jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingzi Zhang | Yuelin Wang | Xuyan Zhou | Chengquan Zhong | Ke Zhang | Jiakai Liu | Kailong Hu | Xi Lin
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys Predictive materials design of high-performance alloy electrocatalysts is a grand challenge in hydrogen production <i>via</i> water electrolysis. The vast combinatorial space of element substitutions in alloy electrocatalysts offers a wealth of candidate materials, but presents a significant challenge in terms of experimental and computational exploration of all possible options. Recent scientific and technological developments in machine learning (ML) have offered a new opportunity to accelerate such electrocatalyst materials design. Herein, by incorporating both the electronic and structural properties of alloys, we are able to construct accurate and efficient ML models and predict high-performance alloy catalysts for the hydrogen evolution reaction (HER). We have identified the light gradient boosting (LGB) algorithm as the best-performed method, with an excellent coefficient of determination (<i>R</i><sup>2</sup>) value reaching 0.921 and the corresponding root-mean-square error (RMSE) being 0.224 eV. The average marginal contributions of alloy features towards Δ<i>G</i><sub>H*</sub> values are estimated to determine the importance of various features during the prediction processes. Our results indicate that both the electronic properties of constituent elements and the structural adsorption site features play the most critical roles in the Δ<i>G</i><sub>H*</sub> prediction. Furthermore, 84 potential alloys with |Δ<i>G</i><sub>H*</sub>| values less than 0.1 eV are successfully screened out of 2290 candidates selected from the Material Project (MP) database. It is reasonable to expect that the ML models with structural and electronic feature engineering developed in this work would provide new insights in future electrocatalyst developments for the HER and other heterogeneous reactions. Catalysis ; Alloy ; Feature (linguistics) ; Materials science ; Space (punctuation) ; Electronic structure ; Computer science ; Biological system ; Nanotechnology ; Artificial intelligence ; Computational chemistry ; Metallurgy ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: N. Kazeev | Abdalaziz Rashid Al-Maeeni | Ignat Romanov | Maxim Faleev | Ruslan Lukin | Alexander Tormasov | A. H. Castro Neto | Kostya S. Novoselov | Pengru Huang | A. Ustyuzhanin
Source: npj Computational Materials (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

Sparse representation for machine learning the properties of defects in 2D materials Abstract Two-dimensional materials offer a promising platform for the next generation of (opto-) electronic devices and other high technology applications. One of the most exciting characteristics of 2D crystals is the ability to tune their properties via controllable introduction of defects. However, the search space for such structures is enormous, and ab-initio computations prohibitively expensive. We propose a machine learning approach for rapid estimation of the properties of 2D material given the lattice structure and defect configuration. The method suggests a way to represent configuration of 2D materials with defects that allows a neural network to train quickly and accurately. We compare our methodology with the state-of-the-art approaches and demonstrate at least 3.7 times energy prediction error drop. Also, our approach is an order of magnitude more resource-efficient than its contenders both for the training and inference part. Computer science ; Inference ; Computation ; Lattice (music) ; Artificial neural network ; Representation (politics) ; Artificial intelligence ; Machine learning ; Computer engineering ; Algorithm ; Physics Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Yongxing Chen | Haining Ji | Mingying Lu | Bin Liu | Yong Zhao | Yangyong Ou | Yi Wang | Jundong Tao | Ting Zou | Yan Huang | Junlong Wang
Source: Ceramics International (article, 2023-07-06)
Field: Materials Science — Polymers and Plastics

Machine learning guided hydrothermal synthesis of thermochromic VO2 nanoparticles Materials science ; Hydrothermal synthesis ; Differential scanning calorimetry ; Monoclinic crystal system ; Phase transition ; Machine learning ; Vanadium dioxide ; Nanoparticle ; Artificial intelligence ; Chemical engineering ; Computer science ; Hydrothermal circulation ; Nanotechnology ; Thermodynamics ; Crystallography ; Crystal structure Transition Metal Oxide Nanomaterials ; Catalysis and Oxidation Reactions ; Gas Sensing Nanomaterials and Sensors
Authors: Tianle Yue | Jinlong He | Lei Tao | Ying Li
Source: Journal of Chemical Theory and Computation (article, 2023-06-20)
Field: Materials Science — Materials Chemistry

High-Throughput Screening and Prediction of High Modulus of Resilience Polymers Using Explainable Machine Learning The ability to store and release elastic strain energy, as well as mechanical strength, are crucial factors in both natural and man-made mechanical systems. The modulus of resilience (<i>R</i>) indicates a material's capacity to absorb and release elastic strain energy, with the yield strength (σ<sub>y</sub>) and Young's modulus (<i>E</i>) as <i>R</i> = σ<sub><i>y</i></sub><sup>2</sup>/(2<i>E</i>) for linear elastic solids. To improve the <i>R</i> in linear elastic solids, a high σ<sub>y</sub> and low <i>E</i> combination in materials is sought after. However, achieving this combination is a significant challenge as both properties typically increase together. To address this challenge, we propose a computational method to quickly identify polymers with a high modulus of resilience using machine learning (ML) and validate the predictions through high-fidelity molecular dynamics (MD) simulations. Our approach commences by training single-task ML models, multitask ML models, and Evidential Deep Learning models to forecast the mechanical properties of polymers based on experimentally reported values. Utilizing explainable ML models, we were able to determine the critical substructures that significantly impact the mechanical properties of polymers, such as <i>E</i> and σ<sub>y</sub>. This information can be utilized to create and develop new polymers with improved mechanical characteristics. Our single-task and multitask ML models can predict the properties of 12 854 real polymers and 8 million hypothetical polyimides and uncover 10 new real polymers and 10 hypothetical polyimides with exceptional modulus of resilience. The improved modulus of resilience of these novel polymers was validated through MD simulations. Our method efficiently speeds up the discovery of high-performing polymers using ML predictions and MD validation and can be applied to other polymer material discovery challenges, such as polymer membranes, dielectric polymers, and more. Resilience (materials science) ; Polymer ; Elastic modulus ; Materials science ; Modulus ; Computer science ; Task (project management) ; Composite material ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Thijs Stuyver | Connor W. Coley
Source: Chemistry - A European Journal (article, 2023-02-14)
Field: Chemistry — Organic Chemistry

Machine Learning‐Guided Computational Screening of New Candidate Reactions with High Bioorthogonal Click Potential Bioorthogonal click chemistry has become an indispensable part of the biochemist's toolbox. Despite the wide variety of applications that have been developed in recent years, only a limited number of bioorthogonal click reactions have been discovered so far, most of them based on (substituted) azides. In this work, we present a computational workflow to discover new candidate reactions with promising kinetic and thermodynamic properties for bioorthogonal click applications. Sampling only around 0.05 % of an overall search space of over 10,000,000 dipolar cycloadditions, we develop a machine learning model able to predict DFT-computed activation and reaction energies within ∼2-3 kcal/mol across the entire space. Applying this model to screen the full search space through iterative rounds of learning, we identify a broad pool of candidate reactions with rich structural diversity, which can be used as a starting point or source of inspiration for future experimental development of both azide-based and non-azide-based bioorthogonal click reactions. Bioorthogonal chemistry ; Click chemistry ; Computer science ; Toolbox ; Azide ; Chemical space ; Chemistry ; Combinatorial chemistry ; Drug discovery ; Programming language ; Organic chemistry ; Biochemistry Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Monoclonal and Polyclonal Antibodies Research
Authors: Ravi Kumar Kottala | Bharat Kumar Chigilipalli | Srinivasnaik Mukuloth | Ragavanantham Shanmugam | Venkata Charan Kantumuchu | Sirisha Bhadrakali Ainapurapu | Muralimohan Cheepu
Source: Energies (article, 2023-02-24)
Field: Materials Science — Materials Chemistry

Thermal Degradation Studies and Machine Learning Modelling of Nano-Enhanced Sugar Alcohol-Based Phase Change Materials for Medium Temperature Applications Thermogravimetric analysis (TGA) was utilised to compare the thermal stability of pure phase change material (D-mannitol) to that of nano-enhanced PCM (NEPCM) (i.e., PCM containing 0.5% and 1% multiwalled carbon nanotubes (MWCNT)). Using model-free kinetics techniques, the kinetics of pure PCM and NEPCM degradation were analysed. Three different kinetic models such as Kissinger-Akahira-Sunose (KAS), the Flynn-Wall-Ozawa (FWO), and the Starink were applied to assess the activation energies of the pure and nano-enhanced PCM samples. Activation energies for pure PCM using the Ozawa, KAS, and Starink methods ranged from 71.10–77.77, 79.36–66.87, and 66.53–72.52 kJ/mol, respectively. NEPCM’s (1% MWCNT) activation energies ranged from 76.59–59.11, 71.52–52.28, and 72.15–53.07 kJ/mol. Models of machine learning were utilised to predict the degradation of NEPCM samples; these included linear regression, support vector regression, random forests, gaussian process regression, and artificial neural network models. The mass loss of the sample functioned as the output parameter, while the addition of nanoparticles weight fraction, the heating rate, and the temperature functioned as the input parameters. Experiment-based TGA data can be accurately predicted using the created machine learning models. Activation energy ; Materials science ; Thermogravimetric analysis ; Mass fraction ; Kinetics ; Artificial neural network ; Weight change ; Degradation (telecommunications) ; Chemical engineering ; Chemistry ; Machine learning ; Composite material ; Computer science ; Organic chemistry ; Physics Thermal and Kinetic Analysis ; Phase Change Materials Research ; Thermochemical Biomass Conversion Processes
Authors: Filippo De Angelis
Source: ACS Energy Letters (article, 2023-02-10)
Field: Materials Science — Materials Chemistry

The Impact of Machine Learning in Energy Materials Research: The Case of Halide Perovskites ADVERTISEMENT RETURN TO ISSUEEditorialNEXTThe Impact of Machine Learning in Energy Materials Research: The Case of Halide PerovskitesFilippo De Angelis*Filippo De AngelisUniversity of Perugia, 06123 Perugia, Italy*[email protected] More by Filippo De Angelishttps://orcid.org/0000-0003-3833-1975Cite this: ACS Energy Lett. 2023, 8, 2, 1270–1272Publication Date (Web):February 10, 2023Publication History Received24 January 2023Accepted26 January 2023Published online10 February 2023Published inissue 10 February 2023https://pubs.acs.org/doi/10.1021/acsenergylett.3c00182https://doi.org/10.1021/acsenergylett.3c00182editorialACS PublicationsCopyright © 2023 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 Views2617Altmetric-Citations2LEARN 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 (2 MB) Get e-AlertscloseSUBJECTS:Energy,Manufacturing,Materials,Perovskites,Solar cells Get e-Alerts Citation ; Altmetrics ; Halide ; Energy (signal processing) ; Computer science ; Social media ; World Wide Web ; Information retrieval ; Library science ; Physics ; Chemistry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advancements in Solid Oxide Fuel Cells
Authors: Naoki Noto | Akira Yada | Takeshi Yanai | Susumu Saito
Source: Angewandte Chemie International Edition (article, 2023-01-16)
Field: Chemistry — Organic Chemistry

Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols Catalytic systems using a small amount of organic photosensitizer for the activation of an inorganic (on-demand ligand-free) nickel(II) salt represent a cost-effective method for cross-coupling reactions, while C(sp<sup>2</sup> )-O bond formation remains less developed. Herein, we report a strategy for the synthesis of phenols with a nickel(II) salt and an organic photosensitizer, which was identified via an investigation into the catalytic activity of 60 organic photosensitizers consisting of various electron donor and acceptor moieties. To examine the effect of multiple intractable parameters on the catalytic activity of photosensitizers, machine-learning (ML) models were developed, wherein we embedded descriptors representing their physical and structural properties, which were obtained from DFT calculations and RDKit, respectively. The study clarified that integrating both DFT- and RDKit-derived descriptors in ML models balances higher "precision" and "recall" across a wide range of search space relative to using only one of the two descriptor sets. Photosensitizer ; Catalysis ; Chemistry ; Nickel ; Salt (chemistry) ; Ligand (biochemistry) ; Phenols ; Acceptor ; Combinatorial chemistry ; Photochemistry ; Organic chemistry Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques ; Polyoxometalates: Synthesis and Applications
Authors: Idris Temitope Bello | Daqin Guan | Na Yu | Zheng Li | Yufei Song | Xi Chen | Siyuan Zhao | Qijiao He | Zongping Shao | Meng Ni
Source: Chemical Engineering Journal (article, 2023-11-04)
Field: Materials Science — Materials Chemistry

Revolutionizing material design for protonic ceramic fuel cells: Bridging the limitations of conventional experimental screening and machine learning methods Bridging (networking) ; Ceramic ; Computer science ; Cathode ; Fuel cells ; Process (computing) ; Process engineering ; Materials science ; Chemical engineering ; Engineering ; Electrical engineering Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Alireza Shafizadeh | Hossein Shahbeik | Mohammad Hossein Nadian | Vijai Kumar Gupta | Abdul‐Sattar Nizami | Su Shiung Lam | Wanxi Peng | Junting Pan | Meisam Tabatabaei | Mortaza Aghbashlo
Source: Journal of Cleaner Production (article, 2023-05-15)
Field: Materials Science — Materials Chemistry

Turning hazardous volatile matter compounds into fuel by catalytic steam reforming: An evolutionary machine learning approach Steam reforming ; Catalysis ; Process engineering ; Computer science ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Zhi Huang | Yu Jiang | Wei He | Jie Yu | Siwei Deng | Chun Yang | Weiwei Zhu | Xiao Shao
Source: Journal of Hazardous Materials (article, 2023-12-24)
Field: Materials Science — Materials Chemistry

AI-enhanced chemical paradigm: From molecular graphs to accurate prediction and mechanism Molecular graph ; Quantitative structure–activity relationship ; Chemical space ; Artificial intelligence ; Molecular descriptor ; Computer science ; Artificial neural network ; Intramolecular force ; Bayesian network ; Biological system ; Graph ; Machine learning ; Chemistry ; Theoretical computer science ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Roshan Patel | Sophia Colmenares | Michael A. Webb
Source: ACS Polymers Au (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning Single-chain nanoparticles (SCNPs) are intriguing materials inspired by proteins that consist of a single precursor polymer chain that has collapsed into a stable structure. In many prospective applications, such as catalysis, the utility of a single-chain nanoparticle will intricately depend on the formation of a mostly specific structure or morphology. However, it is not generally well understood how to reliably control the morphology of single-chain nanoparticles. To address this knowledge gap, we simulate the formation of 7680 distinct single-chain nanoparticles from precursor chains that span a wide range of, in principle, tunable patterning characteristics of cross-linking moieties. Using a combination of molecular simulation and machine learning analyses, we show how the overall fraction of functionalization and blockiness of cross-linking moieties biases the formation of certain local and global morphological characteristics. Importantly, we illustrate and quantify the dispersity of morphologies that arise due to the stochastic nature of collapse from a well-defined sequence as well as from the ensemble of sequences that correspond to a given specification of precursor parameters. Moreover, we also examine the efficacy of precise sequence control in achieving morphological outcomes in different regimes of precursor parameters. Overall, this work critically assesses how precursor chains might be feasibly tailored to achieve given SCNP morphologies and provides a platform to pursue future sequence-based design. Dispersity ; Nanoparticle ; Single chain ; Sequence (biology) ; Nanotechnology ; Chain (unit) ; Polymer ; Materials science ; Computer science ; Morphology (biology) ; Biological system ; Chemistry ; Polymer chemistry ; Physics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Pickering emulsions and particle stabilization
Authors: Lixia Liu | Shenghu Zhou | Yu Deng
Source: ACS Catalysis (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Rational Design of the Substrate Tunnel of β-Ketothiolase Reveals a Local Cationic Domain Modulated Rule that Improves the Efficiency of Claisen Condensation Formation of carbon–carbon bonds via Claisen condensation catalyzed by thiolase generates a diverse range of carbon frameworks in biosynthetic chemistry. During catalysis, the substrate tunnel of thiolases plays an important role in the binding and condensation of substrates, directly affecting enzyme activity. Therefore, rational engineering of the substrate tunnel is a promising approach for enhancing enzyme performance. However, the lack of a general engineering rule hinders rational engineering of the substrate tunnel. In this study, we reported the crystal structure of Tfu_0875, a thermostable β-ketothiolase from Thermobifida fusca belonging to the thiolase superfamily. The enzyme had a Cys–His–Cys catalytic triad and a narrow substrate tunnel mainly built from the cationic domain, the adenine-binding pocket, and the pantetheine loop. Focusing on the substrate tunnel, mutant candidates with increased kcat were predicted by the deep learning approach. The best mutation in the cationic domain (L163H) exhibited a 313% increase in enzyme activity compared with the wild-type enzyme. Molecular dynamics simulations revealed a local cationic domain modulated rule (LCDMR): mutating the nonconserved residue at the junction between the loop region and the α5 region in the sandwich topology to histidine significantly improved enzyme activity by increasing the reaction space and interaction with the substrate or unstable intermediate. The LCDMR has general applicability in rational engineering of the substrate tunnel to enhance the enzyme activity of other β-ketothiolases. Thiolase ; Rational design ; Claisen condensation ; Substrate (aquarium) ; Enzyme kinetics ; Stereochemistry ; Chemistry ; Cationic polymerization ; Active site ; Enzyme ; Catalysis ; Materials science ; Biochemistry ; Nanotechnology ; Organic chemistry ; Biology Enzyme Structure and Function ; Biochemical and Molecular Research ; Enzyme Production and Characterization
Authors: Yu Shu | Ting Li | Naihua Miao | Jian Gou | Xiao‐Chun Huang | Zhou Cui | Rui Xiong | Cuilian Wen | Jian Zhou | Baisheng Sa | Zhimei Sun
Source: Nanoscale Horizons (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Contact engineering for 2D Janus MoSSe/metal junctions The flourish of two-dimensional (2D) materials provides a versatile platform for building high-performance electronic devices in the atomic thickness regime. However, the presence of the high Schottky barrier at the interface between the metal electrode and the 2D semiconductors, which dominates the injection and transport efficiency of carriers, always limits their practical applications. Herein, we show that the Schottky barrier can be controllably lifted in the heterostructure consisting of Janus MoSSe and 2D vdW metals by different means. Based on density functional theory calculations and machine learning modelings, we studied the electrical contact between semiconducting monolayer MoSSe and various metallic 2D materials, where a crossover from Schottky to Ohmic/quasi-Ohmic contact is realized. We demonstrated that the band alignment at the interface of the investigated metal-semiconductor junctions (MSJs) deviates from the ideal Schottky-Mott limit because of the Fermi-level pinning effects induced by the interface dipoles. Besides, the effect of the thickness and applied biaxial strain of MoSSe on the electronic structure of the junctions are explored and found to be powerful tuning knobs for electrical contact engineering. It is highlighted that using the sure-independence-screening-and-sparsifying-operator machine learning method, a general descriptor <i>W</i><sub>M</sub><sup>3</sup>/exp(<i>D</i><sub>int</sub>) was developed, which enables the prediction of the Schottky barrier height for different MoSSe-based MSJ. These results provide valuable theoretical guidance for realizing ideal Ohmic contacts in electronic devices based on the Janus MoSSe semiconductors. Janus ; Materials science ; Engineering ; Nanotechnology 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques