jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Y.S. Wudil | A. Imam | M.A. Gondal | U.F. Ahmad | Mohammed A. Al‐Osta
Source: Sensors and Actuators A Physical (article, 2023-01-25)
Field: Materials Science — Materials Chemistry

Application of machine learning regressors in estimating the thermoelectric performance of Bi2Te3-based materials Mean squared error ; Thermoelectric effect ; Linear regression ; Figure of merit ; Support vector machine ; Seebeck coefficient ; Machine learning ; Materials science ; Regression analysis ; Thermal conductivity ; Mathematics ; Artificial intelligence ; Algorithm ; Computer science ; Statistics ; Thermodynamics ; Physics ; Optoelectronics ; Composite material Advanced Thermoelectric Materials and Devices ; Neural Networks and Applications ; Machine Learning in Materials Science
Authors: Samuel G. Espley | Elliot H. E. Farrar | David Buttar | Simone Tomasi | Matthew N. Grayson
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach Transfer learning (TL) is used to adapt existing neural networks to provide reaction barrier predictions for different reaction classes (horizontal TL) at higher levels of theory (diagonal TL) with tens of datapoints. Diagonal ; Transfer of learning ; Artificial neural network ; Transfer (computing) ; Computer science ; Artificial intelligence ; Biological system ; Physics ; Mathematics ; Geometry ; Parallel computing ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Jingwen Sun | Chong Liu
Source: Current Opinion in Electrochemistry (article, 2023-04-21)
Field: Chemistry — Electrochemistry

What and how can machine learning help to decipher mechanisms in molecular electrochemistry? DECIPHER ; Ambiguity ; Software deployment ; Computer science ; Perspective (graphical) ; Artificial intelligence ; Data science ; Nanotechnology ; Machine learning ; Bioinformatics ; Software engineering ; Materials science ; Programming language Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Yihuan Zhao | Kuan Chen | Lei Zhu | Qiang Huang
Source: Dyes and Pigments (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Data-driven machine learning models for quick prediction of the Stokes shift of organic fluorescent materials Stokes shift ; Fluorescence ; Mean squared error ; Biological system ; Correlation coefficient ; Computer science ; Materials science ; Artificial intelligence ; Chemistry ; Mathematics ; Machine learning ; Physics ; Optics ; Statistics Machine Learning in Materials Science ; Photochemistry and Electron Transfer Studies ; Computational Drug Discovery Methods
Authors: Bahareh Esteki | Mahmood Masoomi | Mohammad Moosazadeh | ChangKyoo Yoo
Source: Langmuir (article, 2023-03-31)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Janus/Core–Shell Morphology in Polymer Particles: A Machine-Learning Approach The majority of research on Janus particles prepared by solvent evaporation-induced phase separation technique uses models based on interfacial tension or free energy to predict Janus/core-shell morphology. Data-driven predictions, in contrast, utilize multiple samples to identify patterns and outliers. Using machine-learning algorithms and explainable artificial intelligence (XAI) analysis, we developed a model based on a 200-instance data set to predict particle morphology. As model features, simplified molecular input line entry system syntax identifies explanatory variables, including cohesive energy density, molar volume, the Flory-Huggins interaction parameter of polymers, and the solvent solubility parameter. Our most accurate ensemble classifiers predict morphology with an accuracy of 90%. In addition, we employ innovative XAI tools to interpret system behavior, suggesting phase-separated morphology to be most affected by solvent solubility, polymer cohesive energy difference, and blend composition. While polymers with cohesive energy densities above a certain threshold favor the core-shell structure, systems with weak intermolecular interactions favor the Janus structure. The correlation between molar volume and morphology suggests that increasing the size of polymer repeating units favors Janus particles. Additionally, the Janus structure is preferred when the Flory-Huggins interaction parameter exceeds 0.4. XAI analysis introduces feature values that generate the thermodynamically low driving force of phase separation, resulting in kinetically stable morphologies as opposed to thermodynamically stable ones. The Shapley plots of this study also reveal novel methods for creating Janus or core-shell particles based on solvent evaporation-induced phase separation by selecting feature values that strongly favor a given morphology. Janus ; Hildebrand solubility parameter ; Phase (matter) ; Morphology (biology) ; Polymer ; Solubility ; Chemical physics ; Materials science ; Thermodynamics ; Chemistry ; Statistical physics ; Nanotechnology ; Physics ; Physical chemistry ; Organic chemistry Pickering emulsions and particle stabilization ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Polymer Synthesis and Characterization
Authors: Keita Kobayashi | Masahiko Okumura | Hiroki Nakamura | Mitsuhiro Itakura | Masahiko Machida | Shingo Urata | Kentaro Suzuya
Source: Scientific Reports (article, 2023-11-16)
Field: Materials Science — Ceramics and Composites

Machine learning molecular dynamics reveals the structural origin of the first sharp diffraction peak in high-density silica glasses The first sharp diffraction peak (FSDP) in the total structure factor has long been regarded as a characteristic feature of medium-range order (MRO) in amorphous materials with a polyhedron network, and its underlying structural origin is a subject of ongoing debate. In this study, we utilized machine learning molecular dynamics (MLMD) simulations to explore the origin of FSDP in two typical high-density silica glasses: silica glass under pressure and permanently densified glass. Our MLMD simulations accurately reproduce the structural properties of high-density silica glasses observed in experiments, including changes in the FSDP intensity depending on the compression temperature. By analyzing the simulated silica glass structures, we uncover the structural origin responsible for the changes in the MRO at high density in terms of the periodicity between the ring centers and the shape of the rings. The reduction or enhancement of MRO in the high-density silica glasses can be attributed to how the rings deform under compression. Materials science ; Diffraction ; Silica glass ; Molecular dynamics ; Amorphous solid ; Structure factor ; Amorphous silica ; Chemical physics ; Polyhedron ; Crystallography ; Composite material ; Optics ; Chemistry ; Physics ; Chemical engineering ; Computational chemistry ; Geometry ; Mathematics Glass properties and applications ; Material Dynamics and Properties ; Phase-change materials and chalcogenides
Authors: Kevin Maik Jablonka | Philippe Schwaller | Andres Ortega‐Guerrero | Berend Smit
Source: ChemRxiv (preprint, 2023-10-17)
Field: Materials Science — Materials Chemistry

Leveraging Large Language Models for Predictive Chemistry Machine learning has revolutionized many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine-learning approaches that incorporate chemical knowledge for each application and, therefore, require much expertise to develop. Here, we show that large language models trained on vast amounts of text extracted from the internet can easily be adapted to solve various tasks in chemistry and materials science by fine-tuning them to answer chemical questions in natural language with the correct answer. We compared this approach with dedicated machine-learning models for many applications spanning properties of molecules and materials to the yield of chemical reactions. Surprisingly, this approach performs comparable to or even outperforms the conventional techniques---particularly in the low data limit. In addition, we can perform inverse design successfully by simply inverting the questions. The high performance, especially for small data sets, combined with the ease of use, can fundamentally impact how we leverage machine learning in the chemical and material sciences. Next to a literature search, querying a foundation model might become a routine way to bootstrap a project by leveraging the collective knowledge encoded in these foundation models or to provide a baseline for predictive tasks. Computer science ; Leverage (statistics) ; Artificial intelligence ; Machine learning ; Chemical space ; Data science ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Boran Ma | Nicholas J. Finan | David Jany | Michael E. Deagen | Linda S. Schadler | L. Catherine Brinson
Source: Macromolecules (article, 2023-05-23)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Understanding of Polymer Nanocomposites Composition–Property Relationship: A Case Study of NanoMine Database The NanoMine database, one of two nodes in the MaterialsMine database, is a new materials data resource that collects annotated data on polymer nanocomposites (PNCs). This work showcases the potential of NanoMine and other materials data resources to assist fundamental materials understanding and therefore rational materials design. This specific case study is built around studying the relationship between the change in the glass transition temperature <i>T</i><sub>g</sub> (<i>ΔT</i><sub>g</sub>) and key descriptors of the nanofillers and the polymer matrix in PNCs. We sifted through data from over 2000 experimental samples curated into NanoMine, trained a decision tree classifier to predict the sign of PNC <i>ΔT</i><sub>g</sub>, and built a multiple power regression metamodel to predict <i>ΔT</i><sub>g</sub>. The successful model used key descriptors including composition, nanoparticle volume fraction, and interfacial surface energy. The results demonstrate the power of using aggregated materials data to gain insight and predictive capability. Further analysis points to the importance of additional analysis of parameters from processing methodologies and continuously adding curated data sets to increase the sample pool size. Computer science ; Decision tree ; Database ; Materials science ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Ryan Jacobs | Priyam Patki | Matthew Lynch | Steven Chen | Dane Morgan | Kevin G. Field
Source: Scientific Reports (article, 2023-03-30)
Field: Materials Science — Materials Chemistry

Materials swelling revealed through automated semantic segmentation of cavities in electron microscopy images Accurately quantifying swelling of alloys that have undergone irradiation is essential for understanding alloy performance in a nuclear reactor and critical for the safe and reliable operation of reactor facilities. However, typical practice is for radiation-induced defects in electron microscopy images of alloys to be manually quantified by domain-expert researchers. Here, we employ an end-to-end deep learning approach using the Mask Regional Convolutional Neural Network (Mask R-CNN) model to detect and quantify nanoscale cavities in irradiated alloys. We have assembled a database of labeled cavity images which includes 400 images, > 34 k discrete cavities, and numerous alloy compositions and irradiation conditions. We have evaluated both statistical (precision, recall, and F1 scores) and materials property-centric (cavity size, density, and swelling) metrics of model performance, and performed targeted analysis of materials swelling assessments. We find our model gives assessments of material swelling with an average (standard deviation) swelling mean absolute error based on random leave-out cross-validation of 0.30 (0.03) percent swelling. This result demonstrates our approach can accurately provide swelling metrics on a per-image and per-condition basis, which can provide helpful insight into material design (e.g., alloy refinement) and impact of service conditions (e.g., temperature, irradiation dose) on swelling. Finally, we find there are cases of test images with poor statistical metrics, but small errors in swelling, pointing to the need for moving beyond traditional classification-based metrics to evaluate object detection models in the context of materials domain applications. Swelling ; Computer science ; Materials science ; Context (archaeology) ; Convolutional neural network ; Irradiation ; Standard deviation ; Artificial intelligence ; Composite material ; Statistics ; Physics ; Mathematics ; Geology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis
Authors: Dayong Wang | Haifeng Lv | Yangyang Wan | Xiaojun Wu | Jinlong Yang
Source: The Journal of Physical Chemistry Letters (article, 2023-07-21)
Field: Materials Science — Materials Chemistry

Band-Edge Prediction of 2D Covalent Organic Frameworks from Molecular Precursor via Machine Learning The band-edge positions of two-dimensional (2D) covalent organic frameworks (COFs) play a crucial role in their applications in photocatalysts and nanoelectronics. However, massive amounts of 2D COFs with targeted band-edge positions from high-level first-principles calculations based on their composition are time-consuming due to the diversity and complexity of unit cell structures. Here, we report a strategy to predict the band-edge positions of 2D COFs by combining first-principles calculations with machine learning (ML). The root-mean-square error (RMSE) of the predicted valence band maximum (VBM) and conduction band minimum (CBM) between ML prediction and first-principles calculated values at the Perdew–Burke–Ernzerhof (PBE) level are 0.229 and 0.247 eV in test data set, respectively. In addition, a linear relationship is established between the PBE results and the HSE06 results with RMSE values of 0.089 and 0.042 eV for VBMs and CBMs in the test data set. Finally, a workflow is developed to determine the band-edge positions of the 2D COFs. Enhanced Data Rates for GSM Evolution ; Mean squared error ; Test set ; Valence (chemistry) ; Valence band ; Band gap ; Workflow ; Nanoelectronics ; Materials science ; Computer science ; Nanotechnology ; Mathematics ; Artificial intelligence ; Physics ; Optoelectronics ; Quantum mechanics ; Statistics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Yannick Ureel | Florence H. Vermeire | Maarten K. Sabbe | Kevin M. Van Geem
Source: Chemical Engineering Journal (article, 2023-07-17)
Field: Materials Science — Materials Chemistry

Beyond group additivity: Transfer learning for molecular thermochemistry prediction Additive function ; Thermochemistry ; Chemistry ; Delocalized electron ; Transfer of learning ; Steric effects ; Group (periodic table) ; Machine learning ; Artificial intelligence ; Computer science ; Organic chemistry ; Mathematics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Jianping Xiao | Yang Li | Shuqun Wang
Source: Computational Materials Science (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Graph isomorphism network for materials property prediction along with explainability analysis Computer science ; Discriminative model ; Embedding ; Suite ; Visualization ; Graph ; Graph property ; Theoretical computer science ; Graph isomorphism ; Data mining ; Artificial intelligence ; Machine learning ; Algorithm ; Line graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Julian Götz | Moritz K. Jackl | Chalupat Jindakun | Alexander N. Marziale | Jérôme André | Daniel J. Gosling | Clayton Springer | Marco Palmieri | Marcel Reck | Alexandre Luneau | Cara E. Brocklehurst | Jeffrey W. Bode
Source: Science Advances (article, 2023-10-27)
Field: Chemistry — Organic Chemistry

High-throughput synthesis provides data for predicting molecular properties and reaction success The generation of attractive scaffolds for drug discovery efforts requires the expeditious synthesis of diverse analogues from readily available building blocks. This endeavor necessitates a trade-off between diversity and ease of access and is further complicated by uncertainty about the synthesizability and pharmacokinetic properties of the resulting compounds. Here, we document a platform that leverages photocatalytic N-heterocycle synthesis, high-throughput experimentation, automated purification, and physicochemical assays on 1152 discrete reactions. Together, the data generated allow rational predictions of the synthesizability of stereochemically diverse C-substituted N-saturated heterocycles with deep learning and reveal unexpected trends on the relationship between structure and properties. This study exemplifies how organic chemists can exploit state-of-the-art technologies to markedly increase throughput and confidence in the preparation of drug-like molecules. Throughput ; Computer science ; Exploit ; Drug discovery ; Combinatorial chemistry ; Biochemical engineering ; Nanotechnology ; Data science ; Chemistry ; Materials science ; Engineering ; Telecommunications Radical Photochemical Reactions ; Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Rui Ding | Xuebin Wang | Aidong Tan | Jia Li | Jianguo Liu
Source: ACS Catalysis (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

Unlocking New Insights for Electrocatalyst Design: A Unique Data Science Workflow Leveraging Internet-Sourced Big Data In the past few decades, numerous electrocatalyst design studies have been reported. Although machine learning (ML) has recently emerged as a more efficient alternative to traditional trial-and-error methods, the cost of preparing training data remains high. Inspired by the success of models like ChatGPT, which learns from a vast corpus of text data collected from the internet, we developed a data science workflow initiated by collecting datasets via a highly automated web crawler. We trained artificial neural network models with acceptable accuracy in predicting electrocatalytic performances and used black-box interpretation methods to mine universal material design knowledge, verifying model reliabilities with data collected from as many as 5277 publications. Thoughtfully, we introduced transfer learning (TL) to address the data scarcity issue for electrocatalysts in neutral electrolytes, with fewer available publications. TL could provide reliable optimization advice even in unknown areas, with knowledge transferred from similar fields. This study examined the patterns of numerous previous electrocatalysts from a data science perspective and proposed a universal ML paradigm to assist in the design of unique materials based on transferable big scientific data. Workflow ; Computer science ; Big data ; Data science ; The Internet ; Artificial intelligence ; World Wide Web ; Data mining ; Database Machine Learning in Materials Science ; Topic Modeling ; Machine Learning and ELM
Authors: Shivanshu Shekhar | Chandra Chowdhury
Source: Results in Surfaces and Interfaces (article, 2023-11-30)
Field: Chemistry — Inorganic Chemistry

Prediction of hydrogen storage in metal-organic frameworks: A neural network based approach Gas capture, sensing, and storage systems are all within the capabilities of metal–organic frameworks (MOFs). It is common practise to choose the MOF with the best adsorption property from a large database before running an adsorption calculation. High-throughput computational research is sometimes hampered by the expense of computing thermodynamic values, slowing the progress of MOFs for separations and storage applications. When trying to predict material properties, machine learning has recently emerged as a possible alternative to more conventional methods like experiments and simulations. The H2 capacities of 918,734 MOFs drawn from 19 databases were recently predicted using ML by Ahmed and Siegel (2021). Several ML methods were utilised, and the extremely randomised tree (ERT) model emerged as the most accurate predictor of hydrogen delivery capacity in terms of both gravimetric and volumetric quantities. Interestingly we have used deep learning model (Feed-forward neural network) as well as ERT model for the prediction of H2 deliverable capacities of a huge number of MOFs developed from the previous studies and got till date best results for predictions. To verify our model’s efficacy, we also performed Grand Canonical Monte Carlo (GCMC) simulations. We show our method by forecasting the hydrogen storage capacity of MOFs during a temperature and pressure swing from 100bar/77K to 5bar/160K. Hydrogen storage ; Metal-organic framework ; Artificial neural network ; Computer science ; Gravimetric analysis ; Monte Carlo method ; Adsorption ; Process engineering ; Machine learning ; Artificial intelligence ; Hydrogen ; Chemistry ; Engineering ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yulan Han | Jiayan Xu | Wenbo Xie | Zhuozheng Wang | P. Hu
Source: ACS Catalysis (article, 2023-11-08)
Field: Materials Science — Materials Chemistry

Unravelling the Impact of Metal Dopants and Oxygen Vacancies on Syngas Conversion over Oxides: A Machine Learning-Accelerated Study of CO Activation on Cr-Doped ZnO Surfaces As a critical component of the OX-ZEO composite catalysts toward syngas conversion, the Cr-doped ZnO ternary system can be considered as a model system for understanding oxide catalysts. However, due to the complexity of its structures, traditional approaches, both experimental and theoretical, encounter significant challenges. Herein, we employ machine learning-accelerated methods, including grand canonical Monte Carlo and genetic algorithm, to explore the ZnO(1010) surface with various Cr and oxygen vacancy (OV) concentrations. Stable surfaces with varied Cr and OV concentrations were then systematically investigated to examine their influence on the CO activation via density functional theory calculations. We observe that Cr tends to preferentially appear on the surface of ZnO(1010) rather than in its interior regions and Cr-doped structures incline to form rectangular islands along the [0001] direction at high Cr and OV concentrations. Additionally, detailed calculations of CO reactivity unveil an inverse relationship between the reaction barrier (<i>E</i><sub>a</sub>) for C-O bond dissociation and the Cr and OV concentrations, and a linear relationship is observed between OV formation energy and <i>E</i><sub>a</sub> for CO activation. Further analyses indicate that the C-O bond dissociation is much more favored when the adjacent OVs are geometrically aligned in the [1210] direction, and Cr is doped around the reactive sites. These findings provide a deeper insight into CO activation over the Cr-doped ZnO surface and offer valuable guidance for the rational design of effective catalysts for syngas conversion. Syngas ; Dopant ; Catalysis ; Dissociation (chemistry) ; Density functional theory ; Oxide ; Doping ; Ternary operation ; Oxygen ; Materials science ; Activation energy ; Physical chemistry ; Chemistry ; Chemical engineering ; Computational chemistry ; Metallurgy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts