jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kevin Maik Jablonka | Andrew Rosen | Aditi S. Krishnapriyan | Berend Smit
Source: ChemRxiv (preprint, 2022-09-01)
Field: Materials Science — Materials Chemistry

An ecosystem for digital reticular chemistry The space of all plausible materials for a given application is so large that it cannot be explored using a brute-force approach. This is, in particular, the case for reticular chemistry which provides materials designers with a practically infinite playground on different length scales. One promising approach to guide the design and discovery of materials is machine learning, which typically involves learning a mapping of structures onto properties from data. While there have been plenty of examples of the use of machine learning for reticular materials, the progress in the field seems to have stagnated. From our perspective, an important reason is that digital reticular chemistry is still more an art than a science in which many parts are only accessible to experienced groups. The lack of standardization across all the steps of the machine learning pipeline makes it practically impossible to directly compare machine learning models and build on top of prior results. To confront these challenges, we present mofdscribe: a software ecosystem that accompanies—seasoned as well as novice—digital reticular chemists on all steps from ideation to model publication. Our package provides reference datasets (including a completely new one), more than 35 reported as well as completely novel featurization strategies, data splitters, and validation helpers which can be used to benchmark new modeling strategies on standard benchmark tasks and to report the results on a public leaderboard. We envision that this ecosystem allows for a more robust, comparable, and productive area of digital reticular chemistry. Computer science ; Standardization ; Benchmark (surveying) ; Data science ; Pipeline (software) ; Artificial intelligence ; Reticular connective tissue ; Machine learning ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Lihang Liu | Donglong He | Xiaomin Fang | Shanzhuo Zhang | Fan Wang | Jingzhou He | Hua Wu
Source: Research Square (preprint, 2022-11-10)
Field: Materials Science — Materials Chemistry

GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions Range (aeronautics) ; Property (philosophy) ; Computer science ; Engineering ; Aerospace engineering ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: A. Tarbi | T. Chtouki | Amal Bouich | Y. El Kouari | H. Erguig | A. Migalska–Zalas
Source: Advances in Materials and Processing Technologies (article, 2022-08-31)
Field: Materials Science — Materials Chemistry

Prediction of mechanical properties of In <sub>1-x</sub> Ga <sub>x</sub> As <sub>y</sub> P <sub>1-y</sub> lattice-matched to different substrates using artificial neural network (ANN) The mechanical properties, namely the elastic constants (C11, C12, and C44), bulk B, shear Cs, and Young’s modulus Y0, of the In1-xGaxAsyP1-y lattice-matched GaAs and InP substrates, were estimated using an artificial neural network (ANN method). This study aimed to create ANN networks that facilitate the estimation of the mechanical properties of alloys subjected to different temperatures and hydrostatic pressures. We aimed to predict the mechanical properties of InGaAsP alloys matched to InP and GaAs substrates as a function of temperature and hydrostatic pressure. ANN learning was performed based on the results validated by the empirical pseudopotential method data within the virtual crystal approximation, including the actual disorder potential. An ANN is a nonlinear process modeling technique frequently encountered in materials science. The predictive curves show high reliability, and underline the importance of using this approach to replace the time-consuming and costly experimental test, as well as to predict the mechanical response of the material where conventional modeling approaches have failed. Our method can consolidate the theoretical references found in the literature, it can also serve the field of engineering the mechanical properties of semiconductors, facilitate the successful design of optoelectronic devices, and serve future experimental works. Artificial neural network ; Materials science ; Pseudopotential ; Hydrostatic pressure ; Hydrostatic equilibrium ; Shear modulus ; Machine learning ; Artificial intelligence ; Algorithm ; Computer science ; Condensed matter physics ; Thermodynamics ; Composite material ; Physics Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Semiconductor Quantum Structures and Devices
Authors: Sergio Pablo‐García | Santiago Morandi | Rodrigo A. Vargas–Hernández | Kjell Jorner | Núria López | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2022-10-06)
Field: Materials Science — Materials Chemistry

Fast Evaluation of the Adsorption Energy ofOrganic Molecules on Metals via GraphNeural Networks Modeling of solid-state material-molecule interfaces in heterogeneous catalysis requires the extensive evaluation of the energy of molecules on surfaces. Obtaining the binding energy of many configurations of large organic molecules requires a vast amount of computational time with density functional theory (DFT). Here, we use a graph neural network (GNN) to evaluate the adsorption energy of molecular species adsorbed on metallic surfaces. The GNN is trained on a set of C1–4 fragments including N, O, S heteroatoms and C6–10 aromatic rings. Compared to DFT, the GNN shows a mean absolute error (MAE) of 0.17 eV on the test set being 6 orders of magnitude faster. When applying the trained model with subsequent hyperparameter optimization to molecules of industrial interest (biomass, plastics and polyurethanes precursors) containing up to 22 carbon atoms, the prediction performance for the adsorption energy yields a MAE of 0.03 eV/(non-H atom). While the error for out-of-distribution molecules is higher, it is still within the acceptable limit for adsorption energies (0.05 eV/atom), confirming the viability of the approach. The proposed framework represents a potential tool for the fast screening of catalytic materials, as well as their inverse design, enabling the multi-scale modeling for systems that cannot be easily simulated by DFT. Adsorption ; Molecule ; Density functional theory ; Artificial neural network ; Heteroatom ; Atom (system on chip) ; Materials science ; Computational chemistry ; Chemistry ; Computer science ; Physical chemistry ; Organic chemistry ; Machine learning Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Green IT and Sustainability
Authors: Shehani T. Wetthasinghe | Chunyan Li | Huina Lin | Tianyu Zhu | Chuanbing Tang | Vitaly A. Rassolov | Qi Wang | Sophya Garashchuk
Source: The Journal of Physical Chemistry A (article, 2022-01-02)
Field: Materials Science — Materials Chemistry

Correlation between the Stability of Substituted Cobaltocenium and Molecular Descriptors Metallocenium cations, used as a component in an anion exchange membrane of a fuel cell, demonstrate excellent thermal and alkaline stability, which can be improved by the chemical modification of the cyclopentadienyl rings with substituent groups. In this work, the relation between the bond dissociation energy (BDE) of the cobaltocenium (CoCp<sub>2</sub><sup>+</sup>) derivatives, used as a measure of the cation stability, and chemistry-informed descriptors obtained from the electronic structural calculations is established. The analysis of 12 molecular descriptors for 118 derivatives reveals a nonlinear dependence of the BDE on the electron donating-withdrawing character of the substituent groups coupled to the energy of the frontier molecular orbitals. A chemistry-informed feed-forward neural network trained using k-fold cross-validation over the modest data set is able to predict the BDE from the molecular descriptors with the mean absolute error of about 1 kcal/mol. The theoretical analysis suggests some promising modifications of cobaltocenium for experimental research. The results demonstrate that even for modest data sets the incorporation of the chemistry knowledge into the neural network architecture, e.g., through mindful selection and screening of the descriptors and their interactions, paves the way to gain new insight into molecular properties. Substituent ; Chemistry ; Molecular descriptor ; Molecular orbital ; Bond-dissociation energy ; Computational chemistry ; Molecule ; Artificial neural network ; Cyclopentadienyl complex ; Electron affinity (data page) ; Dissociation (chemistry) ; Quantitative structure–activity relationship ; Physical chemistry ; Stereochemistry ; Organic chemistry ; Machine learning ; Computer science Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Oluwafisayomi Christiana Folorunso
Source: World Journal of Advanced Research and Reviews (article, 2022-12-30)
Field: Chemistry — Inorganic Chemistry

Metal-Organic Frameworks (MOFs) for environmental remediation: Current Status and Future Prospects Metal–organic frameworks (MOFs) have gained significant attention as advanced materials for environmental remediation due to their high surface area, tunable porosity, and versatile chemical functionality. These hybrid crystalline materials, composed of metal ions or clusters coordinated with organic linkers, offer unprecedented opportunities for the selective adsorption, separation, and catalytic degradation of diverse environmental pollutants. This review provides a comprehensive synthesis of recent progress in the development and application of MOFs for water and air purification. It begins with an exploration of fundamental MOF properties, including structural features, functionalization strategies, and affinity mechanisms relevant to contaminant removal. Various synthesis approaches are compared, with emphasis on emerging green fabrication techniques aimed at reducing environmental impact and enhancing scalability. The article highlights MOF applications in aqueous environments for the removal of heavy metals, dyes, antibiotics, and persistent organic pollutants, as well as gas-phase applications such as CO₂ capture, VOC adsorption, and photocatalytic air purification. The role of composite materials, such as MOF–polymer and MOF–carbon hybrids, is examined in enhancing stability and functionality. Key operational factors including pH, ionic strength, regeneration potential, and structural robustness are analyzed to assess real-world feasibility. Current challenges such as hydrolytic degradation, potential secondary pollution, economic viability, and lack of field-scale demonstrations are critically discussed. Finally, the review outlines future prospects for MOFs in environmental engineering, including the use of computational modeling, machine learning, smart MOF systems, lifecycle assessment (LCA), and integration into scalable treatment platforms. By consolidating recent advancements and identifying future research directions, this article serves as a valuable resource for scientists, engineers, and policymakers striving to develop sustainable and high-performance remediation technologies. Environmental remediation ; Metal-organic framework ; Nanotechnology ; Materials science ; Adsorption ; Environmental science ; Computer science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Magnesium Oxide Properties and Applications ; Adsorption and biosorption for pollutant removal
Authors: Kristian Gubsch | Rosalee Bence | Lawson T. Glasby | Peyman Z. Moghadam
Source: ChemRxiv (preprint, 2022-05-17)
Field: Chemistry — Inorganic Chemistry

DigiMOF: A Database of MOF Synthesis Information Generated via Text Mining The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data mine published MOF papers to extract the materials informatics knowledge contained within the journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials and text mined over 52,680 associated properties including synthesis method, solvent, organic linker, metal precursor, and topology. This centralised, structured database reveals the MOF synthetic data embedded within thousands of MOF publications. The DigiMOF database and associated software are publicly available for other researchers to conduct further analysis of alternative MOF production pathways and create additional parsers to search for other desirable properties. Computer science ; Parsing ; Identification (biology) ; Database ; Data mining ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Sida Gao | Zhiyu Gao | Fei Zhao | Weihua Xue | Xianjin Fan
Source: Materials Today Communications (article, 2022-11-15)
Field: Materials Science — Metals and Alloys

Machine learning (deep learning) and visualization assisted ferrite content prediction in austenitic stainless steel Materials science ; Ferrite (magnet) ; Austenite ; Metallurgy ; Visualization ; Austenitic stainless steel ; Composite material ; Artificial intelligence ; Corrosion ; Microstructure ; Computer science Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Welding Techniques and Residual Stresses
Authors: Manh‐Thuong Nguyen | Benjamin A. Helfrecht | Roger Rousseau | Vassiliki‐Alexandra Glezakou
Source: Journal of Molecular Liquids (article, 2022-08-17)
Field: Materials Science — Materials Chemistry

Actinides in complex reactive media: A combined ab initio molecular dynamics and machine learning analytics study of transuranic ions in molten salts Actinide ; Ab initio ; Chemistry ; Molecular dynamics ; Ionic radius ; Chemical physics ; Solvation ; Ionic bonding ; Ab initio quantum chemistry methods ; Computational chemistry ; Ion ; Physical chemistry ; Inorganic chemistry ; Molecule ; Organic chemistry Nuclear Materials and Properties ; Molten salt chemistry and electrochemical processes ; Radioactive element chemistry and processing
Authors: Hang Sun | Heye Zhang | Guangli Ren | Chao Zhang
Source: Materials (article, 2022-10-24)
Field: Materials Science — Materials Chemistry

A Knowledge Transfer Framework for General Alloy Materials Properties Prediction Biomedical metal implants have many applications in clinical treatment. Due to a variety of application requirements, alloy materials with specific properties are being designed continuously. The traditional alloy properties testing experiment is faced with high-cost and time-consuming challenges. Machine learning can accurately predict the properties of materials at a lower cost. However, the predicted performance is limited by the material dataset. We propose a calculation framework of alloy properties based on knowledge transfer. The purpose of the framework is to improve the prediction performance of machine learning models on material datasets. In addition to assembling the experiment dataset, the simulation dataset is also generated manually in the proposed framework. Domain knowledge is extracted from the simulation data and transferred to help train experiment data by the framework. The high accuracy of the simulation data (above 0.9) shows that the framework can effectively extract domain knowledge. With domain knowledge, the prediction performance of experimental data can reach more than 0.8. And it is 10% higher than the traditional machine learning method. The explanatory ability of the model is enhanced with the help of domain knowledge. In addition, five tasks are applied to show the framework is a general method. Computer science ; Domain knowledge ; Domain (mathematical analysis) ; Variety (cybernetics) ; Machine learning ; Knowledge transfer ; Transfer of learning ; Artificial intelligence ; Experimental data ; Data mining ; Knowledge management Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Jason B. Gibson | Ajinkya C. Hire | Richard G. Hennig
Source: arXiv (Cornell University) (preprint, 2022-02-28)
Field: Materials Science — Materials Chemistry

Data-Augmentation for Graph Neural Network Learning of the Relaxed Energies of Unrelaxed Structures Computational materials discovery has continually grown in utility over the past decade due to advances in computing power and crystal structure prediction algorithms (CSPA). However, the computational cost of the \textit{ab initio} calculations required by CSPA limits its utility to small unit cells, reducing the compositional and structural space the algorithms can explore. Past studies have bypassed many unneeded \textit{ab initio} calculations by utilizing machine learning methods to predict formation energy and determine the stability of a material. Specifically, graph neural networks display high fidelity in predicting formation energy. Traditionally graph neural networks are trained on large data sets of relaxed structures. Unfortunately, the geometries of unrelaxed candidate structures produced by CSPA often deviate from the relaxed state, which leads to poor predictions hindering the model's ability to filter energetically unfavorable prior to \textit{ab initio} evaluation. This work shows that the prediction error on relaxed structures reduces as training progresses, while the prediction error on unrelaxed structures increases, suggesting an inverse correlation between relaxed and unrelaxed structure prediction accuracy. To remedy this behavior, we propose a simple, physically motivated, computationally cheap perturbation technique that augments training data to improve predictions on unrelaxed structures dramatically. On our test set consisting of 623 Nb-Sr-H hydride structures, we found that training a crystal graph convolutional neural networks, utilizing our augmentation method, reduced the MAE of formation energy prediction by 66\% compared to training with only relaxed structures. We then show how this error reduction can accelerates CSPA by improving the model's ability to filter out energetically unfavorable structures accurately. Crystal structure prediction ; Artificial neural network ; Computer science ; Ab initio ; Algorithm ; Machine learning ; Artificial intelligence ; Statistical physics ; Physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Thijs Stuyver | Kjell Jorner | Connor W. Coley
Source: arXiv (Cornell University) (preprint, 2022-12-12)
Field: Chemistry — Organic Chemistry

Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions Bio-orthogonal click chemistry based on [3 + 2] dipolar cycloadditions has had a profound impact on the field of biochemistry and significant effort has been devoted to identify promising new candidate reactions for this purpose. To gauge whether a prospective reaction could be a suitable bio-orthogonal click reaction, information about both on- and off-target activation and reaction energies is highly valuable. Here, we use an automated workflow, based on the autodE program, to compute over 5000 reaction profiles for [3 + 2] cycloadditions involving both synthetic dipolarophiles and a set of biologically-inspired structural motifs. Based on a succinct benchmarking study, the B3LYP-D3(BJ)/def2-TZVP//B3LYP-D3(BJ)/def2-SVP level of theory was selected for the DFT calculations, and standard conditions and an (aqueous) SMD model were imposed to mimic physiological conditions. We believe that this data, as well as the presented workflow for high-throughput reaction profile computation, will be useful to screen for new bio-orthogonal reactions, as well as for the development of novel machine learning models for the prediction of chemical reactivity more broadly. Cycloaddition ; Workflow ; Click chemistry ; Reactivity (psychology) ; Computer science ; Chemistry ; Computational chemistry ; Quantum chemistry ; Benchmarking ; Reaction mechanism ; Computational science ; Combinatorial chemistry ; Biochemical engineering ; Organic chemistry ; Database ; Catalysis ; Engineering Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Freja Eilsø Storm | Linnea M. Folkmann | Thorsten Hansen | Kurt V. Mikkelsen
Source: Journal of Molecular Modeling (article, 2022-09-13)
Field: Materials Science — Materials Chemistry

Machine learning the frontier orbital energies of SubPc based triads Frontier ; Computer science ; Physics ; Political science Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; 2D Materials and Applications
Authors: Weihuan Li | Yang Zhou | Liping Ding | Pengfei Lv | Yifan Su | Rui Wang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

A Deep Learning-Based Potential Developed for Calcium Silicate Hydrates with Both High Accuracy and Efficiency Calcium silicate ; Calcium ; Silicate ; Calcium silicate hydrate ; Computer science ; Environmental science ; Artificial intelligence ; Materials science ; Chemical engineering ; Engineering ; Metallurgy Zeolite Catalysis and Synthesis ; Concrete and Cement Materials Research ; Chemical Synthesis and Characterization