jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pekka Peljo | Gabriel González | Eduardo Martínez González | Qiujun Li | Jenna Hannonen | Ulriika Mattinen | Andrea Hamza | F. Németh | Ádám Madarász | Imre Pápai | Anton A. Nechaev | Petri M. Pihko
Source: ECS Meeting Abstracts (article, 2023-12-22)
Field: Chemistry — Electrochemistry

High-Throughput Screening of Molecules for Flow Batteries Development of next generation stationary energy storage technologies is of crucial importance for increasing the share of the intermitted renewable electricity generation in the world. Traditional trial and error based approach for discovery of novel materials is very slow, so we have been thinking of how to accelerate the process. Some crucial parameters such as redox potentials can be readily evaluated with computational tools, such as those developed in our CompBat project [1], [2], but other parameters such as stability are more difficult to be evaluated. In this work we will demonstrate our web-based application for evaluating redox potentials of molecules from the SMILES structure, illustrating very impressive or problematic performance depending on the training data used for the model. For the question of stability, we believe that sufficient quantity of experimental data is required for training of specific machine learning models for stability prediction. For this purpose, we will illustrate our experimental approach for high-throughput electrochemistry, to evaluate the stability of molecules in the time-scale of voltammetry. Moreover, we will discuss the order of magnitude of the kinetics of the decomposition reactions that can be evaluated by voltammetry, and illustrate that voltammetry is a good tool for roughly separating more stable species from the less stable, but other techniques are required for evaluating stability in the time scale of battery operation. References https://compbat.eu/ A. Hamza, F.B. Németh, Á. Madarász, A. Nechaev, P. Pihko, P. Peljo, I. Pápai, N-alkylated pyridoxal derivatives as negative electrolyte materials for aqueous organic flow batteries: Computational screening, 10.26434/chemrxiv-2023-bzc6w Throughput ; Flow (mathematics) ; High-throughput screening ; Computer science ; Nanotechnology ; Chemistry ; Materials science ; Mechanics ; Physics ; Telecommunications ; Biochemistry ; Wireless Electrochemical Analysis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Electrocatalysts for Energy Conversion
Authors: Mukesh K. Choudhary | Amal Raj V | Gowri Sankar S | P. Ravindran
Source: arXiv (Cornell University) (preprint, 2023-09-14)
Field: Materials Science — Materials Chemistry

Composition and Structure Based GGA Bandgap Prediction Using Machine Learning Approach This study focuses on developing precise machine learning (ML) regression models for predicting energy bandgap values based on chemical compositions and crystal structures. The primary aim is to match the accuracy of predictions derived from GGA-PBE calculations and validate them through density functional theory (DFT)-based band structure calculations. We assessed eight standalone ML regression models, including AdaBoost, Bagging, CatBoost, LGBM, RF, DT, GB, and XGB. These models were analyzed for their ability to predict GGA-PBE bandgap values across diverse material structures and compositions, using a dataset containing bandgap values for 106,113 compounds. Additionally, we constructed four ensemble models using the stacking method and seven using the bagging method. These ensemble models incorporated RidgeCV and LassoCV to explore if ensemble techniques could enhance prediction accuracy. The dataset was divided into subsets of varying sizes: 10,000, 25,000, 50,000, and 100,000 entries. We determined feature importance through permutation techniques and established a correlation coefficient matrix using the Pearson correlation method. The Random Forest (RF) model emerged as the top performer among standalone models, achieving an R2 value of 0.943 and an RMSE value of 0.504 eV. Bagging regression demonstrated improved performance across different dataset sizes with streamlined feature selection. Ensemble models, particularly bagging, consistently outperformed standalone models, achieving the best R2 value of 0.948 and an RMSE value of 0.479 eV in the test dataset. Using the best-performing model, we predicted bandgap values for new half-Heusler compounds with 18 valence electron counts. These predictions were successfully validated using accurate DFT calculations. DFT calculations indicated that the newly predicted compounds are narrow bandgap semiconductors with dynamic stability. Random forest ; Artificial intelligence ; Ensemble learning ; Machine learning ; Regression ; Band gap ; AdaBoost ; Mean squared error ; Regression analysis ; Computer science ; Crystal structure prediction ; Pearson product-moment correlation coefficient ; Feature selection ; Pattern recognition (psychology) ; Mathematics ; Support vector machine ; Materials science ; Statistics ; Crystal structure ; Chemistry Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Surface and Thin Film Phenomena
Authors: Pedram Niknam Rad
Source: International journal of modern achievement in science, engineering and technology. (article, 2023-12-21)
Field: Chemistry — Physical and Theoretical Chemistry

Enhancing precision in chemical experiments In this article, the integration of chemistry and information technology is explored to enhance precision in chemical experiments. This fusion involves leveraging electronic data and employing intelligent analysis to improve the accuracy of experiment results and reduce errors. The paper delves into methods and tools that utilize the combination of chemical data with information technology to enhance experiment performance. The innovative synergy between chemistry and information technology contributes to improved efficiency and precision in the field of chemistry. This intelligent approach to managing chemical experiment data utilizes algorithms and machine learning models for more accurate chemical data analysis. Furthermore, it enhances predictive capabilities for experiment outcomes and provides optimal solutions for conducting new experiments. This integration not only aids in the improvement of chemical experiment performance but also plays a significant role in various industries, including pharmaceuticals and biochemistry. The innovative combination empowers scientists to swiftly and more accurately address complex challenges in the field of chemistry. This amalgamation serves as an intelligent strategy for data management in chemical experiments. By incorporating machine learning algorithms, the analysis of chemical data becomes more precise. The integration not only focuses on retrospective analysis but also enhances the ability to predict future experiment outcomes. This predictive capability opens avenues for optimizing experimental procedures and resource utilization. The article emphasizes the transformative impact of this interdisciplinary collaboration, not only in traditional laboratory settings but also in diverse industries where chemical processes play a crucial role. Moreover, the intelligent synergy between chemistry and information technology proves instrumental in accelerating research and development processes, particularly in pharmaceuticals and biotechnology. The article concludes by highlighting the profound implications of this innovative approach, offering scientists a powerful tool to navigate and overcome the complexities of modern chemistry with increased speed and accuracy. Computer science ; Environmental science Various Chemistry Research Topics ; Metabolomics and Mass Spectrometry Studies
Authors: Shorup Chanda | Dastagir, Rafi | Farsia Kawsar Chowdhury
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

An Atomistic Study and Machine Learning Based Approach on the Elastic Properties of Selectively Laser Melted Invar 36 Alloy under Variable Laser Parameters Laser ; Alloy ; Variable (mathematics) ; Invar Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Linfeng Yu | Kexin Dong | Qi Yang | Yi Zhang | Xiong Zheng | Huimin Wang | Zhenzhen Qin | Guangzhao Qin
Source: arXiv (Cornell University) (preprint, 2023-09-11)
Field: Materials Science — Materials Chemistry

Superfolded configuration induced low thermal conductivity in two-dimensional carbon allotropes revealed via machine learning force constant potential Understanding the fundamental link between structure and functionalization is crucial for the design and optimization of functional materials, since different structural configurations could trigger materials to demonstrate diverse physical, chemical, and electronic properties. However, the correlation between crystal structure and thermal conductivity (\k{appa}) remains enigmatic. In this study, taking two-dimensional (2D) carbon allotropes as study cases, we utilize phonon Boltzmann transport equation (BTE) along with machine learning force constant potential to thoroughly explore the complex folding structure of pure sp2 hybridized carbon materials from the perspective of crystal structure, mode-level phonon resolved thermal transport, and atomic interactions, with the goal of identifying the underlying relationship between 2D geometry and \k{appa}. We propose two potential structure evolution mechanisms for targeted thermal transport properties: in-plane and out-of-plane folding evolutions, which are generally applicable to 2D carbon allotropes. It is revealed that the folded structure produces strong symmetry breaking, and simultaneously produces exceptionally strongly suppressed phonon group velocities, strong phonon-phonon scattering, and weak phonon hydrodynamics, which ultimately lead to low \k{appa}. The insight into the folded effect of atomic structures on thermal transport deepens our understanding of the relationship between structure and functionalization, which offers straightforward guidance for designing novel nanomaterials with targeted \k{appa}, as well as propel developments in materials science and engineering. Phonon ; Thermal conductivity ; Materials science ; Boltzmann equation ; Chemical physics ; Nanotechnology ; Carbon fibers ; Surface modification ; Crystal structure ; Condensed matter physics ; Physics ; Chemistry ; Crystallography ; Thermodynamics ; Physical chemistry ; Composite material ; Composite number Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Yasuaki Inoue | Naoaki Ono | Md. Altaf‐Ul‐Amin | Shigehiko Kanaya
Source: Journal of Computer Aided Chemistry (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Materials Data-science Approach for Comprehensive Understanding of Organic Thin Film Solar Cells In recent years, competition in organic photovoltaic cells (OPVs) performance improvement and organic semiconductor development has intensified. In response, there has been an upsurge in the development of predictive models for OPV performance utilizing machine learning. Until now, chemistry researchers have used various approaches when creating OPV cells as well as developing new materials to improve power conversion efficiency (PCE). However, not many of those original approaches have been used for performance prediction due to the small sample size. In this study, we conducted Data-science approach where we collected information from 115 scientific literatures and constructed a dataset with the addition of some new proposed variables to describe the structure and material composition of the active layer. This allows us to use 25 variables to describe OPVs in which the active layer forms a 1~3-level structure (1-layer, two- tiered and three-tiered). Proposed work also includes post-processing and measurement data that have not been addressed in existing studies. Several regression models were constructed with coefficients of determination exceeding 0.9 by supervised learning methods (random forest (RF), monmlp, etc.) using this data. Organic solar cell ; Materials science ; Nanotechnology ; Thin film solar cell ; Thin film ; Environmental science ; Computer science ; Composite material ; Polymer Machine Learning in Materials Science
Authors: Akihiro Tanaka | Juniper Tyree | Anton Björklund | Jarmo Mäkelä | Kai Puolamäki
Source: arXiv (Cornell University) (preprint, 2023-06-21)
Field: Chemistry — Physical and Theoretical Chemistry

$χ$iplot: web-first visualisation platform for multidimensional data $χ$iplot is an HTML5-based system for interactive exploration of data and machine learning models. A key aspect is interaction, not only for the interactive plots but also between plots. Even though $χ$iplot is not restricted to any single application domain, we have developed and tested it with domain experts in quantum chemistry to study molecular interactions and regression models. $χ$iplot can be run both locally and online in a web browser (keeping the data local). The plots and data can also easily be exported and shared. A modular structure also makes $χ$iplot optimal for developing machine learning and new interaction methods. Computer science ; Visualization ; HTML5 ; Modular design ; Key (lock) ; Domain (mathematical analysis) ; Web application ; Human–computer interaction ; World Wide Web ; Data mining ; Machine learning ; Information retrieval ; Programming language ; Operating system ; Mathematics Various Chemistry Research Topics ; Scientific Computing and Data Management ; Advanced Chemical Sensor Technologies
Authors: Casey J. Galvin | Kanji Nagai | Yu Kaneko | Shinya Yuki
Source: ChemRxiv (preprint, 2023-01-18)
Field: Chemistry — Spectroscopy

A Graph Convolutional Network for Elution Order Prediction in Chromatographic Enantioseparation Chromatography using chiral columns is the most effective method for determining the optical purity of enantiomer pairs, although identifying the absolute configuration of each peak is difficult and remains a challenging issue. We demonstrate a solution to this challenge based on machine learning in this study. We used a dataset that consists of retention times of absolute configurations of enantiomer pairs on CROWNPAK CR-I(+) and CR(+) columns to train a variety of neural network architectures. Our findings show that graph-based neural networks achieve superior prediction accuracy despite the relatively small dataset. Specifically, the model shows enantiomer elution order prediction accuracy of 88.9% for CR-I(+) and 88.4 % for CR(+) columns. We then predicted elution order for molecule types not contained in the original dataset, in order to test this model’s ability to generalize to new molecules. The overall accuracy across both column types was 81.0 %, indicating that our prediction model can be used for virtual experiments on enantioseparation before conducting actual experiments and analysis of column fractions. Elution ; Enantiomer ; Convolutional neural network ; Artificial neural network ; Graph ; Computer science ; Column (typography) ; Biological system ; Chemistry ; Chromatography ; Artificial intelligence ; Pattern recognition (psychology) ; Stereochemistry ; Theoretical computer science Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Zhinan Zhao | Kaige Ding | Xudong Shen | Siyuan Ma | Yanqing Qiu | Tingting Lang | Ting Chen
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accelerating Optimization of Terahertz Metasurface Design Using Principal Component Analysis in Conjunction with Deep Learning Networks Principal component analysis ; Conjunction (astronomy) ; Terahertz radiation ; Component (thermodynamics) ; Computer science ; Principal (computer security) ; Deep learning ; Artificial intelligence ; Materials science ; Physics ; Optoelectronics Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Antenna Design and Analysis
Authors: Ariana Remmel
Source: C&EN Global Enterprise (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

New platform transforms MOF simulations A new study presented last week at the ACS Spring 2023 meeting shows how the powerful computational techniques behind artificial intelligence tools such as ChatGPT can help chemists train machines to better understand metal-organic frameworks (MOFs). Chemists have trained neural networks to build machine learning models of MOFs that are capable of predicting specific properties of the material, such as electrical conductivity or its ability to capture volatile gases. But these neural networks generally can’t share between one another what they’ve learned from their task-specific training—partly because the properties of nanoporous materials are derived from both local features, such as the atomic composition of metal nodes and linkers, and global features, such as pore size. “It would be nice to have a new, universal model that is trained not just for a specific task but [on the] general scope of what the MOF is like,” Jihan Kim , a computational Nanoporous ; Task (project management) ; Artificial neural network ; Computer science ; Metal-organic framework ; Scope (computer science) ; Artificial intelligence ; Nanotechnology ; Machine learning ; Materials science ; Chemistry ; Engineering ; Systems engineering Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Katarina Batalović | Jana Radaković | Bojana Paskaš Mamula | Mirjana Medić Ilić | Bojana Kuzmanović
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

High-throughput screening of novel hydrogen storage materials – ML approach Hydride formation in metals is a widely studied and applied phenomenon necessary to transition to clean energy solutions and various technological applications. We focus on three perspective applications of these materials, namely near-ambient hydrogen storage, hydrogen storage compressor materials, and alkali metal conversion electrodes, to demonstrate acceleration in the research achieved by utilizing a data-driven approach. Graph neural network was developed using a transfer learning approach from the MEGNet model and data related to the thermodynamics of hydride formation obtained in experimental work. Based on the crystal structure and composition as input features, we apply the MetalHydrideEnth model developed in our previous work to predict hydride formation enthalpy in intermetallic compounds. In this work, we focus on demonstrating how this approach, combined with available crystal information obtained from density functional theory calculations, can be applied for fast and extensive searches of novel metal hydride materials, having in mind the above-listed applications. Hydrogen storage ; Hydride ; Intermetallic ; Hydrogen ; Materials science ; Enthalpy ; Work (physics) ; Energy storage ; Thermodynamics ; Computer science ; Metal ; Chemistry ; Metallurgy ; Alloy ; Physics ; Organic chemistry Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Fuel Cells and Related Materials
Authors: Ryosuke Jinnouchi | Ferenc Karsai | Georg Kresse
Source: arXiv (Cornell University) (preprint, 2023-09-22)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided First-Principles Calculations of Redox Potentials Redox potentials of electron transfer reactions are of fundamental importance for the performance and description of electrochemical devices. Despite decades of research, accurate computational predictions for the redox potential of even simple metals remain very challenging. Here we use a combination of first principles calculations and machine learning to predict the redox potentials of three redox couples, $\mathrm{Fe}^{2+}$/$\mathrm{Fe}^{3+}$, $\mathrm{Cu}^{+}$/$\mathrm{Cu}^{2+}$ and $\mathrm{Ag}^{+}$/$\mathrm{Ag}^{2+}$. Using a hybrid functional with a fraction of 25\% exact exchange (PBE0) the predicted values are 0.92, 0.26 and 1.99 V in good agreement with the best experimental estimates (0.77, 0.15, 1.98 V). We explain in detail, how we combine machine learning, thermodynamic integration from machine learning to semi-local functionals, as well as a combination of thermodynamic perturbation theory and $Δ$-machine learning to determine the redox potentials for computationally expensive hybrid functionals. The combination of these approaches allows one to obtain statistically accurate results. Redox ; Electron transfer ; Hybrid functional ; Electrochemistry ; Machine learning ; Physics ; Computer science ; Chemistry ; Materials science ; Thermodynamics ; Computational chemistry ; Density functional theory ; Physical chemistry ; Electrode ; Inorganic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Pancě Naumov
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-08-22)
Field: Chemistry — Physical and Theoretical Chemistry

Crystal adaptronics: intersectional and collective properties and effects of dynamic molecular crystals The anticipated shift in focal point of interest of solid-state chemists, crystal engineers and crystallographers from structure to properties to function of organic solids parallels the need to apply our accumulated understanding of the intricacies of crystal structure to explaining the related properties, with the ultimate goal of harnessing that knowledge in applications that require soft, light-weight, and/or biocompatible organic solids.In these developments, the adaptive molecular single crystals warrant a particular attention as a new class of materials for light, flexible, and environmentally benign devices, primarily memories, capacitors, sensors, and actuators.Some of the outstanding requirements for application of these dynamic materials as high-efficiency energy storage devices are strongly induced polarization, high switching field, and narrow hysteresis in reversible dynamic processes.However, having been studied almost exclusively by crystallographers, molecular crystals still lack the appropriate investigations that reliably evaluate their reproducibility, scalability, and actuating performance, and some important drawbacks have diverted the interest of engineers from these materials.United under the umbrella term crystal adaptronics, the recent research efforts aim to realistically assess the appositeness of dynamic crystals for applications that require fast, reversible and continuous operation over prolonged periods of time [1][2][3][4][5][6][7][8][9][10][11][12][13].With the aim to highlight the most recent developments in the research of adaptive molecular crystals, this lecture discusses their assets and pitfalls.Using machine learning for the first time, we identify inherent features and structure-function relationships that fundamentally impact the mechanical response of dynamic molecular crystals.Our approach factors in different crystal properties in tandem and deciphers their intersectional and combined effects on the dynamic performance.It also provides some hints on the likely future developments that capitalize on the untapped, sequestered potential for applications of this distinct materials class. Crystal (programming language) ; Materials science ; Crystallography ; Computer science ; Chemistry Crystallography and molecular interactions