jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gianmarco Terrones | Chenru Duan | Aditya Nandy | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2022-09-18)
Field: Materials Science — Materials Chemistry

Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis. However, the excited state property prediction of these complexes challenges ab initio methods such as time-dependent density functional theory (TDDFT) both from an accuracy and a computational cost perspective, complicating high throughput virtual screening (HTVS). We instead leverage low-cost machine learning (ML) models to predict the excited state properties of photoactive iridium complexes. We use experimental data of 1,380 iridium complexes to train and evaluate the ML models and identify the best-performing and most transferable models to be those trained on electronic structure features from low-cost density functional theory tight binding calculations. Using these models, we predict the three excited state properties considered, mean emission energy of phosphorescence, excited state lifetime, and emission spectral integral, with accuracy competitive with or superseding TDDFT. We conduct feature importance analysis to identify which iridium complex attributes govern excited state properties and we validate these trends with explicit examples. As a demonstration of how our ML models can be used for HTVS and the acceleration of chemical discovery, we curate a set of novel hypothetical iridium complexes and identify promising ligands for the design of new phosphors. Excited state ; Time-dependent density functional theory ; Iridium ; Density functional theory ; Computer science ; Phosphorescence ; Leverage (statistics) ; Ab initio ; Chemistry ; Materials science ; Artificial intelligence ; Computational chemistry ; Atomic physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts
Authors: R. Stepaniuk
Source: WAYS TO IMPROVE CONSTRUCTION EFFICIENCY (article, 2022-04-11)
Field: Materials Science — Materials Chemistry

A systemic approach to the reconstruction of engineering structures using intelligent information systems and regulatory-technical frameworks The article substantiates the relevance of implementing intelligent information systems (IIS) in the processes of engineering structure reconstruction as an important vector of digital transformation in the construction industry. Contemporary challenges — including the technical aging of critical infrastructure, a high degree of structural wear, and increasing requirements for reliability, resilience, and energy efficiency — necessitate the adoption of a comprehensive approach to managing reconstruction activities. The study demonstrates that reconstruction is impossible without the preliminary stages of technical monitoring, inspection, and residual life assessment of structures, as prescribed by current regulatory documents: DSTU-N B V.1.2-18:2016, DBN V.1.2-14:2018, and DBN A.3.1-5:2016. These documents clearly define the necessity of integrating technical condition evaluation into the general reconstruction cycle. The article proposes a systemic classification of engineering structures based on functional purpose, which serves as a foundation for further technological modeling of their reconstruction in accordance with the regulatory and legal framework. A synthesis of international experience and analytical forecasts (including McKinsey and ABC) demonstrates that without digitalization and large-scale workforce retraining, the construction sector will be unable to adequately respond to growing challenges. Therefore, the use of IIS in reconstruction processes is not seen as an optional innovation, but as an essential tool to ensure the reliability, safety, and technological compliance of engineering structures under modern conditions. The implementation of IIS — particularly BIM/CIM modeling, automated control systems, machine learning algorithms, digital sensors, and common data environment (CDE) platforms — enables more efficient decision-making, improves the accuracy of reconstruction solutions, and reduces costs at all stages. IIS are actively applied during inspection, design, construction execution, and post-reconstruction monitoring, ensuring continuity of information flows, timely risk response, efficient logistics, and long-term operational performance of structures. Relevance (law) ; Information system ; Information technology ; Workforce ; Digital transformation ; Automation ; Intelligent decision support system Structural mechanics and materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Construction Management and Sustainability
Authors: José I. Bueso-Bordils
Source: Proceedings of MOL2NET'22, Conference on Molecular, Biomedical &amp; Computational Sciences and Engineering, 8th ed. - MOL2NET: FROM MOLECULES TO NETWORKS (article, 2022-12-22)
Field: Chemistry — Organic Chemistry

Post-publication cover of Molecular Topology for the Search of New Anti-MRSA Compounds *Note: Mol2Net conference is associated to different MDPI journals special issues guest edited by Mol2Net Conference Committee members. This is an strategy to increase the online post-publication visibility of papers and conference, promote post-publication brainstorming discussion, and increase authors feedback. This association implies that our conference perform post-publication indexing of selected papers already published in MDPI journals with the consent of the issue editors. We publish free-of-cost these post-publication summaries. They include a shortened title, corresponding author info, and paper cover pdf file. The cover pdf file contains paper first page with all authors, abstract, full reference , and link to original papers.Reference: This is a Mol2Net conference post-publication cover for a paper published in the special issue Complex Networks, Bio-Molecular Systems, and Machine Learning, Edited by: Dr. H Gonz&aacute;lez-D&iacute;az. Visit the link to see original paper. Reference: Int. J. Mol. Sci. 2021, 22(11), 5823; https://doi.org/10.3390/ijms22115823 Abstract. The variability of methicillin-resistant Staphylococcus aureus (MRSA), its rapid adaptive response against environmental changes, and its continued acquisition of antibiotic resistance determinants have made it commonplace in hospitals, where it causes the problem of multidrug resistance. In this study, we used molecular topology to develop several discriminant equations capable of classifying compounds according to their anti-MRSA activity. Topological indices were used as structural descriptors and their relationship with anti-MRSA activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four extra equations were constructed, named DFMRSA1, DFMRSA2, DFMRSA3 and DFMRSA4 (DFMRSA was built in a previous study), all with good statistical parameters, such as Fisher&ndash;Snedecor F (&gt;68 in all cases), Wilk&rsquo;s lambda (&lt;0.13 in all cases), and percentage of correct classification (&gt;94% in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound. The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of anti-MRSA activity. Cover (algebra) ; Brainstorming ; Library science ; Computer science ; Publication ; Information retrieval ; Artificial intelligence ; Medicine ; Political science ; Engineering ; Law Synthesis and biological activity ; Computational Drug Discovery Methods ; Click Chemistry and Applications
Authors: Shen, Ciyue
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Systematic approaches for nominating combination therapies in cancer Combination therapy in cancer can provide enhanced anticancer efficacy and reduce the risk of drug resistance. Focused molecular experiments to discover combinations in a large search space are inefficient while high-throughput screening approaches do not provide sufficient mechanistic insights. We present two systematic approaches for nominating combination therapies, i) computational prediction of cellular response to unseen combination perturbations based on network models of cell biology, and ii) unbiased proteomic profiling of cellular response upon drug treatment to identify resistance mechanisms. Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides information for constructing computational models of cell biology. Using a perturbation-response dataset of a melanoma cell line after drug treatments as a testbed, we developed a hybrid approach that combines explicit mathematical models of cell dynamics with a machine-learning framework to quantitatively predict cell behavior in response to perturbation of molecular targets. When sufficient perturbation-response data is available for model training, this model can predict cellular response to a vast number of unseen combination perturbations and therefore efficiently narrow down the search space and nominate promising sets of experimentally testable combination candidates. Investigation of resistance mechanisms can be used for the rational design of combination therapy. In an alternative approach, we used unbiased quantitative protein mass spectrometry to assess the cellular response profile to a small number of anti-cancer drug perturbations in ovarian cancer cells. Data-driven protein network analysis revealed known and novel markers of resistance, which we used to propose combination drug candidates. In a first round of validation experiments, synergistic and additive effects were observed for some combination candidates across multiple ovarian cancer cell lines, suggesting potential therapeutic value for future pre-clinical and clinical studies. Both systematic approaches can be used to effectively nominate combination therapies, provided the availability of sufficiently informative perturbation-response data. With further validation, the proposed combination candidates may contribute to the development of novel effective cancer therapeutics. We believe the approaches can be generalized to other cancer types and potentially be applied to other areas of cell biology. Biological network ; Drug response ; Drug ; Cancer drugs ; Systems biology ; Cancer ; Parameter space Advanced Proteomics Techniques and Applications ; Gene Regulatory Network Analysis ; Bioinformatics and Genomic Networks
Authors: Sergei V. Kalinin | Rama K. Vasudevan | Yongtao Liu | Ayana Ghosh | Kevin M. Roccapriore | Maxim Ziatdinov
Source: arXiv (Cornell University) (preprint, 2022-10-12)
Field: Materials Science — Materials Chemistry

Microscopy is All You Need We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tremendous progress achieved by machine learning (ML) and artificial intelligence over the last decade has been largely achieved via the utilization of static data sets, from the paradigmatic MNIST to the bespoke corpora of text and image data used to train large models such as GPT3, DALLE and others. However, it is now recognized that continuous, minute improvements to state-of-the-art do not necessarily translate to advances in real-world applications. We argue that a promising pathway for the development of ML methods is via the route of domain-specific deployable algorithms in areas such as electron and scanning probe microscopy and chemical imaging. This will benefit both fundamental physical studies and serve as a test bed for more complex autonomous systems such as robotics and manufacturing. Favorable environment characteristics of scanning and electron microscopy include low risk, extensive availability of domain-specific priors and rewards, relatively small effects of exogeneous variables, and often the presence of both upstream first principles as well as downstream learnable physical models for both statics and dynamics. Recent developments in programmable interfaces, edge computing, and access to APIs facilitating microscope control, all render the deployment of ML codes on operational microscopes straightforward. We discuss these considerations and hope that these arguments will lead to creating a novel set of development targets for the ML community by accelerating both real-world ML applications and scientific progress. Computer science ; Artificial intelligence ; Software deployment ; Testbed ; Domain (mathematical analysis) ; Bespoke ; Robotics ; Reinforcement learning ; Machine learning ; Software engineering ; Robot Machine Learning in Materials Science ; Software Engineering Research ; Scientific Computing and Data Management
Authors: Salar Heydari Shalmani | Azadeh Ebrahimian Pirbazari | Fatemeh Esmaeili Khalil Saraei | Fatemeh‐Sadat Tabatabai‐Yazdi | Ali Derakhshesh | Amin Esmaeili | Ali Ebrahimian Pirbazari
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Visible Light-Assisted Deposition of Cobalt Co-Catalyst on Tio2 Nanosheets as Hybrid Photocatalysts: Combined Experimental and Computational Analysis Cobalt ; Catalysis ; Photocatalysis ; Materials science ; Deposition (geology) ; Visible spectrum ; Chemical engineering ; Nanotechnology ; Chemistry ; Optoelectronics ; Metallurgy ; Organic chemistry Nanomaterials for catalytic reactions ; TiO2 Photocatalysis and Solar Cells ; Advanced Photocatalysis Techniques
Authors: Hilal Ezgi Toraman | Barbara Alejandra Perez | Jayarama Krishna J.V.
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Insights into Co-Pyrolysis of Polyethylene Terephthalate and Polyamide 6 Mixture Through Experiments, Kinetic Modeling and Machine Learning Polyethylene terephthalate ; Polyamide ; Kinetic energy ; Pyrolysis ; Materials science ; Chemical engineering ; Polyethylene ; Composite material ; Engineering ; Physics Polymer crystallization and properties ; Fiber-reinforced polymer composites
Authors: Apurba Nandi | Riccardo Conte | Qu, Chen | Paul L. Houston | Yu, Qi | Joel M. Bowman
Source: arXiv (Cornell University) (preprint, 2022-06-06)
Field: Materials Science — Materials Chemistry

Quantum calculations on a new CCSD(T) machine-learned PES reveal the leaky nature of gas-phase $trans$ and $gauche$ ethanol conformers Ethanol is a molecule of fundamental interest in combustion, astrochemistry, and condensed phase as a solvent. It is characterized by two methyl rotors and $trans$ ($anti$) and $gauche$ conformers, which are known to be very close in energy. Here we show that based on rigorous quantum calculations of the vibrational zero-point state, using a new ab initio potential energy surface (PES), the ground state resembles the $trans$ conformer but substantial delocalization to the $gauche$ conformer is present. This explains experimental issues about the identification and isolation of the two conformers. This "leak" effect is partially quenched when deuterating the OH group, which further demonstrates the need for a quantum mechanical approach. Diffusion Monte Carlo (DMC) and full-dimensional semiclassical dynamics calculations are employed. The new PES is obtained by means of a $Δ$-Machine learning approach starting from a pre-existing low level (LL) density functional theory (DFT) surface. This surface is brought to the CCSD(T) level of theory using a relatively small number of $ab$ $initio$ CCSD(T) energies. Agreement between the corrected PES and direct $ab$ $initio$ results for standard fidelity tests is excellent. One- and two-dimensional discrete variable representation calculations focusing on the $trans$-$gauche$ torsional motion are also reported, in reasonable agreement with the experiment. Conformational isomerism ; Ab initio ; Potential energy surface ; Diffusion Monte Carlo ; Ab initio quantum chemistry methods ; Chemistry ; Density functional theory ; Computational chemistry ; Physics ; Quantum mechanics ; Monte Carlo method ; Molecule ; Mathematics ; Hybrid Monte Carlo Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Xiangdong Xu | Dimitrios Valavanis | Paolo Ciocci | Samuel Confederat | Fabio Marcuccio | Jean‐François Lemineur | Paolo Actis | Frédéric Kanoufi | Patrick R. Unwin
Source: ChemRxiv (preprint, 2022-11-14)
Field: Chemistry — Electrochemistry

The new era of high throughput nanoelectrochemistry Scanning electrochemical probe microscopies (SEPMs) have played a key role in advancing small-scale electrochemistry. SEPMs use an electrochemical probe (micro/nanoelectrode or pipet) to quantify and map local interfacial fluxes of electroactive species and have found increasingly wide applications. Our contribution to the Fundamental and Applied Reviews in Analytical Chemistry 2019 discussed how advances in SEPMs converged towards nanoscale electrochemical mapping. This inflection in experimental capability has opened up myriad opportunities for SEPMs in many types of systems, from material and energy sciences to the life sciences. The enhancement in the spatial resolution of imaging techniques, and instrumental developments, have resulted in significant increases in the size of electrochemical datasets from a typical experiment and served to speed up measurement throughput. Next generation nanoelectrochemistry will thus see an emphasis on “big data”, its analysis, storage and curation, high throughput analysis and parallelization, “intelligent” instruments and experiments, active control of nanoscale systems, and the integration of nanoelectrochemistry and nanoscale micro(spectro)scopy. For this review article, we focus on recent advances in frontier nanoscale electrochemistry, analysis and imaging techniques that are already addressing some of these key targets, and are well-placed to embrace other aspects in the near future. Our goal is to provide an overview of the present state-of-the-art in high throughput nanoelectrochemistry and imaging, and signpost promising new avenues for nanoscale electrochemical methods. Throughput ; Nanotechnology ; Nanoscopic scale ; Computer science ; Scale (ratio) ; Data science ; Materials science ; Physics ; Telecommunications Electrochemical Analysis and Applications ; Force Microscopy Techniques and Applications ; Conducting polymers and applications
Authors: Xiangdong Xu | Dimitrios Valavanis | Paolo Ciocci | Samuel Confederat | Fabio Marcuccio | Jean‐François Lemineur | Paolo Actis | Frédéric Kanoufi | Patrick R. Unwin
Source: ChemRxiv (preprint, 2022-11-16)
Field: Chemistry — Electrochemistry

The new era of high throughput nanoelectrochemistry Scanning electrochemical probe microscopies (SEPMs) have played a key role in advancing small-scale electrochemistry. SEPMs use an electrochemical probe (micro/nanoelectrode or pipet) to quantify and map local interfacial fluxes of electroactive species and have found increasingly wide applications. Our contribution to the Fundamental and Applied Reviews in Analytical Chemistry 2019 discussed how advances in SEPMs converged towards nanoscale electrochemical mapping. This inflection in experimental capability has opened up myriad opportunities for SEPMs in many types of systems, from material and energy sciences to the life sciences. The enhancement in the spatial resolution of imaging techniques, and instrumental developments, have resulted in significant increases in the size of electrochemical datasets from a typical experiment and served to speed up measurement throughput. Next generation nanoelectrochemistry will thus see an emphasis on “big data”, its analysis, storage and curation, high throughput analysis and parallelization, “intelligent” instruments and experiments, active control of nanoscale systems, and the integration of nanoelectrochemistry and nanoscale micro(spectro)scopy. For this review article, we focus on recent advances in frontier nanoscale electrochemistry, analysis and imaging techniques that are already addressing some of these key targets, and are well-placed to embrace other aspects in the near future. Our goal is to provide an overview of the present state-of-the-art in high throughput nanoelectrochemistry and imaging, and signpost promising new avenues for nanoscale electrochemical methods. Throughput ; Nanotechnology ; Nanoscopic scale ; Computer science ; Scale (ratio) ; Data science ; Materials science ; Physics ; Telecommunications Electrochemical Analysis and Applications ; Conducting polymers and applications ; Analytical Chemistry and Sensors
Authors: Ehsan Kianfar
Source: Research Square (preprint, 2022-05-19)
Field: Chemistry — Inorganic Chemistry

Synthesis and modification of Nickel /SAPO-34 Nanocatalyst for MTO Process via ultrasound and impregnation and hydrothermal methods Abstract In this paper, to research the effect of Ni on the structure and performance of the SAPO-34 catalyst, seven samples were synthesized. 3 samples were prepared via ultrasound, impregnation, and hydrothermal methods and with 2% Ni, the characteristics and performance of those samples were compared with SAPO-34 and was choosing the sole method. Then three samples were synthesized with a ratio of 3, 5, 7% Nickel then the optimized method. These catalysts were characterized by BET, NH3-TPD techniques. Cooperation of the performance of the catalysts showed that catalysts prepared by using ultrasound are more appropriate because of smaller particle size, Reduction of the expansion of the crystal, and uniform distribution of particles within the catalyst structure than the catalysts prepared by impregnation and hydrothermal method. Catalyst synthesized by ultrasound and 5% nickel, due to high crystallinity, the little size of crystals, high expanse, and suitable acid power showed the most effective possible selectivity for ethylene and propylene. The utmost ethylene and propylene selectivity for this sample was 47.89% and 35.16%. The sample was synthesized by using ultrasound and seven nickel, that possessed strong and high density of acidic sites thanks to the increasing amount of nickel, despite the reduction of particle size, was deactivated rapidly. Pure SAPO-34 was the underside selectivity to light olefins. Catalysis ; Crystallinity ; Nickel ; Hydrothermal circulation ; Materials science ; Selectivity ; Particle size ; Chemical engineering ; Ethylene ; Hydrothermal synthesis ; Nuclear chemistry ; Chemistry ; Metallurgy ; Organic chemistry ; Composite material Zeolite Catalysis and Synthesis ; Polyoxometalates: Synthesis and Applications ; Catalysis and Hydrodesulfurization Studies