jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kazuma Ito | Tatsuya Yokoi | Katsutoshi Hyodo | Hideki Mori
Source: Research Square (preprint, 2024-08-01)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential with DFT accuracy for general grain boundaries: Analysis of grain boundary energy and atomic structure in α-Fe polycrystals Grain boundary ; Interatomic potential ; Molecular dynamics ; Materials science ; Atomic units ; Extrapolation ; Crystallite ; Density functional theory ; Energy (signal processing) ; Microstructure ; Physics ; Chemistry ; Computational chemistry ; Metallurgy Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Materials Characterization Techniques
Authors: Emmanuel Ren | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2024-01-30)
Field: Chemistry — Inorganic Chemistry

Prediction of Diffusion Coefficient Through Machine Learning Based on Transition State Theory Descriptors Nanoporous materials serve as very effective media for storing or separating small molecules. To design the best materials for a given application based on adsorption, one usually assesses the equilibrium performance by using key thermodynamic quantities such as Henry constants or adsorption loading values. To go beyond standard methodologies, we probe here the transport effects occurring in the material by studying the self-diffusion coefficients of xenon inside the nanopores of framework materials. We find good correlations between the diffusion coefficients and the pore aperture size, as well as other geometrical and energetic descriptors. We used extensive molecular dynamics simulations to calculate the diffusion coefficient of xenon in 4,873 MOFs from the CoREMOF 2019 database, the first large-scale database of transport properties published at this scale. Based on this data, we present a tool to quickly evaluate the diffusion energy barrier that proved to be very correlated to the diffusion rate. This descriptor, alongside other geometrical characterizations, were then used to build a machine learning model that can predict the xenon diffusion coefficients in MOFs. The final trained model is quite accurate and shows a root mean square error (RMSE) on the log_{10} of the diffusion coefficient equal to 0.25. Diffusion ; Transition (genetics) ; State (computer science) ; Transition state theory ; Statistical physics ; Effective diffusion coefficient ; Computer science ; Machine learning ; Artificial intelligence ; Thermodynamics ; Physics ; Chemistry ; Algorithm ; Quantum mechanics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Rasmus Fromsejer | Bjørn Maribo‐Mogensen | Georgios M. Kontogeorgis | Xiaodong Liang
Source: npj Computational Materials (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Accurate formation enthalpies of solids using reaction networks Abstract Crystalline solids play a fundamental role in a host of materials and technologies, ranging from pharmaceuticals to renewable energy. The thermodynamic properties of these solids are crucial determinants of their stability and therefore their behavior. The advent of large density functional theory databases with properties of solids has stimulated research on predictive methods for their thermodynamic properties, especially for the enthalpy of formation Δ f H . Increasingly sophisticated artificial intelligence and machine learning (ML) models have primarily driven development in this field in recent years. However, these models can suffer from lack of generalizability and poor interpretability. In this work, we explore a different route and develop and evaluate a framework for the application of reaction network (RN) theory to the prediction of Δ f H of crystalline solids. For an experimental dataset of 1550 compounds we are able to obtain a mean absolute error w.r.t Δ f H of 29.6 meV atom −1 using the RN approach. This performance is better than existing ML-based predictive methods and close to the experimental uncertainty. Moreover, we show that the RN framework allows for straightforward estimation of the uncertainty of the predictions. Thermodynamics ; Materials science ; Process engineering ; Physics ; Engineering Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Computational Drug Discovery Methods
Authors: O. N. Zhukovskaya | Natalya V. Eliseeva | Pavel M. Vassiliev | Г. В. Придворов | O. Yu. Grechko | Anatoly S. Morkovnik | А. А. Спасов
Source: Research Results in Pharmacology (article, 2024-06-30)
Field: Chemistry — Organic Chemistry

Synthesis and pharmacological activity of various organic and inorganic salts of phenyl derivatives of imidazobenzimidazole Introduction: The creation of new opioid analgesics that are devoid of the main undesirable effects – euphoria, respiratory depression, tolerance – is an important task for the treatment of pain syndrome. Eighteen salts of 9-pyrrolidinoethyl-(1a), 9-piperidinoethyl-(1b), and 9-morpholinoethyl-2-(4-fluorophenyl)benzo[d]imidazo[1,2-a]imidazole (1c) were synthesized and tested for kappa agonist and analgesic activity. Materials and Methods: Specific analgesic activity studies were conducted using models of nociceptive pain in the “Hot/Cold-plate” and ”Plantar Test” tests, and in neuropathic pain against the background of sciatic nerve ligation. The relationship between the physicochemical and pharmacological properties of the compounds was studied in silico using quantum chemistry methods and artificial neural network technology. Results: The most active compound identified was 4-(2-(2-(4-fluorophenyl)-benzo[d]imidazo[1,2-a]imidazol-9-yl)ethyl)morpholine dihydrochloride (1c.2HCl). EC50, acute toxicity, and conditional therapeutic index values were calculated, which were 38.8 times superior to the kappa agonist U50488. It was established that compound 1c.2HCl exhibits a dose-dependent analgesic effect in the “Hot/Cold-plate” test, which is six times superior to that of butorphanol, maintaining a statistically significant effect for six hours in the “Plantar test” and without causing the formation of tolerance with a ten-day administration. In models of neuropathic pain syndrome, 1c.2HCl with a fourteen-day administration significantly reduced tactile and cold allodynia by three and 1.6 times, respectively, and was not inferior in activity to gabapentin. The kappa-opioidergic mechanism of the antinociceptive action of 1c.2HCl compound was confirmed. Quantum chemistry methods and artificial neural network technologies have shown that the high level of kappa-opioid agonist activity of hydrochlorides, in contrast to other salts, results in lower total energy production; therefore, there is a significant increase in energy during the formation of a salt supramolecular complex. Conclusion: The most active compound was identified – 1c.2HCl, for which kappa-opioid activity in vitro and analgesic properties were revealed in various in vivo models. It has been shown that the compound does not cause the formation of tolerance and is not toxic. Chemistry ; Organic chemistry ; Combinatorial chemistry Synthesis and biological activity
Authors: Taichi Morishita | Yusei Hisata | Taiki Hashimoto | Sensuke Ogoshi | Yoichi Hoshimoto
Source: Journal of Synthetic Organic Chemistry Japan (article, 2024-11-01)
Field: Chemistry — Organic Chemistry

Triarylborane Catalysis: From Hydrogenation of Unsaturated Molecules to H&lt;sub&gt;2&lt;/sub&gt; Purification Herein we summarize our recent progress on the design, synthesis, and catalytic application of triarylboranes. We demonstrated that structurally well-characterized triarylboranes catalyzed the hydrogenation of N-heteroaromatics and carbonyl compounds when crude H2 (a mixture of H2, CO, CO2, and CH4) was directly used as reductant. In addition, we proposed a concept of ‘remote back strain’ to finely tune the Lewis acidity of triarylboranes by regulating the stability of Lewis base-borane adducts via an intramolecular repulsion between the meta-substituents on the aryl groups. Such approaches were eventually combined with machine learning and efficiently assisted the optimization of boranes that catalyzed the reductive alkylation of multiply-substituted aniline derivatives, including amino acids and peptides, using H2 as reductant. These results manifest a new aspect of main-group catalysis beyond its application as a simple alternative to well-established transition metal-catalyzed processes. Chemistry ; Catalysis ; Molecule ; Medicinal chemistry ; Combinatorial chemistry ; Organic chemistry Organoboron and organosilicon chemistry ; Chemical Synthesis and Reactions
Authors: Clara Kirkvold | Brianna Collins | Jason D. Goodpaster
Source: The Journal of Physical Chemistry Letters (article, 2024-06-24)
Field: Materials Science — Materials Chemistry

CatEmbed: A Machine-Learned Representation Obtained via Categorical Entity Embedding for Predicting Adsorption and Reaction Energies on Bimetallic Alloy Surfaces Machine-learning models for predicting adsorption energies on metallic surfaces often rely on basic elemental properties and electronic and geometric descriptors. Here, we apply categorical entity embedding, a featurization method inspired by natural language processing techniques, to predict adsorption energies on bimetallic alloy surfaces using categorical descriptors. Using this method, we develop a machine-learned representation from categorical descriptors (e.g., surface composition, adsorbate type, and site type) of the slab/adsorbate complex. By combining this representation with numerical features (e.g., slab metal stoichiometric ratios), we create the CatEmbed representation. Remarkably, decision tree models trained using CatEmbed, which includes no explicit geometric information, achieve a Mean Absolute Error (MAE) of 0.12 eV. Additionally, we extend this technique to predict reaction energies on bimetallic surfaces, creating the CatEmbed-React representation, which achieves an MAE of 0.08 eV. These findings highlight the effectiveness of categorical entity embedding for predicting adsorption and reaction energies on bimetallic alloy surfaces. Bimetallic strip ; Categorical variable ; Embedding ; Representation (politics) ; Adsorption ; Materials science ; Slab ; Computer science ; Surface (topology) ; Metal ; Artificial intelligence ; Mathematics ; Chemistry ; Machine learning ; Physics ; Physical chemistry ; Geometry ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Clara Kirkvold | Brianna Collins | Jason D. Goodpaster
Source: ChemRxiv (preprint, 2024-05-22)
Field: Materials Science — Materials Chemistry

CatEmbed: A Machine-Learned Representation Obtained via Categorical Entity Embedding for Predicting Adsorption and Reaction Energies on Bimetallic Alloy Surfaces Machine-learning models for predicting adsorption energies on metallic surfaces often relies on basic elemental properties, electronic, and geometric descriptors. Here, we apply categorical entity embedding, a featurization method inspired by natural language processing techniques, to predict adsorption energies on bimetallic alloy surfaces using categorical descriptors. Using this method, we develop a machine-learned representation from categorical descriptors (e.g., surface composition, adsorbate type, and site type) of the slab/adsorbate complex. By combining this representation with numerical features (e.g., slab metal stoichiometric ratios), we create the CatEmbed representation. Remarkably, decision tree models trained using CatEmbed, which includes no explicit geometric information, achieve a Mean Absolute Error (MAE) of 0.12 eV. Additionally, we extended this technique to predict reaction energies on bimetallic surfaces, creating the CatEmbed-React representation, which achieves an MAE of 0.08 eV. These findings highlight the effectiveness of categorical entity embedding for predicting adsorption and reaction energies on bimetallic alloy surfaces. Bimetallic strip ; Categorical variable ; Representation (politics) ; Embedding ; Adsorption ; Materials science ; Slab ; Surface (topology) ; Alloy ; Stoichiometry ; Computer science ; Metal ; Mathematics ; Artificial intelligence ; Chemistry ; Machine learning ; Physics ; Physical chemistry ; Geometry ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Lei Zheng | Zhao-Yi Zeng | Yao Zhao | Xiao Liu | Zhe Huai | Xudong Zhang | Zhaoxi Sun | John Z. H. Zhang
Source: ChemRxiv (preprint, 2024-05-20)
Field: Chemistry — Spectroscopy

HSADab: A Comprehensive Database for Human Serum Albumin Human Serum Albumin (HSA), the most prevalent protein in human body fluids, is integral to the transportation, absorption, metabolism, distribution, and excretion of drugs. Its influence on a drug's therapeutic efficacy is substantial. Despite the importance of HSA as a drug target, the available data on its interactions with external agents (e.g., drug-like molecules and antibodies) are rather limited, which poses challenges for both molecular modelling investigations and the development of empirical scoring functions or machine learning predictors on this target. Moreover, the reported entries in existing databases often contain major inconsistencies due to varied experiments and conditions, which incurs worries about the data quality. To address these issues, we established a pioneering database through extensively reviewing more than 30000 scientific publications published between 1987 and 2023, encompassing over 5000 affinity data at multiple temperatures and more than 130 crystal structures that involve both the ligand-bound and apo forms. The current HSADab resource (www.hsadab.cn) serves as a reliable foundation for protocol validations of molecular simulations (e.g., traditional virtual screening workflow using docking, end-point and alchemical free energy techniques) as well as the data source for the implementation of machine learning predictors. Serum albumin ; Albumin ; Human albumin ; Database ; Computer science ; Internal medicine ; Medicine Advanced Proteomics Techniques and Applications
Authors: Lai Wei | Sadman Sadeed Omee | Rongzhi Dong | Nihang Fu | Yuqi Song | Edirisuriya M. Dilanga Siriwardane | Meiling Xu | Chris Wolverton | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2024-06-30)
Field: Materials Science — Materials Chemistry

CSPBench: a benchmark and critical evaluation of Crystal Structure Prediction Crystal structure prediction (CSP) is now increasingly used in discovering novel materials with applications in diverse industries. However, despite decades of developments and significant progress in this area, there lacks a set of well-defined benchmark dataset, quantitative performance metrics, and studies that evaluate the status of the field. We aim to fill this gap by introducing a CSP benchmark suite with 180 test structures along with our recently implemented CSP performance metric set. We benchmark a collection of 13 state-of-the-art (SOTA) CSP algorithms including template-based CSP algorithms, conventional CSP algorithms based on DFT calculations and global search such as CALYPSO, CSP algorithms based on machine learning (ML) potentials and global search, and distance matrix based CSP algorithms. Our results demonstrate that the performance of the current CSP algorithms is far from being satisfactory. Most algorithms cannot even identify the structures with the correct space groups except for the template-based algorithms when applied to test structures with similar templates. We also find that the ML potential based CSP algorithms are now able to achieve competitive performances compared to the DFT-based algorithms. These CSP algorithms' performance is strongly determined by the quality of the neural potentials as well as the global optimization algorithms. Our benchmark suite comes with a comprehensive open-source codebase and 180 well-selected benchmark crystal structures, making it convenient to evaluate the advantages and disadvantages of CSP algorithms from future studies. All the code and benchmark data are available at https://github.com/usccolumbia/cspbenchmark Benchmark (surveying) ; Crystal structure prediction ; Computer science ; Artificial intelligence ; Crystal structure ; Geology ; Crystallography ; Chemistry ; Geodesy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: YeonA Lee | Inho Lee | Hee‐Jun Kim | Hyun‐Woo Kim | Hyun-Woo Kim
Source: Processes (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Smart Strategic Management for the Cold Plasma Process Using ORP Monitoring and Total Organic Carbon Correlation Assessing oxidation–reduction potential (ORP) is of paramount importance in the efficient management of wastewater within both chemical and biological treatment processes. However, despite its critical role, insufficient information exists about how reactive chemical species generated by cold plasma (CP) in chemical treatment are associated with ORP and air flow rate. Therefore, we aim to identify the correlation between ORP and the removal of organic pollutants when using CP treatment. Additionally, we introduce a machine-learning-based operation to predict removal efficiency in the CP process. Results reveal a significant correlation of over 0.9 between real-time ORP and total organic carbon (TOC), which underscores the efficacy of ORP as a key parameter. This approach made it possible to control OH radical generation by regulating the air flow rate of the CP. This study posits that smart management facilitated by machine learning has the potential to enhance the economic viability of CP feasibility while maintaining overall treatment performance. Plasma ; Environmental chemistry ; Total organic carbon ; Environmental science ; Carbon fibers ; Chemistry ; Chemical engineering ; Materials science ; Physics ; Engineering ; Composite material Catalytic Processes in Materials Science ; Analytical chemistry methods development ; Industrial Gas Emission Control
Authors: Jing 晶 Xu 许 | Ruijuan 睿娟 Xiao 肖 | Lu Hong
Source: Chinese Physics Letters (article, 2024-04-17)
Field: Materials Science — Materials Chemistry

ESM Cloud Toolkit: A Copilot for Energy Storage Material Research Searching and designing new materials play crucial roles in the development of energy storage devices. In today’s world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on MatElab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI. Cloud computing ; Computer science ; Environmental science ; Systems engineering ; Operating system ; Engineering Machine Learning in Materials Science ; Advancements in Battery Materials ; Inorganic Chemistry and Materials
Authors: Robbin Bouwmeester | Keith Richardson | Richard Denny | Ian D. Wilson | Sven Degroeve | Lennart Martens | Johannes P.C. Vissers
Source: Talanta (article, 2024-03-28)
Field: Chemistry — Spectroscopy

Predicting ion mobility collision cross sections and assessing prediction variation by combining conventional and data driven modeling Chemistry ; Isobaric process ; Range (aeronautics) ; Collision ; Analyte ; Biological system ; Ranking (information retrieval) ; Data mining ; Machine learning ; Computer science ; Chromatography ; Thermodynamics Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Yulin Xie | Ghulam Mustafa | Najla AlMasoud | Taghrid S. Alomar | Mudassir Hussain Tahir | Zeinhom M El-Bahy | Muhammad Khurram Tufail
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-06-27)
Field: Materials Science — Polymers and Plastics

Machine learning assisted designing of conjugated organic chromophores, light absorption, and emission behavior prediction Chromophore ; Chemistry ; Conjugated system ; Absorption (acoustics) ; Maxima ; Biological system ; Cluster analysis ; Photochemistry ; Artificial intelligence ; Organic chemistry ; Computer science ; Optics ; Polymer Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Luminescence and Fluorescent Materials
Authors: Chao Liang | Yilimiranmu Rouzhahong | Yao Shen | Junhao Liang | Chunlin Yu | Biao Wang | Huashan Li
Source: Advanced Science (article, 2024-10-18)
Field: Materials Science — Materials Chemistry

A Cluster‐Based Deep Learning Model Perceiving Series Correlation for Accurate Prediction of Phonon Spectrum The spectral properties are the most prevalent continuous representation for characterizing transport phenomena and excitation responses, yet their accurate predictions remain a challenge due to the inability to perceive series correlations by existing machine learning (ML) models. Herein, a ML model named cluster-based series graph networks (CSGN) is developed based on the dynamical theory of crystal lattices to predict phonon density of states (PDOS) spectrum for crystal materials. The multiple atomic cluster representation is constructed to capture the diverse vibration modes, while the mixture Gaussian process and dynamic time warping mechanism are compiled to project from clusters to PDOS spectrum. Accurate predictions of complicated spectra with multiple or overlapping peaks are achieved. The high performance of CSGN model can be attributed to the pertinent feature extraction and the appropriate similarity evaluation, which enable the natural perception of structure-property relation and intrinsic series correlations as confirmed in the predictive results. The transferable and interpretable CSGN model advances ML predictions of spectral properties and reveals the potential of designing ML methods based on physical mechanisms. Series (stratigraphy) ; Statistical physics ; Artificial intelligence ; Computer science ; Phonon ; Representation (politics) ; Cluster (spacecraft) ; Dynamic time warping ; Gaussian ; Machine learning ; Algorithm ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics ; Geology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermography and Photoacoustic Techniques
Authors: Chunghee Nam
Source: Applied Physics A (article, 2024-11-23)
Field: Materials Science — Materials Chemistry

Prediction of supercooled liquid region of Fe-based metallic glasses by deep learning Supercooling ; Mean squared error ; Convolutional neural network ; Amorphous metal ; Artificial intelligence ; Python (programming language) ; Crystallization ; Deep learning ; Materials science ; Artificial neural network ; Computer science ; Machine learning ; Mineralogy ; Pattern recognition (psychology) ; Thermodynamics ; Mathematics ; Physics ; Chemistry ; Statistics ; Metallurgy Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys