jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew Stasiuk | Pai Peng | Garrett Heller | Paola Cappellaro
Source: arXiv (Cornell University) (preprint, 2023-11-27)
Field: Chemistry — Spectroscopy

Frame Change Technique for Phase Transient Cancellation The precise control of complex quantum mechanical systems can unlock applications ranging from quantum simulation to quantum computation. Controlling strongly interacting many-body systems often relies on Floquet Hamiltonian engineering that is achieved by fast switching between Hamiltonian primitives via external control. For example, in our solid-state NMR system, we perform quantum simulation by modulating the natural Hamiltonian with control pulses. As the Floquet heating errors scale with the interpulse delay, $δt$, it is favorable to keep $δt$ as short as possible, forcing our control pulses to be short duration and high power. Additionally, high-power pulses help to minimize undesirable evolution from occurring during the duration of the pulse. However, such pulses introduce an appreciable phase-transient control error, a form of unitary error. In this work, we detail our ability to diagnose the error, calibrate its magnitude, and correct it for $π/2$-pulses of arbitrary phase. We demonstrate the improvements gained by correcting for the phase transient error, using a method which we call the ``frame-change technique'', in a variety of experimental settings of interest. Given that the correction mechanism adds no real control overhead, we recommend that any resonance probe be checked for these phase transient control errors, and correct them using the frame-change technique. Hamiltonian (control theory) ; Floquet theory ; Control theory (sociology) ; Computer science ; Transient (computer programming) ; Computation ; Quantum ; Quantum computer ; Physics ; Algorithm ; Quantum mechanics ; Mathematics ; Control (management) Advanced NMR Techniques and Applications ; Quantum and electron transport phenomena ; Radio Frequency Integrated Circuit Design
Authors: A. Saita | Jefferson Fernando Firmino Gomes | Laura Santana Guimarães Carmero | Gustavo Henrique Pinheiro Cotrim | João Miguel Rabeschini | Sidnei Dias Silva | Denise Rodrigues Bueno | Érico Torrieri
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-08-30)
Field: Chemistry — Organic Chemistry

Farmacogenética do gene cyp2d6 no metabolismo da codeína Farmacogenética é um termo cunhado nos anos 50 e se relaciona com o fato que a variabilidade no DNA, resultado de mutações naturais, seleção natural e reprodução sexuada, podem interferir no metabolismo dos fármacos. Duas vias são de grande importância nessa “área”: farmacocinética, que estuda como um fármaco é absorvido, entregue ao tecido alvo, metabolizado e excretado; e farmacodinâmica, que descreve a ação do fármaco. O gene citocromo p450 2d6 (cyp2d6) está envolvido no metabolismo de diversos fármacos e já foram descritas mais de 100 variações polimórficas que resultam desde perda de função ao aumento no metabolismo dos fármacos. Neste artigo, realizamos uma revisão bibliográfica sobre cyp2d6 e sua ação no metabolismo da codeína.. Code (set theory) ; Gene ; Biology ; Molecular biology ; Genetics ; Programming language ; Computer science Chemical synthesis and alkaloids ; Chemical Reactions and Isotopes
Authors: Eva Perlt | Jamoliddin Khanifaev | Tim Schrader
Source: Nachrichten aus der Chemie (article, 2023-10-31)
Field: Chemistry — Physical and Theoretical Chemistry

Trendbericht Theoretische Chemie 2023 (2/3): Berechnungen von Molekülschwingungen in der Thermodynamik Abstract Maschinelles Lernen: Bisher zielten Anwendungen vor allem auf das Lernen von Potenzialenergiehyperflächen, derzeit zeigen sich viele neue Forschungsrichtungen. Molekülschwingungen in der Thermodynamik: Berechnung in Theorie und Simulation. Ultrakurze Laserpulse zum Beobachten molekularer Dynamik: Da die Bewegung von Elektronen genauso schnell ist wie die zeitliche Ausdehnung der Pulse, sind die Pulseigenschaften bei Simulationen explizit zu berücksichtigen. Physics ; Humanities ; Philosophy Various Chemistry Research Topics ; Spectroscopy and Quantum Chemical Studies ; Machine Learning in Materials Science
Authors: Kiarash Jamali | Lukas Käll | Rui Zhang | Alan Brown | Dari Kimanius | Sjors H. W. Scheres
Source: Nature (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Automated model building and protein identification in cryo-EM maps Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs<sup>1,2</sup>. Here we present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality to those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy to those built by humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will therefore remove bottlenecks and increase objectivity in cryo-EM structure determination. Identification (biology) ; Computational biology ; Cryo-electron microscopy ; Computer science ; Data science ; Biology ; Ecology ; Biophysics Enzyme Structure and Function ; Genomics and Phylogenetic Studies ; Glycosylation and Glycoproteins Research
Authors: Ahmed I. Osman | Yubin Zhang | Mohamed Farghali | Ahmed K. Rashwan | Abdelazeem S. Eltaweil | Eman M. Abd El-Monaem | Israa M. A. Mohamed | Mai M. Badr | Ikko Ihara | David W. Rooney | Pow‐Seng Yap
Source: Environmental Chemistry Letters (review, 2024-01-20)
Field: Materials Science — Materials Chemistry

Synthesis of green nanoparticles for energy, biomedical, environmental, agricultural, and food applications: A review Abstract Nanomaterials have been rapidly developed during the last decades, yet many nanoparticles synthesized by classical methods are toxic and their synthesis procedure is not sustainable. Here we review the green synthesis of nanoparticles from biomass and waste with a focus on synthetic mechanisms and applications in energy production and storage, medicine, environmental remediation, and agriculture and food. Biomass use for synthesis include microorganisms, fungi, plants, and agro-industrial bio-waste. Compared to conventional synthesis, green synthesis allows a 30% reduction in energy consumption, cost savings of up to 40%, and a 50% increase in production output. Biomedical applications comprise antibacterials, anticancers, antioxidants, and drug delivery mechanisms. Carbon quantum dots and photovoltaics are discussed in the energy section. Agricultural and food applications focus on nanofertilization, pest control, and food quality. Environmental remediation includes water and soil purification. Biomass (ecology) ; Agriculture ; Environmental remediation ; Environmental science ; Food processing ; Biotechnology ; Waste management ; Nanotechnology ; Chemistry ; Materials science ; Engineering ; Contamination ; Food science ; Agronomy ; Ecology ; Biology Carbon and Quantum Dots Applications ; Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: John Dagdelen | Alexander Dunn | Sang‐Hoon Lee | Nicholas Walker | Andrew Rosen | Gerbrand Ceder | Kristin A. Persson | Anubhav Jain
Source: Nature Communications (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Structured information extraction from scientific text with large language models Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers. Computer science ; Relationship extraction ; Information extraction ; Task (project management) ; Information retrieval ; JSON ; Natural language processing ; Simple (philosophy) ; Knowledge extraction ; Artificial intelligence ; World Wide Web Machine Learning in Materials Science ; Topic Modeling ; Biomedical Text Mining and Ontologies
Authors: Andres M. Bran | Sam Cox | Oliver Schilter | Carlo Baldassari | Andrew Dickson White | Philippe Schwaller
Source: Nature Machine Intelligence (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

Augmenting large language models with chemistry tools Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent and three organocatalysts and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow's effectiveness in automating a diverse set of chemical tasks. Our work not only aids expert chemists and lowers barriers for non-experts but also fosters scientific advancement by bridging the gap between experimental and computational chemistry. Limiting ; Bridging (networking) ; Computer science ; Set (abstract data type) ; Drug discovery ; Nanotechnology ; Data science ; Chemistry ; Engineering ; Programming language Machine Learning in Materials Science ; Topic Modeling ; Software Engineering Research
Authors: Hanqun Cao | Cheng Tan | Zhangyang Gao | Yilun Xu | Guangyong Chen | Pheng‐Ann Heng | Stan Z. Li
Source: IEEE Transactions on Knowledge and Data Engineering (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

A Survey on Generative Diffusion Models Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion models, recognized as one of the paramount generative models, materialize human ideation into tangible instances across diverse domains, encompassing imagery, text, speech, biology, and healthcare. To provide advanced and comprehensive insights into diffusion, this survey comprehensively elucidates its developmental trajectory and future directions from three distinct angles: the fundamental formulation of diffusion, algorithmic enhancements, and the manifold applications of diffusion. Each layer is meticulously explored to offer a profound comprehension of its evolution. Structured and summarized approaches are presented here. Computer science ; Generative grammar ; Creativity ; Generative model ; Realm ; Diffusion ; Computational creativity ; Artificial intelligence ; Data science ; Comprehension ; Cognitive science ; Psychology Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis
Authors: Philipp Pracht | Stefan Grimme | Christoph Bannwarth | Fabian Bohle | Sebastian Ehlert | Gereon Feldmann | Johannes Gorges | Marcel Müller | Tim Stauch | Christoph Plett | Sebastian Spicher | Pit Steinbach | Patryk A. Wesołowski | Felix Zeller
Source: The Journal of Chemical Physics (article, 2024-03-21)
Field: Chemistry — Physical and Theoretical Chemistry

CREST—A program for the exploration of low-energy molecular chemical space Conformer-rotamer sampling tool (CREST) is an open-source program for the efficient and automated exploration of molecular chemical space. Originally developed in Pracht et al. [Phys. Chem. Chem. Phys. 22, 7169 (2020)] as an automated driver for calculations at the extended tight-binding level (xTB), it offers a variety of molecular- and metadynamics simulations, geometry optimization, and molecular structure analysis capabilities. Implemented algorithms include automated procedures for conformational sampling, explicit solvation studies, the calculation of absolute molecular entropy, and the identification of molecular protonation and deprotonation sites. Calculations are set up to run concurrently, providing efficient single-node parallelization. CREST is designed to require minimal user input and comes with an implementation of the GFNn-xTB Hamiltonians and the GFN-FF force-field. Furthermore, interfaces to any quantum chemistry and force-field software can easily be created. In this article, we present recent developments in the CREST code and show a selection of applications for the most important features of the program. An important novelty is the refactored calculation backend, which provides significant speed-up for sampling of small or medium-sized drug molecules and allows for more sophisticated setups, for example, quantum mechanics/molecular mechanics and minimum energy crossing point calculations. Crest ; Space (punctuation) ; Chemical space ; Space program ; Energy (signal processing) ; Astrobiology ; Geology ; Computer science ; Engineering physics ; Physics ; Biology ; Optics ; Bioinformatics Various Chemistry Research Topics ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Walaa Abd‐Elaziem | Moustafa A. Darwish | Atef Hamada | Walid M. Daoush
Source: Materials & Design (review, 2024-03-13)
Field: Materials Science — Materials Chemistry

Titanium-Based alloys and composites for orthopedic implants Applications: A comprehensive review The increasing demand for orthopedic implants has driven the search for materials that combine strength, biocompatibility, and long lifetime. Compared to stainless steel and Co-Cr-based alloys, titanium (Ti) and its alloys are favored for biomedical implants because of their high strength, corrosion resistance, and biocompatibility. This comprehensive review delivers a wide overview of the field of titanium-based biomaterials for orthopedic implants applications, focusing on their types, mechanical and chemical resistance, surface modifications, innovations in fabrication techniques, titanium matrix composites, and machine learning advancements. Titanium alloys of different crystalline phases, including α, near-α, (α + β), β, and shape memory alloys, offer diverse options for orthopedic applications. Strengthening properties, wear, fatigue, and corrosion resistance are crucial factors influencing the performance and reliability of titanium implants. Moreover, this review discussed the challenges to titanium-based biomaterial durability through surface modifications to enhance their biofunction, wear resistance, corrosion resistance, and antibacterial properties. Recent developments in fabrication techniques for titanium-based biomaterials are also discussed. Eventually, this review investigated how machine learning (ML) revolutionized titanium orthopedic implants by providing insights into the behavior of new alloys, aiding in manufacturing optimization, allowing for real-time quality control, and advancing the development of personalized, biocompatible, and reliable implants. Materials science ; Titanium ; Composite material ; Orthopedic surgery ; Titanium alloy ; Metallurgy ; Alloy ; Medicine Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials ; Orthopaedic implants and arthroplasty
Authors: Yingjie Hang | Anyang Wang | Nianqiang Wu
Source: Chemical Society Reviews (review, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic silver and gold nanoparticles: shape- and structure-modulated plasmonic functionality for point-of-caring sensing, bio-imaging and medical therapy Silver and gold nanoparticles have found extensive biomedical applications due to their strong localized surface plasmon resonance (LSPR) and intriguing plasmonic properties. This review article focuses on the correlation among particle geometry, plasmon properties and biomedical applications. It discusses how particle shape and size are tailored <i>via</i> controllable synthetic approaches, and how plasmonic properties are tuned by particle shape and size, which are embodied by nanospheres, nanorods, nanocubes, nanocages, nanostars and core-shell composites. This article summarizes the design strategies for the use of silver and gold nanoparticles in plasmon-enhanced fluorescence, surface-enhanced Raman scattering (SERS), electroluminescence, and photoelectrochemistry. It especially discusses how to use plasmonic nanoparticles to construct optical probes including colorimetric, SERS and plasmonic fluorescence probes (labels/reporters). It also demonstrates the employment of Ag and Au nanoparticles in polymer- and paper-based microfluidic devices for point-of-care testing (POCT). In addition, this article highlights how to utilize plasmonic nanoparticles for <i>in vitro</i> and <i>in vivo</i> bio-imaging based on SERS, fluorescence, photoacoustic and dark-field models. Finally, this article shows perspectives in plasmon-enhanced photothermal and photodynamic therapy. Plasmon ; Nanotechnology ; Colloidal gold ; Nanoparticle ; Materials science ; Plasmonic nanoparticles ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced Nanomaterials in Catalysis
Authors: Ying Xia | Ming Sun | Hai Huang | Weilin Jin
Source: Signal Transduction and Targeted Therapy (review, 2024-04-18)
Field: Materials Science — Biomaterials

Drug repurposing for cancer therapy Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application. Drug repositioning ; Repurposing ; Medicine ; Drug ; Context (archaeology) ; Cancer ; Drug development ; Intensive care medicine ; Pharmacology ; Internal medicine ; Biology Nanoparticle-Based Drug Delivery ; Protein Degradation and Inhibitors ; Cancer Cells and Metastasis
Authors: Arseniy I. Kuznetsov | Mark L. Brongersma | Jin Yao | Mu Ku Chen | Uriel Levy | Din Ping Tsai | Nikolay I. Zheludev | Andrei Faraon | Amir Arbabi | Nanfang Yu | Debashis Chanda | Kenneth B. Crozier | Alexander V. Kildishev | Hao Wang | Joel K. W. Yang | Jason Valentine | Patrice Genevet | Jonathan A. Fan | Owen D. Miller | Arka Majumdar | Johannes E. Fröch | David J. Brady | Felix Heide | Ashok Veeraraghavan | Nader Engheta | Andrea Alù | Albert Polman | Harry A. Atwater | Prachi Thureja | Ramón Paniagua‐Domínguez | Son Tung Ha | Ángela Barreda | Jon A. Schuller | Isabelle Staude | Gustavo Grinblat | Yuri S. Kivshar | Samuel Peana | Susanne F. Yelin | Alexander Senichev | Vladimir M. Shalaev | Soham Saha | Alexandra Boltasseva | Junsuk Rho | Dong Kyo Oh | Joohoon Kim | Junghyun Park | Robert C. Devlin | Ragip Pala
Source: ACS Photonics (article, 2024-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Roadmap for Optical Metasurfaces Metasurfaces have recently risen to prominence in optical research, providing unique functionalities that can be used for imaging, beam forming, holography, polarimetry, and many more, while keeping device dimensions small. Despite the fact that a vast range of basic metasurface designs has already been thoroughly studied in the literature, the number of metasurface-related papers is still growing at a rapid pace, as metasurface research is now spreading to adjacent fields, including computational imaging, augmented and virtual reality, automotive, display, biosensing, nonlinear, quantum and topological optics, optical computing, and more. At the same time, the ability of metasurfaces to perform optical functions in much more compact optical systems has triggered strong and constantly growing interest from various industries that greatly benefit from the availability of miniaturized, highly functional, and efficient optical components that can be integrated in optoelectronic systems at low cost. This creates a truly unique opportunity for the field of metasurfaces to make both a scientific and an industrial impact. The goal of this Roadmap is to mark this "golden age" of metasurface research and define future directions to encourage scientists and engineers to drive research and development in the field of metasurfaces toward both scientific excellence and broad industrial adoption. Holography ; Computer science ; Pace ; Field (mathematics) ; Polarimetry ; Virtual reality ; Optical engineering ; Nanotechnology ; Physics ; Optics ; Materials science ; Human–computer interaction Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications