jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lilia S. Xie | Samra Husremović | Oscar González | Isaac M. Craig | D. Kwabena Bediako
Source: ChemRxiv (preprint, 2022-04-07)
Field: Materials Science — Materials Chemistry

Structure and Magnetism of Iron- and Chromium-Intercalated Niobium and Tantalum Disulfides Transition metal dichalcogenides (TMDs) intercalated with spin-bearing transition metal centers are a diverse class of magnetic materials where the spin density and ordering behavior can be varied by the choice of host lattice, intercalant identity, level of intercalation, and intercalant disorder. Each of these degrees of freedom alters the interplay between several key magnetic interactions to produce disparate collective electronic and magnetic phases. The array of magnetic and electronic behavior typified by these systems renders them distinctive platforms for realizing tunable magnetism in solid-state materials and promising candidates for spin-based electronic devices. This Perspective provides an overview of the rich magnetism displayed by transition metal-intercalated TMDs by considering Fe- and Cr-intercalated NbS2 and TaS2. These four exemplars of this large family of materials exhibit a wide range of magnetic properties, including sharp switching of magnetic states, current-driven magnetic switching, and chiral spin textures. An understanding of the fundamental origins of the resultant magnetic/electronic phases in these materials is discussed in the context of composition, bonding, electronic structure, and magnetic anisotropy in each case study. Magnetism ; Condensed matter physics ; Materials science ; Electronic structure ; Transition metal ; Niobium ; Magnetic anisotropy ; Density functional theory ; Tantalum ; Chemical physics ; Chemistry ; Magnetic field ; Magnetization ; Physics ; Computational chemistry 2D Materials and Applications ; Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials
Authors: Alexander Humeniuk | William J. Glover
Source: ChemRxiv (preprint, 2022-06-29)
Field: Chemistry — Physical and Theoretical Chemistry

Multistate, polarizable QM/MM embedding scheme based on the direct reaction field method: Solvatochromic shifts, analytic gradients and optimization of conical intersections in solution A polarizable embedding scheme is presented that accounts for differential solvation of ground and excited states in QM/MM simulations. The polarization and dispersion interactions between the quantum-mechanical (QM) and molecular-mechanical (MM) regions are described by the direct reaction field (DRF) Hamiltonian, while the Pauli repulsion between explicitly treated QM electrons and the implicit electron density around MM atoms is modeled with effective core potentials. A single Hamiltonian is used for all electronic states, so that Born-Oppenheimer states belonging to the same geometry are orthogonal and state crossings are well-defined. The method is implemented in TeraChem, where it is combined with multiple electronic structure methods, including Hartree-Fock, Configuration Interaction Singles, and Complete Active Space Self-Consistent Field. In contrast with older implementations of the DRF method, integrals of the polarization operators are evaluated exactly. Expressions for ingredients needed to construct analytical gradients and non-adiabatic coupling vectors are derived and tested by optimizing a conical intersection between two excited states in the presence of a polarizable solvent shell. The method is applied to estimating the solvent shifts of absorption energies of a series of donor-acceptor dyes having low-lying charge transfer states. Even for a non-polar solvent such as n-hexane the inclusion of its static polarizability leads to non-negligible shifts that improve the agreement with the positions of experimental absorption maxima measured in solution. Conical intersection ; Polarizability ; Hamiltonian (control theory) ; Chemistry ; QM/MM ; Excited state ; Configuration interaction ; Solvent models ; Solvation ; Complete active space ; Solvatochromism ; Molecular physics ; Atomic physics ; Quantum mechanics ; Density functional theory ; Physics ; Computational chemistry ; Solvent ; Molecular dynamics ; Molecule Photochemistry and Electron Transfer Studies ; Spectroscopy and Quantum Chemical Studies ; Free Radicals and Antioxidants
Authors: Chiamaka Okafor | Norman Munroe
Source: Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research and Leadership in Post-pandemic Engineering: Resilient, Inclusive and Sustainable Actions” (article, 2022-01-01)
Field: Materials Science — Biomaterials

Developing trends in Mg alloys for biomedical implant applications Magnesium based alloys are biocompatible and safe for the manufacture of temporary implantable medical devices. Specifically, Mg-Zn-Ca alloys have received much attention as a biodegradable implant material due to superior mechanical properties. Various alloying and processing techniques have been adopted to simultaneously improve their mechanical properties and degradation behaviors. Extrusion, amongst other processing techniques offers these desired properties, and in addition to effective alloy design can be used in tuning magnesium alloys for various implant applications. With reference to current research progress, future directions should explore the development of alloys that incorporate elements such as Zn and Li that enhance the strength and ductility without promoting localized degradation. Materials science ; Implant ; Computer science ; Medicine Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Mykola Nagirniak | Konstantin Rusakov
Source: Acta Scientiarum Polonorum Architectura (article, 2022-02-11)
Field: Materials Science — Materials Chemistry

Elastic beam resting on Vlasov elastic foundation and subjected to an external concentrated load Foundation (evidence) ; Beam (structure) ; Mechanics ; Physics ; Structural engineering ; Materials science ; Classical mechanics ; Engineering ; Political science Material Properties and Failure Mechanisms ; Geotechnical Engineering and Underground Structures ; Composite Structure Analysis and Optimization
Authors: Michael S. Chen | Joonho Lee | Hong‐Zhou Ye | Timothy C. Berkelbach | David R. Reichman | Thomas E. Markland
Source: arXiv (Cornell University) (preprint, 2022-11-29)
Field: Materials Science — Materials Chemistry

Machine learning potentials from transfer learning of periodic correlated electronic structure methods: Application to liquid water with AFQMC, CCSD, and CCSD(T) Obtaining the atomistic structure and dynamics of disordered condensed phase systems from first principles remains one of the forefront challenges of chemical theory. Here we exploit recent advances in periodic electronic structure to show that, by leveraging transfer learning starting from lower tier electronic structure methods, one can obtain machine learned potential energy surfaces for liquid water from the higher tier AFQMC, CCSD, and CCSD(T) approaches using $\le$200 energies. By performing both classical and path integral molecular dynamics simulations on these machine learned potential energy surfaces we uncover the interplay of dynamical electron correlation and nuclear quantum effects across the entire liquid range of water while providing a general strategy for efficiently utilizing periodic correlated electronic structure methods to explore disordered condensed phase systems. Electronic structure ; Statistical physics ; Liquid water ; Molecular dynamics ; Phase (matter) ; Chemical physics ; Range (aeronautics) ; Physics ; Chemistry ; Computer science ; Materials science ; Condensed matter physics ; Quantum mechanics ; Thermodynamics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Ling Pan | Dinghuai Zhang | Aaron Courville | Longbo Huang | Yoshua Bengio
Source: arXiv (Cornell University) (preprint, 2022-10-07)
Field: Materials Science — Materials Chemistry

Generative Augmented Flow Networks The Generative Flow Network is a probabilistic framework where an agent learns a stochastic policy for object generation, such that the probability of generating an object is proportional to a given reward function. Its effectiveness has been shown in discovering high-quality and diverse solutions, compared to reward-maximizing reinforcement learning-based methods. Nonetheless, GFlowNets only learn from rewards of the terminal states, which can limit its applicability. Indeed, intermediate rewards play a critical role in learning, for example from intrinsic motivation to provide intermediate feedback even in particularly challenging sparse reward tasks. Inspired by this, we propose Generative Augmented Flow Networks (GAFlowNets), a novel learning framework to incorporate intermediate rewards into GFlowNets. We specify intermediate rewards by intrinsic motivation to tackle the exploration problem in sparse reward environments. GAFlowNets can leverage edge-based and state-based intrinsic rewards in a joint way to improve exploration. Based on extensive experiments on the GridWorld task, we demonstrate the effectiveness and efficiency of GAFlowNet in terms of convergence, performance, and diversity of solutions. We further show that GAFlowNet is scalable to a more complex and large-scale molecule generation domain, where it achieves consistent and significant performance improvement. Leverage (statistics) ; Computer science ; Reinforcement learning ; Generative grammar ; Probabilistic logic ; Scalability ; Artificial intelligence ; Machine learning ; Generative model ; Function (biology) Machine Learning in Materials Science ; Reinforcement Learning in Robotics ; Explainable Artificial Intelligence (XAI)
Authors: Hunter King | Aida Shahrokhian | Fan Kiat Chan | Jiansheng Feng | Mattia Gazzola
Source: Research Square (preprint, 2022-02-25)
Field: Materials Science — Surfaces, Coatings and Films

How a beetle catches fog Abstract In the face of humanity's increasing problems of water insecurity, useful lessons in moisture management may be learned from organisms living in extreme climates. The Namib desert beetle uses its own body to extract water, in the form of fog, from the wind, and its dorsal features have inspired designs based on controlled wettability. Here, we revisit the beetle's physical adaptations from the fluid dynamics perspective of aerosol impaction. Coupled experiments and simulations show that subtle modulations in shape and texture create large gains in water harvesting performance by encouraging droplets to `slip' toward targets at the millimetric scale, and by disrupting boundary and lubrication layers effects at the microscopic scale. Our results offer a lesson in biological fog collection, and design principles for controlling particle separation in a variety of contexts. Environmental science ; Aerosol ; Ecology ; Computer science ; Meteorology ; Geography ; Biology Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Plant Surface Properties and Treatments
Authors: Gaurav Kumar
Source: Journal of Survey in Fisheries Sciences (review, 2022-12-16)
Field: Chemistry — Organic Chemistry

A Review On Anti-Bacterial Activity Of Substituted Azetidinone, Benzothiazole,Thiazole, Thiadiazole, Triazole, Triazolothiazole And Naphthalene Derivatives Benzothiazole ; Thiazole ; Naphthalene ; Chemistry ; Triazole ; 1,2,4-Triazole ; Combinatorial chemistry ; Stereochemistry ; Medicinal chemistry ; Organic chemistry Synthesis of β-Lactam Compounds ; Synthesis and biological activity ; Synthesis of Tetrazole Derivatives
Authors: Hanna Türk | Elisabetta Landini | Christian Künkel | Johannes T. Margraf | Karsten Reuter
Source: ChemRxiv (preprint, 2022-07-06)
Field: Materials Science — Materials Chemistry

Assessing Deep Generative Models in Chemical Composition Space The computational discovery of novel materials has been one of the main motivations behind research in theoretical chemistry for several decades. Despite much effort, this is far from a solved problem, however. Among other reasons, this is due to the enormous space of possible structures and compositions that could potentially be of interest. In the case of inorganic materials, this is exacerbated by the combinatorics of the periodic table, since even a single crystal structure can in principle display millions of compositions. Consequently, there is a need for tools that enable a more guided exploration of the materials design space. Here, generative machine learning (ML) models have recently emerged as a promising technology. In this work, we assess the performance of a range of deep generative models based on Reinforcement Learning (RL), Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) for the prototypical case of designing Elpasolite compositions with low formation energies. By relying on the fully enumerated space of 2~million main group Elpasolites, the precision, coverage and diversity of the generated materials is rigorously assessed. Additionally, a hyperparameter selection scheme for generative models in chemical composition space is developed. Generative grammar ; Chemical space ; Space (punctuation) ; Computer science ; Hyperparameter ; Artificial intelligence ; Range (aeronautics) ; Machine learning ; Generative model ; Generative Design ; Composition (language) ; Reinforcement learning ; Adversarial system ; Theoretical computer science ; Engineering ; Biology ; Bioinformatics Machine Learning in Materials Science ; Topic Modeling
Authors: Yifei Wang | Shiyang Chen | Guobin Chen | Ethan Shurberg | Hang Liu | Pengyu Hong
Source: Preprints.org (preprint, 2022-12-05)
Field: Materials Science — Materials Chemistry

Motif-based Graph Representation Learning with Application to Chemical Molecules This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose Motif Convolution Module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM&amp;rsquo;s advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher-level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better in capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks. Computer science ; ENCODE ; Motif (music) ; Graph ; Theoretical computer science ; Feature learning ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Haibo Cui | Yuqi Zhou | Weiming Liu | Yangying Li | Zhijun Chen | Du He
Source: Electronics (article, 2022-07-28)
Field: Chemistry — Analytical Chemistry

TMCS-ENN: A Temporal Feature-Correlation Cuckoo Search-Elman Neural Network for Sugar Raw Materials Demands Prediction The prediction of the demand for raw materials is of vital importance to modern industries. Most studies are based on traditional regression, linear programming, and other methods. Previous studies have often overlooked the characteristics of the sugar raw materials business and the influence of time factors on raw material demand, resulting in limited prediction accuracy. How to accurately predict the demand for sugar raw materials is one of the key issues for intelligent management. In view of the above problems, combined with the characteristics of the supply and demand cycle of sugar raw materials, this paper aims to predict the demand for raw materials based on their supply and demand in a real sugar company by optimizing the Elman neural network through the modified cuckoo search (MCS) algorithm with temporal features. This study proposes a temporal feature-correlation cuckoo search–Elman neural network (TMCS-ENN) for predicting the demand for sugar raw materials. The experimental results show that the accuracy of the TMCS-ENN model reaches 93.89%, a better performance than that achieved by existing models. Therefore, the study model effectively improves the accuracy of the demand forecast of sugar raw materials for companies. This output will be helpful for improving the production efficiency and automation level, as well as reducing costs. Raw material ; Artificial neural network ; Cuckoo search ; Computer science ; Feature (linguistics) ; Artificial intelligence ; Sugar ; Raw data ; Demand forecasting ; Key (lock) ; Machine learning ; Data mining ; Engineering ; Operations research ; Particle swarm optimization Spectroscopy and Chemometric Analyses
Authors: Alexander Pomberger | Antonio Pedrina McCarthy | Ahmad Khan | Simon Sung | Connor J. Taylor | Matthew J. Gaunt | Lucy J. Colwell | Alexei A. Lapkin
Source: ChemRxiv (preprint, 2022-01-10)
Field: Materials Science — Materials Chemistry

The Effect of Chemical Representation on Active Machine Learning Towards Closed-Loop Optimization Multivariate chemical reaction optimization involving catalytic systems is a non-trivial task due to the high number of tuneable parameters and discrete choices. Closed-loop optimization featuring active Machine Learning (ML) represents a powerful strategy for automating reaction optimization. However, the translation of chemical reaction conditions into a machine-readable format comes with the challenge of finding highly informative features which accurately capture the factors for reaction success and allow the model to learn efficiently. Herein, we compare the efficacy of different calculated chemical descriptors for a high throughput generated dataset to determine the impact on a supervised ML model when predicting reaction yield. Then, the effect of featurization and size of the initial dataset within a closed-loop reaction optimization was examined. Finally, the balance between descriptor complexity and dataset size was considered. Ultimately, tailored descriptors did not outperform simple generic representations, however, a larger initial dataset accelerated reaction optimization. Computer science ; Machine learning ; Artificial intelligence ; Representation (politics) ; Throughput ; Task (project management) ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Rodolfo Keesey | Armi Tiihonen | Alexander E. Siemenn | Thomas W. Colburn | Shijing Sun | Noor Titan Putri Hartono | James Serdy | Margaret Zeile | Keqing He | Cole A. Gurtner | Austin C. Flick | Clio Batali | Alex Encinas | Richa R. Naik | Zhe Liu | Felipe Oviedo | Ian Marius Peters | Janak Thapa | Siyu Tian | Reinhold H. Dauskardt | Alexander J. Norquist | Tonio Buonassisi
Source: ChemRxiv (preprint, 2022-09-22)
Field: Materials Science — Materials Chemistry

An Open-Source Environmental Chamber for Materials-Stability Testing Using an Optical Proxy This study is motivated by the desire to disseminate a low-cost, high-precision, high-throughput environmental chamber to test materials and devices under elevated humidity, temperature, and light. This paper documents the creation of an open-source tool with a bill of materials as low as US$2,000, and the subsequent evolution of three second-generation tools installed at three different universities spanning thin films, bulk crystals, and thin-film solar-cell devices. We introduce an optical proxy measurement to detect real-time phase changes in materials. We present correlations between this optical proxy and standard X-ray diffraction measurements, describe some edge cases where the proxy measurement fails, and report key learnings from the technology-translation process. By sharing lessons learned, we hope that future open-hardware development and translation efforts can proceed with reduced friction. Throughout the paper, we provide examples of scientific impact, wherein participating laboratories used their environmental chambers to study and improve the stabilities of halide-perovskite materials. All generations of hardware bills of materials, assembly instructions, and operating codes are available in open-source repositories. Proxy (statistics) ; Dissemination ; Computer science ; Open source hardware ; Open source ; Materials science ; Engineering physics ; Process engineering ; Telecommunications ; Engineering ; Operating system ; Software Machine Learning in Materials Science
Authors: Guangqi Wu | Haisen Zhou | Jun Zhang | Zi‐You Tian | Xingyi Liu | Shuo Wang | Connor W. Coley | Hua Lu
Source: ChemRxiv (preprint, 2022-08-04)
Field: Chemistry — Organic Chemistry

A High-Throughput Platform for Efficient Exploration of Functional polypeptides Chemical Space Rapid and in-depth exploration of the chemical space of high molecular weight synthetic polypeptides via the ring-opening polymerization (ROP) of N-carboxyanhydride (NCA) is a viable approach towards protein mimics and functional biomaterials. The traditional synthetic workflow, however, has been labour-intensive and with limited throughput. Here, we develop an efficient chemistry for the high throughput diversification of polypeptides based on a click-like reaction between selenolate and various electrophiles in aqueous solutions. Importantly, the platform is amenable to automation, which allows rapid generation of up to 1200 homopolypeptides or random heteropolypeptides (RHP) within one day. With the assistance of machine learning, iterative exploration of the RHP library efficiently and effectively identifies candidates with improved glutathione peroxidase-like activity from complex chemical space of which we have little prior knowledge. This automated and high-throughput platform provides potential solutions to unmet challenges such as de novo design of artificial enzyme, biomacromolecule delivery, and understanding of intrinsically disordered proteins. Chemical space ; Throughput ; Computer science ; Workflow ; Chemistry ; Click chemistry ; On the fly ; Combinatorial chemistry ; Automation ; Nanotechnology ; Materials science ; Drug discovery ; Biochemistry ; Engineering Click Chemistry and Applications ; biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization