jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alba Aguilera | Miquel Albertí | Nardine Osman | Georgina Curto
Source: arXiv (Cornell University) (preprint, 2024-08-18)
Field: Chemistry — Organic Chemistry

Value-Enriched Population Synthesis: Integrating a Motivational Layer In recent years, computational improvements have allowed for more nuanced, data-driven and geographically explicit agent-based simulations. So far, simulations have struggled to adequately represent the attributes that motivate the actions of the agents. In fact, existing population synthesis frameworks generate agent profiles limited to socio-demographic attributes. In this paper, we introduce a novel value-enriched population synthesis framework that integrates a motivational layer with the traditional individual and household socio-demographic layers. Our research highlights the significance of extending the profile of agents in synthetic populations by incorporating data on values, ideologies, opinions and vital priorities, which motivate the agents' behaviour. This motivational layer can help us develop a more nuanced decision-making mechanism for the agents in social simulation settings. Our methodology integrates microdata and macrodata within different Bayesian network structures. This contribution allows to generate synthetic populations with integrated value systems that preserve the inherent socio-demographic distributions of the real population in any specific region. Value (mathematics) ; Layer (electronics) ; Population ; Mathematics ; Statistics ; Materials science ; Sociology ; Nanotechnology ; Demography Chemical synthesis and alkaloids
Authors: Christian Hagemeier | Giuseppe De Giacomo | Moshe Y. Vardi
Source: arXiv (Cornell University) (preprint, 2024-12-19)
Field: Materials Science — Polymers and Plastics

LTLf Synthesis Under Unreliable Input We study the problem of realizing strategies for an LTLf goal specification while ensuring that at least an LTLf backup specification is satisfied in case of unreliability of certain input variables. We formally define the problem and characterize its worst-case complexity as 2EXPTIME-complete, like standard LTLf synthesis. Then we devise three different solution techniques: one based on direct automata manipulation, which is 2EXPTIME, one disregarding unreliable input variables by adopting a belief construction, which is 3EXPTIME, and one leveraging second-order quantified LTLf (QLTLf), which is 2EXPTIME and allows for a direct encoding into monadic second-order logic, which in turn is worst-case nonelementary. We prove their correctness and evaluate them against each other empirically. Interestingly, theoretical worst-case bounds do not translate into observed performance; the MSO technique performs best, followed by belief construction and direct automata manipulation. As a byproduct of our study, we provide a general synthesis procedure for arbitrary QLTLf specifications. Computer science Synthesis and properties of polymers ; Advanced Control Systems Design ; Microwave Engineering and Waveguides
Authors: Klemens Fellner | Isabelle Gallagher | Pierre‐Emmanuel Jabin
Source: Oberwolfach Reports (article, 2024-04-18)
Field: Chemistry — Physical and Theoretical Chemistry

Classical and Quantum Mechanical Models of Many-Particle Systems The workshop focused on the collective behavior of many-particle systems in various application fields: physics (gas dynamics, plasmas, quantum mechanics), mathematical biology (cell mobility, evolution of trait-structured species), and social sciences (wealth distribution). This includes famous models such as the Boltzmann equation of gas dynamics, Vlasov equation for plasmas, Fokker–Planck equations, Smoluchowski and related equations, Keller–Segel system of chemotaxis. Quantum ; Particle (ecology) ; Physics ; Statistical physics ; Classical mechanics ; Theoretical physics ; Quantum mechanics ; Geology ; Oceanography Advanced Physical and Chemical Molecular Interactions
Authors: P. Rybacki | Przemysław Przygodziński | Przemysław Łukasz Kowalczewski | Z. Sawińska | I. Kowalik | Andrzej Osuch | Ewa Osuch
Source: Acta Scientiarum Polonorum Hortorum Cultus (article, 2024-11-30)
Field: Chemistry — Analytical Chemistry

Development and application of a model for the automatic evaluation and classification of onions (Allium cepa L.) using a Deep Neural Network (DNN) Evaluating onions for size, shape, damage, colour and discolouration is the first and most important step in classifying them for raw material quality, processing and the horticultural and agri-food sectors. Current methods of geometric evaluation and grading of onions involve mechanical and extremely invasive sorting, which causes additional damage, reduces the quality of the raw material and is also labour and time-consuming. As a result, non-invasive evaluation and classification methods that are both fast and accurate are being sought. One such method is digital image analysis, which, when combined with instrumentation and deep neural networks, can fully automate the process. The main aim of this study was the development of a model for the automatic evaluation and classification of onions using a deep convolutional neural network (CNN) model. A fixed-architecture network was built, for which a computational algorithm was developed in Python 3.9 and published at https://github.com/piotrrybacki/onion-CNN.git (accessed on 4 October 2024). The Hyduro F1 onion variety, a hybrid all-purpose variety of the Rijnsburger type, was used to build, teach and test the model. The developed algorithm classified the onion images qualitatively with an accuracy of 91.85%. This classification was based on the geometric parameters of the onion, i.e. diameter, height, transversal and longitudinal circumference, and the estimated area of damage or discolouration of the skin. The root mean square error (MSE) in RGB space varied between 87.99 and 91.24, and the maximum image classification time was 28.98 ms/image. The developed algorithm has a very high utility, as it automates the classification process, reducing its time and labour intensity. Allium ; Artificial neural network ; Artificial intelligence ; Computer science ; Mathematics ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Garlic and Onion Studies
Authors: Alireza Mansouri | A Aastha | Helen Wilding | N Mikoljewicz | Leonardo de Macêdo Filho | Michael Glantz | Thomas Kislinger
Source: Neuro-Oncology (article, 2024-10-01)
Field: Chemistry — Spectroscopy

P08.03.A CSF-BASED PROTEOMIC BIOMARKERS OF RESPONSE TO MULTI-AGENT INTRAVENTRICULAR CHEMOTHERAPY IN CNS LYMPHOMA Abstract BACKGROUND Despite promising responses to initial therapy for central nervous system lymphoma (CNSL), relapse is common and overall responses are disappointing compared to other non-CNS diffuse large B-cell lymphomas. Literature reports 1- and 3-year survival rates of 50% and 30-40% respectively, with standard treatment. Incorporation of multi-agent intrathecal chemotherapy (MAITC) alongside systemic therapy is novel but not widely adopted. Biomarkers predictive of response could help select patients most likely to benefit. Our retrospective analysis of CNSL patients treated with MAITC between 2015-2021 indicates a significant overall survival advantage. Cerebrospinal fluid (CSF) is a less invasive alternative for diagnosis and management of CNSL compared to traditional biopsies, while also reducing sampling bias. The objective of this study is to identify CSF-based proteomic biomarkers that can serve as reliable indicators of MAITC treatment response and guide CNSL management. MATERIAL AND METHODS Patient-matched CSF samples were banked at pre- and post-therapeutic endpoint in 22 primary and 36 secondary CNSL patients. The samples were profiled using high-throughput fluid sample protocol coupled with mass-spectrometry that only requires 30 microliters of CSF. Clinically, patients were categorized as “early responders” (objective imaging response and cytology clearance by cycle 6, both maintained until cycle 12) vs. “non responders”. Tumor burden (based on MRI and cytology) was correlated with proteomic markers to identify potential correlative markers. RESULTS SGCE, LCP1 and AGRN were found to reliably (AUROC 0.95, 95% CI 0.81-0.98, Sensitivity: 0.62, Specificity: 0.76) stratify patients based on MAITC response (responder vs. non-responder). Through analysis of matched pre- and post-treatment samples (&amp;gt;3 cycles) from the same patient cohort and correlation with brain MRI and CSF cytology, we identified H3F3A, YWHAE, LCP1, CA1 and GAPDH as candidate tumor burden biomarkers (AUROC 0.9, 95% CI 0.79-1.0). Conclusion: Our low input proteomic analyses of CSF has identified candidates potentially predictive of response to MAITC along with potential tumor burden markers. Validation studies with a larger cohort and additional timepoints are currently underway. Medicine ; Lymphoma ; Chemotherapy ; Oncology ; Pathology ; Internal medicine Advanced Proteomics Techniques and Applications ; Lymphoma Diagnosis and Treatment
Authors: Devendra Parkar | Kirtus G. Leyba | Raylene A. Faerber | Joshua J. Daymude
Source: arXiv (Cornell University) (preprint, 2024-04-09)
Field: Materials Science — Materials Chemistry

Evolving Collective Behavior in Self-Organizing Particle Systems Local interactions drive emergent collective behavior, which pervades biological and social complex systems. But uncovering the interactions that produce a desired behavior remains a core challenge. In this paper, we present EvoSOPS, an evolutionary framework that searches landscapes of stochastic distributed algorithms for those that achieve a mathematically specified target behavior. These algorithms govern self-organizing particle systems (SOPS) comprising individuals with no persistent memory and strictly local sensing and movement. For aggregation, phototaxing, and separation behaviors, EvoSOPS discovers algorithms that achieve 4.2-15.3% higher fitness than those from the existing "stochastic approach to SOPS" based on mathematical theory from statistical physics. EvoSOPS is also flexibly applied to new behaviors such as object coating where the stochastic approach would require bespoke, extensive analysis. Finally, we distill insights from the diverse, best-fitness genomes produced for aggregation across repeated EvoSOPS runs to demonstrate how EvoSOPS can bootstrap future theoretical investigations into SOPS algorithms for new behaviors. Collective behavior ; Particle (ecology) ; Particle system ; Self-organization ; Statistical physics ; Psychology ; Computer science ; Physics ; Sociology ; Artificial intelligence ; Social science ; Geology Pickering emulsions and particle stabilization
Authors: Raffaella Fiamma Cabini | Horacio Tettamanti | Mattia Zanella
Source: arXiv (Cornell University) (preprint, 2024-12-04)
Field: Chemistry — Physical and Theoretical Chemistry

Understanding the Impact of Evaluation Metrics in Kinetic Models for Consensus-based Segmentation In this article we extend a recently introduced kinetic model for consensus-based segmentation of images. In particular, we will interpret the set of pixels of a 2D image as an interacting particle system which evolves in time in view of a consensus-type process obtained by interactions between pixels and external noise. Thanks to a kinetic formulation of the introduced model we derive the large time solution of the model. We will show that the choice of parameters defining the segmentation task can be chosen from a plurality of loss functions characterising the evaluation metrics. Segmentation ; Computer science ; Econometrics ; Artificial intelligence ; Mathematics History and advancements in chemistry
Authors: Saaketh Desai | Sadhvikas Addamane | Rémi Dingreville | Igal Brener | Prasad P. Iyer
Source:  (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-driving lab discovers high-efficiency directional incoherent emission from reconfigurable semiconductor metasurfaces We discover high efficiency (77%) steering of incoherent emission from reconfigurable semiconductor metasurfaces by engineering the spatial refractive index profile of the metasurface resonators using autonomous experiments driven by generative models and active learning. Semiconductor ; Optoelectronics ; Materials science ; Physics ; Optics Metamaterials and Metasurfaces Applications ; Molecular Communication and Nanonetworks ; Energy Harvesting in Wireless Networks
Authors: Shayan Azizi | Ehsan Asadi | Shaun C. Howard | Benjamin W. Muir | Riley O’Shea | Alireza Bab‐Hadiashar
Source: arXiv (Cornell University) (preprint, 2024-02-19)
Field: Chemistry — Analytical Chemistry

Autonomous Hyperspectral Characterisation Station: Robotically Assisted Characterisation of Polymer Degradation This paper addresses the gap between the capabilities and utilisation of robotics and automation in laboratory settings and builds upon the concept of Self Driving Labs (SDL). %to significantly impact laboratory operations. We introduce an innovative approach to the temporal characterisation of materials. The article discusses the challenges posed by manual methods involving established laboratory equipment and presents an automated hyperspectral characterisation station. This station integrates robot-aided hyperspectral imaging, complex material characterisation modeling, and automated data analysis, offering a non-destructive and comprehensive approach. This work explains how the proposed assembly can automatically measure the half-life of biodegradable polymers with higher throughput and accuracy than manual methods. The investigation explores the effect of pH, number of average molecular weight (Mn), end groups, and blends on the degradation rate of polylactic acid (PLA). The contributions of the paper lie in introducing an adaptable classification station for novel characterisation methods and presenting an innovative methodology for polymer degradation rate measurement. The proposed system has the potential to accelerate the development of high-throughput screening and characterisation methods in material and chemistry laboratories. Hyperspectral imaging ; Degradation (telecommunications) ; Polymer ; Computer science ; Remote sensing ; Environmental science ; Artificial intelligence ; Materials science ; Geology ; Composite material ; Telecommunications Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Water Quality Monitoring and Analysis
Authors: Guangqi Wu | Tianyi Jin | Alfredo Alexander‐Katz | Connor W. Coley
Source: ChemRxiv (preprint, 2024-10-29)
Field: Chemistry — Organic Chemistry

Autonomous Discovery of Functional Random Heteropolymer Blends through Evolutionary Formulation Optimization While developing new polymers typically requires years of investigation, blending existing polymers offers a cost-effective strategy for creating new materials that meet specific requirements. Yet identifying functional polymer blends is often a laborious development process, complicated by the vast design space and non-additive nature of polymer properties, exacerbated by an often-limited understanding of structure-function relationships. To this end, we report an autonomous closed-loop platform with an evolutionary algorithm for the development of functional polymer blends. We focus on random heteropolymers (RHPs), which are gathering increasing interest as versatile materials with a range of promising applications. Using enzyme thermal stabilization as an objective, we identify blended compositions from combinatorial 96- or 192-dimensional spaces (with over 10^9 potential candidates) that exhibit emergent function and outperform all of their constituent polymers by an absolute margin of 26% retained enzyme activity. Our findings highlight the immense potential of leveraging autonomous closed-loop discovery platforms for polymer blend discovery, as well as the opportunity for materials discovery within the RHP blend space. The algorithmic goal of blend optimization also bears a strong resemblance to other formulation optimization problems that are pervasive in molecular and material discovery. Computer science ; Mathematical optimization ; Artificial intelligence ; Mathematics Advanced Polymer Synthesis and Characterization ; Chemistry and Chemical Engineering ; Design Education and Practice
Authors: Fabian O. von Rohr
Source: CHIMIA International Journal for Chemistry (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

The Changing Landscape of Materials Discovery In this perspective, we will discuss the impact of some of the most recent advancements in materials discovery, particularly focusing on the role of robotics, artificial intelligence, and self-driving laboratories, as well as their implications for the Swiss research landscape. While it seems timely to aim for broad, revolutionary breakthroughs in this field, we argue that more incremental steps - such as, for example, fully automatic grinding of solid powders or fully automated Rietveld refinements - may have a more significant impact on materials discovery, at least in the short run. In the center of these considerations is how small, interdisciplinary groups can drive significant progress by contributing targeted innovations, such as e.g.robotic sample preparation or computational predictions. Additionally, given the large investments that are necessary for future infrastructures in materials discovery, we discuss the potential case for the establishment - in the long run - of a national infrastructure, a Swiss Materials Discovery Lab, to support automated material synthesis and advanced characterization, ultimately accelerating innovation in both academic and industrial settings. Data science ; Field (mathematics) ; Robotics ; Perspective (graphical) ; Computer science ; Systems engineering ; Artificial intelligence ; Engineering ; Management science ; Nanotechnology ; Engineering management ; Robot Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Rajeev S. Assary
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Quantum-Chemistry Enabled AI for Materials Discovery for Energy Storage A priori and reliable simulations can enable timely and cost-efficient design and discovery of materials for energy. Therefore, ‘ Let’s Start from Computing ’ is an optimal approach to initialize modern day R&amp;D processes. In energy storage, beyond lithium-ion (BLI) research has the potential to revolutionize consumer electronics including portable and stationary power, transportation, and grid energy storage sectors. Multi-valent (Mg, Ca, Zn) energy storage or economically viable monovalent (Na, K) batteries, high-density metal-air, metal-sulfur batteries, or grid-storage systems are considered in the beyond lithium-ion research and development. All these R&amp;D efforts require significant fundamental knowledge via a priori computations for materials discovery, property prediction, and optimization . Atomistic modeling when coupled with reliable Artificial Intelligence (AI) approaches can provide accurate insights to accelerate discovery of optimal electrolytes, electrodes, and membranes for BLI systems to reduce the cost . Thus, coupled with AI and multi-scale simulations techniques, atomistic modeling can address prediction of molecular level properties of materials (redox potentials, solvation, spectroscopic, and reactivity) to down-select optimal materials or material combinations . In this presentation, I will describe some of our recent efforts in active learning coupled with large scale first principles simulations to down select/optimize desired molecules for flow battery technology. This concept can be utilized for design of experiments using autonomous experimentation. Additionally, I will describe some of our quantum chemistry-informed molecular property predictions redoxmers and liquid organic hydrogen carriers. In addition to molecules, I will present. A data driven approach to study longer time scale diffusion of ions for multivalent battery concepts. Figure 1 Nanotechnology ; Chemistry ; Materials science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Kaipeng Zheng | Weiran Huang | Wanli Ouyang | Han-Sen Zhong | Yuqiang Li
Source: arXiv (Cornell University) (preprint, 2024-10-17)
Field: Materials Science — Materials Chemistry

CrystalX: Ultra-Precision Crystal Structure Resolution and Error Correction Using Deep Learning Atomic structure analysis of crystalline materials is a paramount endeavor in both chemical and material sciences. This sophisticated technique necessitates not only a solid foundation in crystallography but also a profound comprehension of the intricacies of the accompanying software, posing a significant challenge in meeting the rigorous daily demands. For the first time, we confront this challenge head-on by harnessing the power of deep learning for ultra-precise structural analysis at the full-atom level. To validate the performance of the model, named CrystalX, we employed a vast dataset comprising over 50,000 X-ray diffraction measurements derived from authentic experiments, demonstrating performance that is commensurate with human experts and adept at deciphering intricate geometric patterns. Remarkably, CrystalX revealed that even peer-reviewed publications can harbor errors that are stealthy to human scrutiny, yet CrystalX adeptly rectifies them. This deep learning model revolutionizes the time frame for crystal structure analysis, slashing it down to seconds. It has already been successfully applied in the structure analysis of newly discovered compounds in the latest research without human intervention. Overall, CrystalX marks the beginning of a new era in automating routine structural analysis within self-driving laboratories. Resolution (logic) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Enzyme Structure and Function
Authors: Robert Waelder | Chiwoo Park | Arthur D. Sloan | Jennifer Carpena‐Núñez | Josh Yoho | Stéphane Gorsse | Rahul Rao | Benji Maruyama
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Improved Carbon Nanotube Growth Via Autonomous Jump Regression Targeting of Discontinuous Behavior Jump ; Carbon nanotube ; Materials science ; Nanotechnology ; Nanotube ; Chemical physics ; Regression ; Chemical engineering ; Biological system ; Chemistry ; Mathematics ; Biology ; Engineering ; Physics ; Statistics Machine Learning in Materials Science
Authors: Sofia Sivilotti | David Friday | Nicholas E. Jackson
Source: ChemRxiv (preprint, 2024-11-13)
Field: Chemistry — Physical and Theoretical Chemistry

Active Learning High Coverage Sets of Complementary Reaction Conditions Chemical reaction conditions capable of producing high yields over diverse reactants will be a key component of future self driving labs. While much work has been done to discover general reaction conditions, any single conditions are necessarily limited over increasingly diverse chemical spaces. A potential solution to this problem is to identify small sets of complementary reaction conditions that, when combined, cover a much larger chemical space than any one general reaction condition. In this work, we analyze experimentally derived datasets to assess the relative performance of individual general reaction conditions vs sets of complementary reaction conditions. We then propose and benchmark active learning methods to efficiently discover these complimentary sets of conditions. The results show the value of active learning in exploring sets of reaction conditions and provide an avenue for improving synthetic hit rates in high-throughput synthesis campaigns. Computer science ; Business Various Chemistry Research Topics ; Chemistry and Chemical Engineering ; Advanced Data Processing Techniques