jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yannic Rath | George H. Booth
Source: arXiv (Cornell University) (preprint, 2023-02-02)
Field: Materials Science — Materials Chemistry

A framework for efficient ab initio electronic structure with Gaussian Process States We present a general framework for the efficient simulation of realistic fermionic systems with modern machine learning inspired representations of quantum many-body states, towards a universal tool for ab initio electronic structure. These machine learning inspired ansatzes have recently come to the fore in both a (first quantized) continuum and discrete Fock space representations, where however the inherent scaling of the latter approach for realistic interactions has so far limited practical applications. With application to the 'Gaussian Process State', a recently introduced ansatz inspired by systematically improvable kernel models in machine learning, we discuss different choices to define the representation of the computational Fock space. We show how local representations are particularly suited for stochastic sampling of expectation values, while also indicating a route to overcome the discrepancy in the scaling compared to continuum formulated models. We are able to show competitive accuracy for systems with up to 64 electrons, including a simplified (yet fully ab initio) model of the Mott transition in three-dimensional hydrogen, indicating a significant improvement over similar approaches, even for moderate numbers of configurational samples. Fock space ; Ansatz ; Statistical physics ; Ab initio ; Scaling ; Gaussian ; Computer science ; Gaussian process ; Kernel (algebra) ; Representation (politics) ; Electronic structure ; Theoretical physics ; Theoretical computer science ; Physics ; Quantum mechanics ; Mathematics ; Discrete mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Gérliz Mercedes Gutiérrez-Finol | Silvia Giménez‐Santamarina | Ziqi Hu | Lorena E. Rosaleny | Salvador Cardona | Alejandro Gaita‐Ariño
Source: Research Square (preprint, 2023-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Lanthanide molecular nanomagnets as probabilistic bits Lanthanide ; Probabilistic logic ; Nanomagnet ; Computer science ; Nanotechnology ; Chemistry ; Materials science ; Physics ; Artificial intelligence ; Quantum mechanics ; Organic chemistry Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Catalysis and Oxidation Reactions
Authors: Giovanni Li Manni | Ignacio Fdez. Galván | Ali Alavi | Flavia Aleotti | Francesco Aquilante | Jochen Autschbach | Davide Avagliano | Alberto Baiardi | Jie Bao | Stefano Battaglia | Letitia Birnoschi | Alejandro Blanco-González | Sergey I. Bokarev | Ria Broer | Roberto Cacciari | Paul B. Calio | Rebecca K. Carlson | Rafael C. Couto | Luis Cerdán | Liviu F. Chibotaru | Nicolas F. Chilton | Jonathan R. Church | Irene Conti | Sonia Coriani | Juliana Cuéllar-Zuquin | Razan E. Daoud | Nike Dattani | Piero Decleva | Coen de Graaf | Mickaël G. Delcey | Luca De Vico | Werner Dobrautz | Sijia S. Dong | Rulin Feng | Nicolas Ferré | Michael Filatov | Laura Gagliardi | Marco Garavelli | Leticia González | Yafu Guan | Meiyuan Guo | Matthew R. Hennefarth | Matthew R. Hermes | Chad E. Hoyer | Miquel Huix‐Rotllant | Vishal Kumar Jaiswal | Andy Kaiser | Danil Kaliakin | Marjan Khamesian | Daniel S. King | Vladislav Kochetov | Marek Krośnicki | Arpit Arun Kumaar | Ernst D. Larsson | Susi Lehtola | Marie-Bernadette Lepetit | Hans Lischka | Pablo López Ríos | Marcus Lundberg | Dongxia Ma | Sebastian Mai | Philipp Marquetand | Isabella C. D. Merritt | Francesco Montorsi | Maximilian Mörchen | Artur Nenov | Nguyen Vu Ha Anh | Yoshio Nishimoto | Meagan S. Oakley | Massimo Olivucci | Markus Oppel | Daniele Padula | Riddhish Pandharkar | Quan Manh Phung | Felix Plasser | Gerardo Raggi | Elisa Rebolini | Markus Reiher | Ivan Rivalta | Daniel Roca‐Sanjuán | Thies Romig | Arta Anushirwan Safari | Aitor Sánchez-Mansilla | Andrew M. Sand | Igor Schapiro | Thais R. Scott | Javier Segarra‐Martí | Francesco Segatta | Dumitru‐Claudiu Sergentu | Prachi Sharma | Ron Shepard | Yinan Shu | Jakob K. Staab | Tjerk P. Straatsma | Lasse Kragh Sørensen | Bruno Nunes Cabral Tenorio | Donald G. Truhlar | Liviu Ungur | Morgane Vacher | Valera Veryazov
Source: ChemRxiv (preprint, 2023-04-12)
Field: Materials Science — Materials Chemistry

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry In this article the recent developments of the open-source OpenMolcas chemistry software environment, since spring 2020, are described, with the main focus on novel functionalities that are accessible in the stable branch of the package and/or via interfaces with other packages. These community developments span a wide range of topics in computational chemistry, and are presented in thematic sections associated with electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report represents a useful summary of these developments, and it offers a solid overview of the chemical phenomena and processes that OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations. Computer science ; Focus (optics) ; Electronic structure ; Software ; Computational science ; Nanotechnology ; Chemistry ; Data science ; Computational chemistry ; Physics ; Materials science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: M. V. Belyakov | E. A. Nikitin
Source: Хранение и переработка сельхозсырья (article, 2023-06-30)
Field: Chemistry — Analytical Chemistry

Сравнительная оценка спектральных люминесцентных характеристик молока и молочных продуктов Введение. Разработка аналитических методов контроля молока и молочных продуктов имеет важное значение для их хранения и переработки. Спектральный фотолюминесцентный метод контроля отличается высокой чувствительностью и селективностью, не требует химикатов в качестве расходного материала. Цель. Исследование спектральных характеристик фотолюминесценции молока и молочных продуктов для последующего создания методик их контроля. Материалы и методы. Измеряли спектральные люминесцентные характеристики и рассчитывали параметры молока, сметаны, творога и сливочного масла (Агрофирма «Катынь», Смоленская область) в диапазоне 200-600нм по ранее разработанной методике с использованием дифракционного спектрофлуориметра «Флюорат-02-Панорама». Результаты. Диапазон наибольшего возбуждения исследованных продуктов составил 220-340нм. Основные максимумы возбуждения 231, 262, 271, 288, 308 и 322нм. Для кисломолочных продуктов добавляется пик на 250нм. Спектры фотолюминесценции и интегральные параметры молока при скисании практически не меняются. При этом для коротковолнового возбуждения (262нм) как спектральные характеристики, так и интегральные потоки в два раза больше, чем для длинноволнового (442нм). Сравнивая потоки фотолюминесценции сметаны и молока видно, что при коротковолновом возбуждении для сметаны они примерно в два раза ниже, а при длинноволновом – примерно одинаковы, что согласуется со спектрами возбуждения. Для творога при всех использованных длинах волн возбуждения спектры получились качественно одинаковыми, но по интегральному потоку наилучшим является возбуждение 288нм. Предположительно, люминесценция больше при повышенном содержании белков и пониженном содержании жиров, что подтверждается исследованием фотолюминесценции сливочного масла. Выводы. Для возбуждения молока и кисломолочных продуктов наиболее целесообразным является использование длин волн возбуждения 262 нм (молоко), 271 нм (сметана) и 288 нм (творог). Для сливочного масла следует выбирать более длинноволновое возбуждение – 308нм. При этом фотолюминесцентное излучение следует измерять в диапазонах 290-400нм для молока, сметаны и творога, а для масла – в диапазоне 340-450нм. Полученные результаты могут быть применены для создания методик экспрессного контроля переработки и хранения молока и молочных продуктов. Philosophy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Scientific Research Methods
Authors: Alan E. Rask | Lee Huntington | Sungyeon Kim | David Walker | Andrew Wildman | Rodrigo Wang | Nicole Hazel | Alan Judi | James T. Pegg | Punit K. Jha | Zara Mayimfor | Carl Dukatz | Hassan Naseri | Ilan Gleiser | Maxime Hugues | Paul M. Zimmerman | Arman Zaribafiyan | Rudi Plesch | Takeshi Yamazaki
Source: arXiv (Cornell University) (preprint, 2023-07-20)
Field: Materials Science — Materials Chemistry

Massively parallel quantum chemistry: PFAS on over 1 million cloud vCPUs Accurate solutions to the electronic Schrödinger equation can provide valuable insight for electron interactions within molecular systems, accelerating the molecular design and discovery processes in many different applications. However, the availability of such accurate solutions are limited to small molecular systems due to both the extremely high computational complexity and the challenge of operating and executing these workloads on high-performance compute clusters. This work presents a massively scalable cloud-based quantum chemistry platform by implementing a highly parallelizable quantum chemistry method that provides a polynomial-scaling approximation to full configuration interaction (FCI). Our platform orchestrates more than one million virtual CPUs on the cloud to analyze the bond-breaking behaviour of carbon-fluoride bonds of per- and polyfluoroalkyl substances (PFAS) with near-exact accuracy within the chosen basis set. This is the first quantum chemistry calculation utilizing more than one million virtual CPUs on the cloud and is the most accurate electronic structure computation of PFAS bond breaking to date. Massively parallel ; Cloud computing ; Parallelizable manifold ; Computer science ; Scalability ; Quantum computer ; Computational science ; Quantum chemistry ; Quantum ; Scaling ; Theoretical computer science ; Parallel computing ; Chemistry ; Molecule ; Supramolecular chemistry ; Physics ; Quantum mechanics ; Algorithm ; Mathematics ; Database ; Organic chemistry Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Chemical Physics Studies
Authors: X. M. Zhang | Xiaoxiang Hou | Yiming Su | XiaoBin Yan | Xingxing Qiao | Wude Yang | Meichen Feng | Huihua Kong | Zhou Zhang | Fahad Shafiq | Wenjie Han | Guangxin Li | Ping Chen | Chao Wang
Source: Research Square (preprint, 2023-05-08)
Field: Chemistry — Analytical Chemistry

Analyzing protein concentration from intact wheat caryopsis using hyperspectral reflectance Abstract Winter wheat grain samples from 185 sites across southern Shanxi region were processed and analyzed by using a non-destructive approach. For this purpose, spectral data and protein content data of grain and grain powder were obtained. After combining six types of pre-processed spectra and four types of multivariate statistical models, a relationship hyperspectral datasets and grain protein content is presented. It was found that the hyperspectral reflectance of winter wheat grain and powder was positively correlated with the protein contents, which provide the possibility for hyperspectral quantitative assessment. The spectral characteristic bands of protein content in winter wheat extracted based on the SPA algorithm were proved to be around 350–430 nm; 851–1154 nm; 1300–1476 nm; and 1990–2050 nm. In powder samples, SG-BPNN had the best monitoring effect, with the accuracy of Rv 2 = 0.814, RMSEv = 0.024, and RPDv = 2.318. While in case of grain samples, the SG-SVM model exhibited the best monitoring effect, with the accuracy of Rv 2 = 0.789, RMSEv = 0.026, and RPDv = 2.177. Based on the experimental findings, we propose that a combination of spectral pretreatment and multivariate statistical modeling is helpful for the non-destructive and rapid estimation of protein content in winter wheat. Hyperspectral imaging ; Multivariate statistics ; Grain size ; Winter wheat ; Reflectivity ; Content (measure theory) ; Mathematics ; Biological system ; Analytical Chemistry (journal) ; Chemistry ; Environmental science ; Agronomy ; Materials science ; Remote sensing ; Biology ; Statistics ; Environmental chemistry ; Physics ; Geology ; Optics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Remote Sensing in Agriculture
Authors: Laura Andreozzi | Elisa Martinelli
Source: Polymers (article, 2023-10-23)
Field: Materials Science — Materials Chemistry

An Electron Spin Resonance Study Comparing Nanometer–Nanosecond Dynamics in Diblock Copolymers and Their Poly(methyl Methacrylate) Binary Blends Block copolymers are a class of materials that are particularly interesting with respect to their capability to self-assemble in ordered structures. In this context, the coupling between environment and dynamics is particularly relevant given that movements at the molecular level influence various properties of macromolecules. Mixing the polymer with a second macromolecule appears to be an easy method for studying these relationships. In this work, we studied blends of poly(methyl methacrylate) (PMMA) and a block copolymer composed of PMMA as the first block and poly(3-methyl-4-[6-(methylacryloyloxy)-hexyloxy]-4'-pentyloxy azobenzene) as the second block. The relaxational properties of these blends were investigated via electron spin resonance (ESR) spectroscopy, which is sensitive to nanometric length scales. The results of the investigations on the blends were related to the dynamic behavior of the copolymers. At the nanoscale, the study revealed the presence of heterogeneities, with slow and fast dynamics available for molecular reorientation, which are further modulated by the ability of the block copolymers to form supramolecular structures. For blends, the heterogeneities at the nanoscale were still detected. However, it was observed that the presence of the PMMA as a major component of the blends modified their dynamic behavior. Copolymer ; Materials science ; Polymer blend ; Context (archaeology) ; Polymer ; Methyl methacrylate ; Molecular dynamics ; Nanosecond ; Chemical physics ; Micelle ; Supramolecular chemistry ; Nanoscopic scale ; Nanometre ; Polymer chemistry ; Methacrylate ; Electron paramagnetic resonance ; Chemical engineering ; Nanotechnology ; Molecule ; Composite material ; Physical chemistry ; Chemistry ; Computational chemistry ; Nuclear magnetic resonance ; Organic chemistry ; Optics ; Physics Block Copolymer Self-Assembly ; Liquid Crystal Research Advancements ; Advanced Polymer Synthesis and Characterization
Authors: Rweetuparna Guha | Anna Gonzàlez‐Rosell | Malak Rafik | Nery Arevalos | Benjamin B. Katz | Stacy M. Copp
Source: ChemRxiv (preprint, 2023-06-02)
Field: Materials Science — Materials Chemistry

Electron count and ligand composition influence the optical and chiroptical signatures of NIR-emissive DNA-stabilized silver nanoclusters Near-infrared (NIR) emissive DNA-stabilized silver nanoclusters (AgN-DNAs) are promising fluorophores in the biological tissue transparency windows. Hundreds of NIR-emissive AgN-DNAs have been recently discovered, but their structure-property relationships remain poorly understood. Here, we investigate 19 different far-red and NIR emissive AgN-DNA species stabilized by 10-base DNA templates, including several well-studied emitters whose compositions and chiroptical properties have never been reported before. The molecular formula of each purified species is determined by high-resolution mass spectrometry and correlated to its optical absorbance, emission, and circular dichroism (CD) spectra. We find that AgN-DNAs emissive at the far red/NIR spectral border are either 6-electron clusters or 8-electron clusters. 8-electron AgN-DNAs are stabilized by two DNA oligomer copies, while three different ligand compositions are observed for 6-electron AgN-DNAs: two oligomer copies, three oligomer copies, or two oligomer copies with additional chlorido ligands. Distinct optical and chiroptical signatures correlate with ligand composition of 6-electron AgN-DNAs. AgN-DNAs with three oligomer ligands exhibit shorter Stokes shifts than AgN-DNAs with two oligomers, and the presence of chlorido ligands is correlated with increased Stokes shifts and significantly suppressed visible CD transitions. Nanocluster electron count also significantly influences electronic structure and optical properties, with 6-electron and 8-electron AgN-DNAs exhibiting distinct absorbance and CD spectral features. This study shows that the optical and chiroptical properties of NIR-emissive AgN-DNAs are highly sensitive to nanocluster composition and illustrates the diversity of structure-property relationships for NIR-emissive AgN-DNAs, which could be harnessed to precisely tune these emitters for bioimaging applications. Nanoclusters ; Oligomer ; Absorbance ; Circular dichroism ; DNA ; Chemistry ; Crystallography ; Photochemistry ; Materials science ; Analytical Chemistry (journal) ; Organic chemistry ; Chromatography Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Jamie W. Gittins | Yuan Chen | Stefanie Arnold | Veronica Augustyn | Andrea Balducci | Thierry Brousse | Elżbieta Frąckowiak | Pedro Gómez‐Romero | Archana Kanwade | Lukas Köps | Plawan Kumar Jha | Dongxun Lyu | Michele Meo | Deepak Pandey | Le Pang | Volker Presser | Mario Rapisarda | Daniel Rueda-García | Saeed Saeed | Parasharam M. Shirage | Adam Ślesiński | Francesca Soavi | Jayan Thomas | Maria‐Magdalena Titirici | Hongxia Wang | Zhen Xu | Aiping Yu | Maiwen Zhang | Alexander C. Forse
Source: ChemRxiv (preprint, 2023-07-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interlaboratory Study Assessing the Analysis of Supercapacitor Electrochemistry Data Supercapacitors are fast-charging energy storage devices of great importance for developing robust and climate-friendly energy infrastructures for the future. Research in this field has seen rapid growth in recent years. Therefore, consistent reporting practices must be implemented to enable reliable comparison of device performance. Although several studies have highlighted the best practices for analysing and reporting data from such energy storage devices, there is yet to be an empirical study investigating whether researchers in the field are correctly implementing these recommendations, and which assesses the variation in reporting between different laboratories. Here, we address this deficit by carrying out the first interlaboratory study of the analysis of supercapacitor electrochemistry data. We find that the use of incorrect formulae and researchers having different interpretations of key terminologies are the primary causes of variability in data reporting. Furthermore, we highlight the more significant variation in reported results for electrochemical profiles showing non-ideal capacitive behaviour. From the insights gained through this study, we make additional recommendations to the community to help ensure consistent reporting of performance metrics moving forward. Supercapacitor ; Computer science ; Data science ; Field (mathematics) ; Risk analysis (engineering) ; Business ; Electrochemistry Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Neetu Rani | S. K. Garg | Kiran Bamel | Vaibhav Bhatt | S. P. Sharma | Shashvat Kumar Mishra | Nitesh Saini | Saloni Parmar
Source: Plant Science Today (review, 2023-12-28)
Field: Chemistry — Analytical Chemistry

A Systematic Review and Comparative Meta-analysis of Non-destructive Fruit Maturity Detection Techniques The global fruit industry is growing rapidly due to increased awareness of the health benefits associated with fruit consumption. Fruit maturity detection plays a crucial role in fruit logistics and maintenance, enabling farmers and fruit industries to grade fruits and develop sustainable policies for enhanced profitability and service quality. Non-destructive fruit maturity detection methods have gained significant attention, especially with advancements in machine vision and spectroscopic techniques. This systematic review provides a concise overview of the techniques and algorithms used in fruit quality grading by farmers and industries. The study reviewed 63 full-text articles published between 2012 and 2023 along with their bibliometric analysis. Qualitative analysis revealed that researchers from various disciplines contributed to this field, with techniques falling into 3 categories: machine vision (mathematical modelling or deep learning), spectroscopy and other miscellaneous approaches. There was a high level of diversity among these categories, as indicated by an I-square value of 88.37% in the heterogeneity analysis. Meta-analysis, using odds ratios as the effect measure, established the relationship between techniques and their accuracy. Machine vision showed a positive correlation with accuracy across different categories. Additionally, Egger's and Begg's tests were used to assess publication bias and no strong evidence of its occurrence was found. This study offers valuable insights into the advantages and limitations of various fruit maturity detection techniques. For employing statistical and meta-analytical methods, key factors such as accuracy and sample size have been considered. These findings will aid in the development of effective strategies for fruit quality assessment. Grading (engineering) ; Maturity (psychological) ; Funnel plot ; Publication bias ; Meta-analysis ; Profitability index ; Bibliometrics ; Computer science ; Statistics ; Artificial intelligence ; Mathematics ; Business ; Odds ratio ; Engineering ; Data mining ; Psychology ; Medicine Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Smart Agriculture and AI
Authors: Joel Davidsson | Mykyta Onizhuk | Christian Vorwerk | Giulia Galli
Source: arXiv (Cornell University) (preprint, 2023-02-15)
Field: Materials Science — Materials Chemistry

Discovery of Atomic Clock-Like Spin Defects in Simple Oxides from First Principles Virtually noiseless due to the scarcity of spinful nuclei in the lattice, simple oxides hold promise as hosts of solid-state spin qubits. However, no suitable spin defect has yet been found in these systems. Using high-throughput first-principles calculations, we predict spin defects in calcium oxide with electronic properties remarkably similar to those of the NV center in diamond. These defects are charged complexes where a dopant atom -- Sb, Bi, or I -- occupies the volume vacated by adjacent cation and anion vacancies. The predicted zero phonon line shows that the Bi complex emits in the telecommunication range, and the computed many-body energy levels suggest a viable optical cycle required for qubit initialization. Notably, the high-spin nucleus of each dopant strongly couples to the electron spin, leading to many controllable quantum levels and the emergence of atomic clock-like transitions that are well protected from environmental noise. Specifically, the Hanh-echo coherence time increases beyond seconds at the clock-like transition in the defect with \ch{^{209}Bi}. Our results pave the way to designing quantum states with long coherence times in simple oxides, making them attractive platforms for quantum technologies. Qubit ; Physics ; Coherence (philosophical gambling strategy) ; Quantum technology ; Condensed matter physics ; Quantum computer ; Hyperfine structure ; Quantum ; Atomic physics ; Chemistry ; Quantum mechanics ; Open quantum system Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Electronic and Structural Properties of Oxides