jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zdeněk Vacek | Stanislav Vacek | Jakub Černý | Jan Cukor | Michal Kuběnka | Josef Gallo | Václav Trojan | Ivan Lukáčik | Igor Štefančík | Ivo Králíček
Source: Frontiers in Forests and Global Change (article, 2026-01-14)
Field: Chemistry — Organic Chemistry

Sycamore maple (Acer pseudoplatanus L.) and global climate change: a new perspective for sustainable forestry Global climate change is a major challenge for forestry because it fundamentally alters the growth conditions of tree species. Sycamore maple ( Acer pseudoplatanus L.), often overlooked in forest management, is increasingly recognized as a valuable species for sustainable forestry in Central Europe. This tree species offers a unique combination of high economic and ecological value, characterized by rapid juvenile growth, abundant natural regeneration, and adaptability to diverse environmental conditions, including mountainous regions. Its contribution to biodiversity, nutrient cycling and soil stabilization underlines its ecological importance. Despite its many advantages, sycamore maple faces significant threats from global climate change, such as prolonged drought and increased susceptibility to pathogens. Nevertheless, its genetic diversity and phenotypic plasticity allow it to thrive in diverse habitats, including areas affected by human activities. This review synthesizes current knowledge of the species distribution, site requirements, silviculture, and associated threats and provides an economic evaluation of sycamore maple wood assortments. Its wood is highly valued for its exceptional versatility and quality, underlining its economic attractiveness. To maximize its potential, effective forest management practices are essential. These include strategies such as establishing mixed stands and implementing careful regeneration techniques to ensure the species’ resilience in the face of climate change. By incorporating ecological, economic, and climate-resilience perspectives, this review demonstrates the vital role of sycamore maple in sustainable forestry and biodiversity conservation in Central Europe. The results highlight its ecological and adaptive capacity and economic viability as a resource for future forest ecosystems. Climate change ; Biodiversity ; Sustainability ; Global warming ; Psychological resilience ; Sustainable development ; Ecoforestry ; Sustainable forest management Plant-Derived Bioactive Compounds ; Botanical Studies and Applications ; Tree-ring climate responses
Authors: Akli Younsi | Armelle Nouviaire | Rafik Co-Encadreur Belarbi
Source: Results in Engineering (article, 2026-02-25)
Field: Materials Science — Polymers and Plastics

Effect of recycled waste cardboard chips on the physico-thermal-hygric properties of hemp-lime biocomposites International audience cardboard ; Raw material ; Plastic waste ; Corrugated fiberboard Natural Fiber Reinforced Composites ; Hygrothermal properties of building materials ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Nur Fitriayu Mandasari | Erwin | Haeruddin Hafid | Arlistria Mutmainnah | Nur Wahyuni Syahrir | Virda Evi Yanti Deril | Muhammad Ashdaq
Source: Jurnal Ilmiah Manajemen Kesatuan (article, 2026-01-31)
Field: Chemistry — Inorganic Chemistry

Strategic Management of Coconut Industry Investment and Regional Competitiveness Coconut is a strategic commodity with high economic potential in West Sulawesi Province, yet its development and utilization remain suboptimal. This study aims to identify and strengthen the investment potential of coconut as a leading sector in the region. A participatory and systematic mapping approach was employed, combining secondary and primary data from planning documents, official statistics, field observations, and focus group discussions. Analytical methods included location quotient, shift share, and Klassen typology to assess the economic significance of coconut across regencies. The results indicate that coconut is a basic and growing commodity, particularly in Polewali Mandar Regency, with strong contributions to regional income. The development of coconut processing industries, especially coconut shell briquettes, shows high prospects due to abundant yet underutilized raw materials. Financial feasibility analysis confirms the viability of investment projects (NPV of IDR 24.94 billion, IRR 29%, Payback Period 3.9 years). This investment not only offers economic returns but also creates employment, increases farmers’ income, and promotes environmentally friendly energy. Strengthening synergies among government, business actors, and communities is recommended to support targeted and sustainable investment policies in coconut commodity development. Investment (military) ; Commodity ; Typology ; Strategic planning ; Payback period ; Raw material Coconut Research and Applications ; Agriculture and Agroindustry Studies ; Strategic Planning and Analysis
Authors: Ádám Titrik | Dmytro M. Kurhan | Mykola Sysyn | Szabolcs Fischer
Source: Acta Polytechnica Hungarica (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Optimization of Metal Can (bottle) Shapes for Maximizing Waste Container Capacity Effective waste management is key to creating more sustainable cities.This study explores a straightforward but impactful way to enhance waste collection efficiency -by optimizing the compaction of metal cans.Various methods of compressing metal containers, including hand and foot compression (middle, full diameter, and full height), were tested to see how much they could reduce the volume of these cans.Our simulations showed that fullheight and full-diameter compression were the most effective, significantly increasing container capacity by minimizing empty space.This approach is not only practical but scalable, offering waste management operators a clear pathway to improving resource efficiency.Beyond the immediate benefits of reduced collection frequency and lower fuel consumption, this method contributes to broader sustainability goals by minimizing the carbon footprint associated with waste management.These findings have wide-ranging implications, from urban policy-making to everyday waste disposal practices, highlighting a simple yet transformative step toward a cleaner environment. Container (type theory) ; Metal ; Key (lock) ; Waste heat Material Selection and Properties ; Belt Conveyor Systems Engineering ; Heat Transfer and Numerical Methods
Authors: Christian Jendreiko | Karl Svozil
Source: ArXiv.org (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Quantum Structures as Generative Scores: Partition Logic, Generative Logic, and Aesthetic Form We connect partition logic with Generative Logic by translating finite partition logics into Prolog-based Simple Generative Logic Grammars. As a proof of concept, we use the five-atom V-logic L_{12} to generate a modular visual artifact, the \emph{Quantum Square}. The approach separates logical structure from its visual, textual, or sonic realization. This makes partition logic useful both as a generative design resource and as a tool for communicating complementarity. Generative grammar ; Partition (number theory) ; Modular design ; Simple (philosophy) ; Description logic ; Generative model Quasicrystal Structures and Properties ; DNA and Biological Computing ; Design Education and Practice
Authors: Jianjun Huang | Shuang Liang | Guoyi Wu | S. Liang | Yanyun Liu | Gang Deng | Liqun Shen
Source: Scientific Reports (article, 2026-02-26)
Field: Chemistry — Analytical Chemistry

Quantification of heavy metals in Chinese jasmine flower cakes and risk assessment Jasmine flower cakes are particularly popular in China; however, they may contain heavy metals, posing health risks. We quantified heavy metals in jasmine flower cakes and assessed associated health risks. Al, Cr, Mn, Ni, Cu, As, Cd, and Pb in 40 samples were quantified via inductively coupled plasma mass spectrometry, revealing concentrations of 9.32, 0.012, 6.64, 0.296, 4.34, 0.002, 0.001, and 0.008 mg/kg, respectively. The detection rates were 100% except for Cd (82.50%) and As (67.50%). Cluster analysis categorised these metals into five groups: As/Cd, Al/Cr/Pb, Mn, Ni, and Cu. Although all samples exhibited mean health indices (HIs) < 1, nine samples (22.5%) exhibited HI > 1. The maximum HI was 2.261. The average estimated carcinogenic risk values were 27.00 × 10⁻<sup>6</sup> across all samples, with risk ranking as Cd > As > Pb. Overall, the cumulative carcinogenic risk of the cakes is within an acceptable range; however, the risk levels of certain batches warrant further attention. The findings provide a scientific basis for evaluating the safety of jasmine-flavoured cake consumption and establishes a reference analytical framework for risk screening and regulatory strategies of other traditional specialty foods. This supports high-quality development of food industry while safeguarding public health. Risk assessment ; Heavy metals ; Health risk assessment ; Health risk ; Safeguarding ; Food safety ; Human health ; Consumer safety Heavy Metals in Plants ; Heavy metals in environment ; Pesticide Residue Analysis and Safety
Authors: Sk Md Ahnaf Akif Alvi | Brent Vela | Vahid Attari | Jan Janssen | Danny Perez | Douglas Allaire | Raymundo Arróyave
Source: npj Computational Materials (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Deep Gaussian process-based cost-aware batch Bayesian optimization for complex materials design campaigns The accelerating pace and expanding scope of materials discovery demand optimization frameworks that efficiently navigate vast design spaces with complex response surfaces while judiciously allocating limited evaluation resources. We present a cost-aware, batch Bayesian optimization scheme powered by deep Gaussian process (DGP) surrogates and a heterotopic querying strategy. Our DGP surrogate, formed by stacking GP layers, models complex hierarchical relationships among high-dimensional compositional features and captures correlations across multiple target properties, propagating uncertainty through successive layers. We integrate evaluation cost into an upper-confidence-bound acquisition extension, which, together with heterotopic querying, proposes small batches of candidates in parallel, balancing exploration of under-characterized regions with exploitation of high-mean, low-variance predictions across correlated properties. Applied to refractory high-entropy alloys for high-temperature applications, our framework converges to optimal formulations in fewer iterations with cost-aware queries than conventional GP-based BO, highlighting the value of deep, uncertainty-aware, cost-sensitive strategies in materials campaigns. Bayesian optimization ; Gaussian process ; Bayesian probability ; Process (computing) ; Scope (computer science) ; Gaussian ; Kriging ; Optimal design Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Multi-Objective Optimization Algorithms
Authors: Akhil S Nair | Lucas Foppa | Matthias Scheffler
Source: Faraday Discussions (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Interpretable Bayesian Optimization for Catalyst Discovery Bayesian Optimization (BO) efficiently explores vast design spaces using probabilistic surrogate models, enabling the guided discovery of materials with desired properties. However, most BO frameworks rely on the knowledge of... Bayesian optimization ; Probabilistic logic ; Bayesian probability ; Surrogate model ; Uncertainty quantification ; Bayesian inference Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Multi-Objective Optimization Algorithms
Authors: Bin Cao | Jie Xiong | Jiaxuan Ma | Yuan Tian | Yirui Hu | Mengwei He | Longhan Zhang | Jiayu Wang | Jian Hui | Li Liu | Dezhen Xue | Turab Lookman | Tong-Yi Zhang
Source: ArXiv.org (article, 2026-01-11)
Field: Materials Science — Materials Chemistry

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Efficient exploration of vast compositional and processing spaces is essential for accelerated materials discovery. Bayesian optimization (BO) provides a principled strategy for identifying optimal materials with minimal experiments, yet its adoption in materials science is hindered by implementation complexity and limited domain-specific tools. Here, we present Bgolearn, a comprehensive Python framework that makes BO accessible and practical for materials research through an intuitive interface, robust algorithms, and materials-oriented workflows. Bgolearn supports both single-objective and multi-objective Bayesian optimization with multiple acquisition functions (e.g., expected improvement, upper confidence bound, probability of improvement, and expected hypervolume improvement etc.), diverse surrogate models (including Gaussian processes, random forests, and gradient boosting etc.), and bootstrap-based uncertainty quantification. Benchmark studies show that Bgolearn reduces the number of required experiments by 40-60% compared with random search, grid search, and genetic algorithms, while maintaining comparable or superior solution quality. Its effectiveness is demonstrated not only through the studies presented in this paper, such as the identification of maximum-elastic-modulus triply periodic minimal surface structures, ultra-high-hardness high-entropy alloys, and high-strength, high-ductility medium-Mn steels, but also by numerous publications that have proven its impact in material discovery. With a modular architecture that integrates seamlessly into existing materials workflows and a graphical user interface (BgoFace) that removes programming barriers, Bgolearn establishes a practical and reliable platform for Bayesian optimization in materials science, and is openly available at https://github.com/Bin-Cao/Bgolearn. Bayesian optimization ; Python (programming language) ; Workflow ; Modular design ; Bayesian probability ; Probabilistic logic ; Grid ; Benchmark (surveying) ; Gaussian process Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Stochastic Gradient Optimization Techniques
Authors: Bin Cao | Jie Xiong | Jiaxuan Ma | Yuan Tian | Yirui Hu | Mengwei He | Longhan Zhang | Jiayu Wang | Jian Hui | Li Liu | Dezhen Xue | Turab Lookman | Tong-Yi Zhang
Source: arXiv (Cornell University) (preprint, 2026-01-11)
Field: Materials Science — Materials Chemistry

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Efficient exploration of vast compositional and processing spaces is essential for accelerated materials discovery. Bayesian optimization (BO) provides a principled strategy for identifying optimal materials with minimal experiments, yet its adoption in materials science is hindered by implementation complexity and limited domain-specific tools. Here, we present Bgolearn, a comprehensive Python framework that makes BO accessible and practical for materials research through an intuitive interface, robust algorithms, and materials-oriented workflows. Bgolearn supports both single-objective and multi-objective Bayesian optimization with multiple acquisition functions (e.g., expected improvement, upper confidence bound, probability of improvement, and expected hypervolume improvement etc.), diverse surrogate models (including Gaussian processes, random forests, and gradient boosting etc.), and bootstrap-based uncertainty quantification. Benchmark studies show that Bgolearn reduces the number of required experiments by 40-60% compared with random search, grid search, and genetic algorithms, while maintaining comparable or superior solution quality. Its effectiveness is demonstrated not only through the studies presented in this paper, such as the identification of maximum-elastic-modulus triply periodic minimal surface structures, ultra-high-hardness high-entropy alloys, and high-strength, high-ductility medium-Mn steels, but also by numerous publications that have proven its impact in material discovery. With a modular architecture that integrates seamlessly into existing materials workflows and a graphical user interface (BgoFace) that removes programming barriers, Bgolearn establishes a practical and reliable platform for Bayesian optimization in materials science, and is openly available at https://github.com/Bin-Cao/Bgolearn. Bayesian optimization ; Python (programming language) ; Workflow ; Modular design ; Bayesian probability ; Probabilistic logic ; Grid ; Benchmark (surveying) ; Gaussian process Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Stochastic Gradient Optimization Techniques
Authors: R. Seaton Ullberg | Megan C. Davis | Jeremy Schroeder | Andrew Salij | M. J. Cawkwell | Christopher J. Snyder | Wilton J. M. Kort-Kamp | Ivana Matanović
Source: arXiv (Cornell University) (preprint, 2026-04-09)
Field: Materials Science — Materials Chemistry

Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials The discovery of new energetic materials is critical for advancing technologies from defense to private industry. However, experimental approaches remain slow and expensive while computational alternatives require accurate material property inputs that are often costly to obtain, limiting their ability to efficiently predict detonation performance across a vast chemical space. We address this challenge through an active learning strategy that integrates density functional theory calculations, thermochemical modeling, message-passing neural networks, and Bayesian optimization. The resulting high-throughput workflow iteratively expands the training dataset by selecting new molecules in a targeted manner that balances the exploration of broad chemical space with the exploitation of promising high-performing candidates. This approach yields the largest publicly available database of potential CHNO explosives drawn from an initial pool of more than 70 billion candidates and a generalizable surrogate model capable of accurately predicting detonation performance (R$^2$ &gt; 0.98). Feature importance analysis on this largest-to-date dataset reveals that oxygen balance is the dominant driver of detonation performance, complemented by contributions from local electronic structure, density, and the presence of specific functional groups. Cheminformatics analysis highlights how energetic materials with similar performance metrics tend to cluster in distinct chemical spaces offering a clearer direction for future synthesis studies. Together, the surrogate model, database, and resulting chemical insights provide a valuable foundation for high-throughput screening and targeted discovery of new energetic materials spanning diverse and previously unexplored regions of chemical space. Chemical space ; Cheminformatics ; Detonation ; Workflow ; Explosive material ; Limiting ; Bayesian probability ; Property (philosophy) ; Bayes' theorem Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Energetic Materials and Combustion
Authors: Yuhan Liu | Jiaxin Xu | Renzheng Zhang | Meng Jiang | Tengfei Luo
Source: arXiv (Cornell University) (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Active learning-enabled multi-objective design of thermally conductive and mechanically compliant polymers Polymers are attractive in applications like flexible electronics and thermal interface materials due to their mechanical compliance and processability. However, conventional polymers have low thermal conductivity (TC), limiting their heat dissipation performance. Identifying polymers that simultaneously achieve high intrinsic TC and mechanical flexibility (i.e., low modulus) remains a challenge. Here, we develop an active learning (AL) framework based on multi-objective Bayesian optimization (MOBO) to discover polymers exhibiting both high TC and low bulk modulus. Initially, a high-throughput molecular dynamics (MD) pipeline generated an initial dataset, and independent Deep Kernel Learning (DKL) surrogate models were trained for TC and bulk modulus to predict properties and uncertainties. Using the parallel noisy expected hypervolume improvement (qNEHVI) acquisition function, the framework iteratively screens a larger unlabeled polymer database, systematically recommends new polymer candidates for MD evaluation, and updates the DKL models with newly acquired data. Ultimately, six candidates were identified on the Pareto front, representing optimal trade-offs between TC and modulus. Interpretability analysis further revealed molecular features associated with these trade-offs, and synthesizability assessment supported the practical relevance of the selected candidates. By combining MD simulations with AL-enabled MOBO, our workflow mitigates data scarcity, reduces development time, and provides actionable guidance for designing multifunctional polymers tailored for different applications. Interpretability ; Polymer ; Bayesian optimization ; Flexibility (engineering) ; Pipeline (software) ; Workflow ; Electrical conductor ; Electronics ; Thermal conductivity Machine Learning in Materials Science ; Thermal properties of materials ; Block Copolymer Self-Assembly
Authors: Yuhan Liu | Jiaxin Xu | Renzheng Zhang | Meng Jiang | Tengfei Luo
Source: arXiv (Cornell University) (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

Active learning-enabled multi-objective design of thermally conductive and mechanically compliant polymers Polymers are attractive in applications like flexible electronics and thermal interface materials due to their mechanical compliance and processability. However, conventional polymers have low thermal conductivity (TC), limiting their heat dissipation performance. Identifying polymers that simultaneously achieve high intrinsic TC and mechanical flexibility (i.e., low modulus) remains a challenge. Here, we develop an active learning (AL) framework based on multi-objective Bayesian optimization (MOBO) to discover polymers exhibiting both high TC and low bulk modulus. Initially, a high-throughput molecular dynamics (MD) pipeline generated an initial dataset, and independent Deep Kernel Learning (DKL) surrogate models were trained for TC and bulk modulus to predict properties and uncertainties. Using the parallel noisy expected hypervolume improvement (qNEHVI) acquisition function, the framework iteratively screens a larger unlabeled polymer database, systematically recommends new polymer candidates for MD evaluation, and updates the DKL models with newly acquired data. Ultimately, six candidates were identified on the Pareto front, representing optimal trade-offs between TC and modulus. Interpretability analysis further revealed molecular features associated with these trade-offs, and synthesizability assessment supported the practical relevance of the selected candidates. By combining MD simulations with AL-enabled MOBO, our workflow mitigates data scarcity, reduces development time, and provides actionable guidance for designing multifunctional polymers tailored for different applications. Interpretability ; Polymer ; Bayesian optimization ; Flexibility (engineering) ; Pipeline (software) ; Workflow ; Electrical conductor ; Electronics ; Thermal conductivity Machine Learning in Materials Science ; Thermal properties of materials ; Block Copolymer Self-Assembly