstanding challenges are also discussed extensively: the modification and stability of catalyst surfaces under realistic reaction conditions is key for meaningful predictions. Furthermore, adequately accounting for solvent effects remains a topic of active research particularly relevant for biomass transformations and electrocatalysis. Finally, efficient, autonomous workflows for investigating active sites of amorphous catalysts remain underdeveloped. The computations can also be supplemented with autonomous laboratories, which allow the performance of sophisticated experiments driven by artificial intelligence-augmented design of experiments, reducing human-time investment for optimizing synthesis and reaction conditions as well as catalyst characterizations. The combination of autonomous computations and laboratories promise to power the dearly needed transition to a sustainable chemical industry. Workflow ; Throughput ; Computer science ; Computation ; Biochemical engineering ; Nanotechnology ; Process engineering ; Materials science ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Shiliang Zong | Guangzheng Zhou | Meng Li | Xuezhong Wang
Source: Particuology (article, 2022-07-16)
Field: Materials Science — Materials Chemistry

Deep learning-based on-line image analysis for continuous industrial crystallization processes Crystallization ; Nucleation ; Crystal (programming language) ; Convolutional neural network ; Artificial intelligence ; Biological system ; Deep learning ; Materials science ; Computer science ; Line (geometry) ; Process (computing) ; Image (mathematics) ; Breakage ; Segmentation ; Pattern recognition (psychology) ; Chemistry ; Mathematics Crystallization and Solubility Studies ; Mineral Processing and Grinding ; Image Processing Techniques and Applications
Authors: Xiyun Zhan | Yanjun Liu | Kun‐Lin Yang | Dan Luo
Source: Biosensors (review, 2022-07-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

State-of-the-Art Development in Liquid Crystal Biochemical Sensors As an emerging stimuli-responsive material, liquid crystal (LC) has attracted great attentions beyond display applications, especially in the area of biochemical sensors. Its high sensitivity and fast response to various biological or chemical analytes make it possible to fabricate a simple, real-time, label-free, and cost-effective LC-based detection platform. Advancements have been achieved in the development of LC-based sensors, both in fundamental research and practical applications. This paper briefly reviews the state-of-the-art research on LC sensors in the biochemical field, from basic properties of LC material to the detection mechanisms of LC sensors that are categorized into LC-solid, LC-aqueous, and LC droplet platforms. In addition, various analytes detected by LCs are presented as a proof of the application value, including metal ions, nucleic acids, proteins, glucose, and some toxic chemical substances. Furthermore, a machine-learning-assisted LC sensing platform is realized to provide a foundation for device intelligence and automatization. It is believed that a portable, convenient, and user-friendly LC-based biochemical sensing device will be achieved in the future. Analyte ; Nanotechnology ; Liquid crystal ; Computer science ; Materials science ; Biological fluids ; Chemistry ; Biochemical engineering ; Process engineering ; Chromatography ; Engineering ; Optoelectronics Liquid Crystal Research Advancements ; Photonic and Optical Devices ; Advanced Materials and Mechanics
Authors: Gerardo Campos-Villalobos | Giuliana Giunta | Susana Marín-Aguilar | Marjolein Dijkstra
Source: The Journal of Chemical Physics (article, 2022-06-22)
Field: Materials Science — Materials Chemistry

Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions Spherically symmetric atom-centered descriptors of atomic environments have been widely used for constructing potential or free energy surfaces of atomistic and colloidal systems and to characterize local structures using machine learning techniques. However, when particle shapes are non-spherical, as in the case of rods and ellipsoids, standard spherically symmetric structure functions alone produce imprecise descriptions of local environments. In order to account for the effects of orientation, we introduce two- and three-body orientation-dependent particle-centered descriptors for systems composed of rod-like particles. To demonstrate the suitability of the proposed functions, we use an efficient feature selection scheme and simple linear regression to construct coarse-grained many-body interaction potentials for computationally efficient simulations of model systems consisting of colloidal particles with an anisotropic shape: mixtures of colloidal rods and non-adsorbing polymer coils, hard rods enclosed by an elastic microgel shell, and ligand-stabilized nanorods. We validate the machine-learning (ML) effective many-body potentials based on orientation-dependent symmetry functions by using them in direct coexistence simulations to map out the phase behavior of colloidal rods and non-adsorbing polymer coils. We find good agreement with the results obtained from simulations of the true binary mixture, demonstrating that the effective interactions are well described by the orientation-dependent ML potentials. Rod ; Symmetry (geometry) ; Orientation (vector space) ; Particle (ecology) ; Anisotropy ; Materials science ; Ellipsoid ; Nanorod ; Colloid ; Chemical physics ; Circular symmetry ; Statistical physics ; Biological system ; Physics ; Classical mechanics ; Molecular physics ; Geometry ; Nanotechnology ; Chemistry ; Optics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yizhen Situ | Xueying Yuan | Xiangning Bai | Shuhua Li | Hong Liang | Xin Zhu | Bangfen Wang | Zhiwei Qiao
Source: Membranes (article, 2022-07-11)
Field: Chemistry — Inorganic Chemistry

Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO2 from Flue Gas To combat global warming, as an energy-saving technology, membrane separation can be applied to capture CO<sub>2</sub> from flue gas. Metal-organic frameworks (MOFs) with characteristics like high porosity have great potential as membrane materials for gas mixture separation. In this work, through a combination of grand canonical Monte Carlo and molecular dynamics simulations, the permeability of three gases (CO<sub>2</sub>, N<sub>2</sub>, and O<sub>2</sub>) was calculated and estimated in 6013 computation-ready experimental MOF membranes (CoRE-MOFMs). Then, the relationship between structural descriptors and permeance performance, and the importance of available permeance area to permeance performance of gas molecules with smaller kinetic diameters were found by univariate analysis. Furthermore, comparing the prediction accuracy of seven classification machine learning algorithms, XGBoost was selected to analyze the order of importance of six structural descriptors to permeance performance, through which the conclusion of the univariate analysis was demonstrated one more time. Finally, seven promising CoRE-MOFMs were selected, and their structural characteristics were analyzed. This work provides explicit directions and powerful guidelines to experimenters to accelerate the research on membrane separation for the purification of flue gas. Permeance ; Flue gas ; Gas separation ; Membrane ; Univariate ; Process engineering ; Computer science ; Materials science ; Chemistry ; Machine learning ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Zeolite Catalysis and Synthesis
Authors: Othman Al Musaimi | Oscar M. Mercado-Valenzo | Daryl R. Williams
Source: Journal of Separation Science (review, 2022-11-09)
Field: Chemistry — Spectroscopy

Prediction of peptides retention behavior in reversed‐phase liquid chromatography based on their hydrophobicity Hydrophobicity is an important physicochemical property of peptides and proteins. It is responsible for their conformational changes, stability, as well as various chemical intramolecular and intermolecular interactions. Enormous efforts have been invested to study the extent of hydrophobicity and how it could influence various biological processes, in addition to its crucial role in the separation and purification endeavor as well. Here, we have reviewed various studies that were carried out to determine the hydrophobicity starting from (i) simple amino acids solubility behavior, (ii) experimental approach that was undertaken in the reversed-phase liquid chromatography mode, and ending with (iii) some examples of more advanced computational and machine learning models. Chromatography ; Reversed-phase chromatography ; Chemistry ; High-performance liquid chromatography ; Hydrophilic interaction chromatography ; Phase (matter) ; Organic chemistry Analytical Chemistry and Chromatography ; Protein purification and stability
Authors: Duo Zhang | Hangrui Bi | Fu‐Zhi Dai | Wanrun Jiang | Linfeng Zhang | Handong Wang
Source: arXiv (Cornell University) (preprint, 2022-08-17)
Field: Materials Science — Materials Chemistry

DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation Machine learning assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation. With the accumulation of high-quality electronic structure data, a model that can be pretrained on all available data and finetuned on downstream tasks with a small additional effort would bring the field to a new stage. Here we propose DPA-1, a Deep Potential model with a novel attention mechanism, which is highly effective for representing the conformation and chemical spaces of atomic systems and learning the PES. We tested DPA-1 on a number of systems and observed superior performance compared with existing benchmarks. When pretrained on large-scale datasets containing 56 elements, DPA-1 can be successfully applied to various downstream tasks with a great improvement of sample efficiency. Surprisingly, for different elements, the learned type embedding parameters form a $spiral$ in the latent space and have a natural correspondence with their positions on the periodic table, showing interesting interpretability of the pretrained DPA-1 model. Interpretability ; Computer science ; Embedding ; Artificial intelligence ; Field (mathematics) ; Deep learning ; Chemical space ; Table (database) ; Machine learning ; Chemistry ; Data mining Machine Learning in Materials Science ; Topic Modeling ; Protein Structure and Dynamics
Authors: Mengchuang Zhang | Yuan Jing | Jian Zhang | Zhizhi Sheng | Yaqi Hou | Jiadai Xu | Baiyi Chen | Jing Liu | Miao Wang | Xu Hou
Source: Interdisciplinary materials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Performance prediction of magnetorheological fluid‐based liquid gating membrane by kriging machine learning method Abstract A smart liquid gating membrane is a responsive structural material as a pressure‐driven system that consists of solid membrane and dynamic liquid, responding to the external field. An accurate prediction of rheological and mechanical properties is important for the designs of liquid gating membranes for various applications. However, high predicted accuracy by the traditional sequential method requires a large amount of experimental data, which is not practical in some situations. To conquer these problems, artificial intelligence has promoted the rapid development of material science in recent years, bringing hope to solve these challenges. Here we propose a Kriging machine learning model with an active candidate region, which can be smartly updated by an expected improvement probability method to increase the local accuracy near the most sensitive search region, to predict the mechanical and rheological performance of liquid gating system with an active minimal size of experimental data. Besides this, this new machine learning model can instruct our experiments with optimal size. The methods are then verified by liquid gating membrane with magnetorheological fluids, which would be of wide interest for the design of potential liquid gating applications in drug release, microfluidic logic, dynamic fluid control, and beyond. Gating ; Magnetorheological fluid ; Membrane ; Microfluidics ; Computer science ; Rheology ; Materials science ; Artificial intelligence ; Control engineering ; Engineering ; Nanotechnology ; Chemistry Pickering emulsions and particle stabilization ; Advanced Sensor and Energy Harvesting Materials ; Rheology and Fluid Dynamics Studies
Authors: Ruirui Xie | Xiangfei Song | Huiting Chen | Pei‐Ru Lin | Siyun Guo | Zehong Zhuang | Yuying Chen | Wei Zhao | Peng Zhao | Hao Long | Jia Tao
Source: Analytical Chemistry (article, 2022-11-07)
Field: Chemistry — Inorganic Chemistry

Intelligent Clinical Lab for the Diagnosis of Post-Neurosurgical Meningitis Based on Machine-Learning-Aided Cerebrospinal Fluid Analysis Post-neurosurgical meningitis (PNM) often leads to serious consequences; unfortunately, the commonly used clinical diagnostic methods of PNM are time-consuming or have low specificity. To realize the accurate and convenient diagnosis of PNM, herein, we propose a comprehensive strategy for cerebrospinal fluid (CSF) analysis based on a machine-learning-aided cross-reactive sensing array. The sensing array involves three Eu<sup>3+</sup>-doped metal-organic frameworks (MOFs), which can generate specific fluorescence responding patterns after reacting with potential targets in CSF. Then, the responding pattern is used as learning data to train the machine learning algorithms. The discrimination confidence for artificial CSF containing different components of molecules, proteins, and cells is from 81.3 to 100%. Furthermore, the machine-learning-aided sensing array was applied in the analysis of CSF samples from post-neurosurgical patients. Only 25 μL of CSF samples was needed, and the samples could be robustly classified into "normal," "mild," or "severe" groups within 40 min. It is believed that the combination of machine learning algorithms with robust data processing capability and a lanthanide luminescent sensor array will provide a reliable alternative for more comprehensive, convenient, and rapid diagnosis of PNM. Cerebrospinal fluid ; Artificial intelligence ; Chemistry ; Sensor array ; Machine learning ; Pattern recognition (psychology) ; Computer science ; Pathology ; Medicine Metal-Organic Frameworks: Synthesis and Applications ; Head and Neck Surgical Oncology ; Acoustic Wave Resonator Technologies
Authors: Mavrik Zavarin | Elliot Chang | Haruko Wainwright | Nicholas Parham | Rahul Kaukuntla | Jadallah Zouabe | Amanda Deinhart | Victoria Genetti | Sam Shipman | Frank Bok | Vinzenz Brendler
Source: Environmental Science & Technology (article, 2022-02-01)
Field: Chemistry — Inorganic Chemistry

Community Data Mining Approach for Surface Complexation Database Development This paper presents a comprehensive data-to-model workflow, including a findable, accessible, interoperable, reusable (FAIR) community sorption database (newly developed LLNL Surface Complexation/Ion Exchange (L-SCIE) database) along with a data fitting workflow to efficiently optimize surface complexation reaction constants with multiple surface complexation model (SCM) constructs. This workflow serves as a universal framework to mine, compile, and analyze large numbers of published sorption data as well as to estimate reaction constants for parameterizing reactive transport models. The framework includes (1) data digitization from published papers, (2) data unification including unit conversions, and (3) data-model integration and reaction constant estimation using geochemical software PHREEQC coupled with the universal parameter estimation code PEST. We demonstrate our approach using an analysis of U(VI) sorption to quartz based on a first L-SCIE implementation, concluding that a multisite SCM construct with carbonate surface species yielded the best fit to community data. Surface complexation reaction constants extracted from this approach captured all available sorption data available in the literature and provided insight into previously published reaction constants and surface complexation model constructs. The L-SCIE sorption database presented herein allows for automating this approach across a wide range of metals and minerals and implementing novel machine learning approaches to reactive transport in the future. Sorption ; Computer science ; Workflow ; Database ; Chemistry ; Data mining ; Adsorption ; Physical chemistry Radioactive element chemistry and processing ; Geochemistry and Geologic Mapping ; Groundwater flow and contamination studies
Authors: Jie Zhang | Pei-Lin Xin | Xiaoyuan Wang | Hua‐Ying Chen | Da‐Wei Li
Source: The Journal of Physical Chemistry A (article, 2022-04-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning-Based Spectral Extraction for Improving the Performance of Surface-Enhanced Raman Spectroscopy Analysis on Multiplexed Identification and Quantitation Surface-enhanced Raman spectroscopy (SERS) has been recognized as a promising analytical technique for its capability of providing molecular fingerprint information and avoiding interference of water. Nevertheless, direct SERS detection of complicated samples without pretreatment to achieve the high-efficiency identification and quantitation in a multiplexed way is still a challenge. In this study, a novel spectral extraction neural network (SENN) model was proposed for synchronous SERS detection of each component in mixed solutions using a demonstration sample containing diquat dibromide (DDM), methyl viologen dichloride (MVD), and tetramethylthiuram disulfide (TMTD). A SERS spectra dataset including 3600 spectra of DDM, MVD, TMTD, and their mixtures was first constructed to train the SENN model. After the training step, the cosine similarity of the SENN model can achieve 0.999, 0.997, and 0.994 for DDM, MVD, and TMTD, respectively, which means that the spectra extracted from the mixture are highly consistent with those collected by the SERS experiment of the corresponding pure samples. Furthermore, a convolutional neural network model for quantitative analysis is combined with the SENN, which can simultaneously and rapidly realize the qualitative and quantitative SERS analysis of mixture solutions with lower than 8.8% relative standard deviation. The result demonstrates that the proposed strategy has great potential in improving SERS analysis in environmental monitoring, food safety, and so on. Raman spectroscopy ; Materials science ; Cosine similarity ; Extraction (chemistry) ; Biological system ; Surface-enhanced Raman spectroscopy ; Analytical Chemistry (journal) ; Spectroscopy ; Chromatography ; Computer science ; Pattern recognition (psychology) ; Chemistry ; Artificial intelligence ; Raman scattering ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Spectroscopy and Chemometric Analyses
Authors: Lenz Fiedler | Zhandos A. Moldabekov | Xuecheng Shao | Kaili Jiang | Tobias Dornheim | Michele Pavanello | Attila Cangi
Source: Physical Review Research (article, 2022-10-17)
Field: Materials Science — Materials Chemistry

Accelerating equilibration in first-principles molecular dynamics with orbital-free density functional theory We introduce a practical hybrid approach that combines orbital-free density functional theory (DFT) with Kohn-Sham DFT for speeding up first-principles molecular dynamics simulations. Equilibrated ionic configurations are generated using orbital-free DFT for subsequent Kohn-Sham DFT molecular dynamics. This leads to a massive reduction of the simulation time without any sacrifice in accuracy. We assess this finding across systems of different sizes and temperature, up to the warm dense matter regime. To that end, we use the cosine distance between the time series of radial distribution functions representing the ionic configurations. Likewise, we show that the equilibrated ionic configurations from this hybrid approach significantly enhance the accuracy of machine-learning models that replace Kohn-Sham DFT. Our hybrid scheme enables systematic first-principles simulations of warm dense matter that are otherwise hampered by the large numbers of atoms and the prevalent high temperatures. Moreover, our finding provides an additional motivation for developing kinetic and noninteracting free-energy functionals for orbital-free DFT. Density functional theory ; Molecular dynamics ; Orbital mechanics ; Dynamics (music) ; Orbital-free density functional theory ; Statistical physics ; Physics ; Classical mechanics ; Time-dependent density functional theory ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies