jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaomin Tang | Ömer Faruk Altundal | Frits Daeyaert | Zhiqiang Liu | Germán Sastre
Source: Chemical Society Reviews (review, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Computational insights on the role of structure-directing agents (SDAs) in the synthesis of zeolites Most of the zeolites synthesised in the last 3 or 4 decades employ organic structure-directing agents (OSDAs) in their synthesis. The synthesis or test of new OSDAs is a major driving force in order to achieve the goal of synthesising a new zeolite. Despite this, predicting which zeolite phase will form when a specific OSDA is used remains a complex challenge. Moreover, even knowing the zeolite phase that will be obtained using a given OSDA, it is not easy to always rationalise or fully explain the result obtained. Computational simulations have occasionally been employed, and in a number of cases they are limited to calculate the van der Waals contribution between zeolite and the OSDA molecules (zeo-OSDA). Strongly negative values indicate an increasing viability of the synthesis, but it is only by comparison between competing zeolite phases that some insight can be gained, and this has been done in a limited number of cases. Other, less simple, approaches consider the total energy of the zeolite formed, of which the van der Waals zeo-OSDA contribution is not always the dominant contribution. These and other similar procedures need to be analysed and summarised in order to clarify the pros and cons of each approach. The recent advent of big data has allowed to construct databases that make it possible to analyse a large number of results. With the help of new descriptors and algorithms (some of them making use of artificial intelligence) further advances have been made. In spite of the large pool of data available, it becomes difficult to systematise and obtain general rules. A recent burst to this topic comes from the possibility to (at least partially) control the Al distributions by selecting appropriate OSDAs. These Al distributions influence the location and strength of Brønsted acid sites which in turn are directly responsible for the catalytic activity of the material. Zeolite ; van der Waals force ; Materials science ; Nanotechnology ; Stability (learning theory) ; Chemical engineering ; Computational simulation ; Chemistry ; Computer science ; Molecule ; Catalysis ; Computational science ; Engineering ; Organic chemistry Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Luigi Melchiorre | Francesco Anelli | Giansergio Menduni | Andrea Annunziato | Laurine Bodin | Solenn Cozic | Giovanni Magno | Angelo Sampaolo | Francesco Prudenzano | Vincenzo Spagnolo
Source: Photoacoustics (article, 2025-01-13)
Field: Chemistry — Spectroscopy

Dual-gas quartz-enhanced photoacoustic spectroscopy sensor exploiting two fiber-combined interband cascade lasers In this work, a novel indium fluoride glass 2-input-1-output fiber combiner was designed and fabricated to combine two Interband Cascade Laser (ICL) sources emitting in the mid-infrared wavelength range. To test the combiner performance, a dual-gas quartz-enhanced photoacoustic spectroscopy sensor was demonstrated for the detection of carbon dioxide (CO<sub>2</sub>) and nitric oxide (NO), employing two fiber-coupled ICLs having central emission wavelengths of 4,234 nm and 5,263 nm, respectively. The laser beams were coupled via the fiber combiner and then focused into a commercial acoustic detection module equipped with an input fiber-port, thus resulting in a plug-and-play sensing system. Tens of ppm-level detection limits at 3σ are achieved for both pollutants with a lock-in integration time (τ) of 0.1 s. Finally, an Allan-Werle analysis demonstrated the stability of the sensor, allowing the achievement of detection limit of 13 ppm and 4 ppm at τ = 10 s for CO<sub>2</sub> and NO, respectively. Cascade ; Materials science ; Photoacoustic spectroscopy ; Photoacoustic imaging in biomedicine ; Optoelectronics ; Quartz ; Laser ; Optical fiber ; Spectroscopy ; Optics ; Dual (grammatical number) ; Chemistry ; Physics Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Sensors
Authors: Haiyan He | Zhoutao Li | Qin Qian | Yue Yu | Yuanxin Guo | Sheng Cai | Zhanming Li
Source: Foods (review, 2025-07-30)
Field: Chemistry — Analytical Chemistry

Spectroscopic and Imaging Technologies Combined with Machine Learning for Intelligent Perception of Pesticide Residues in Fruits and Vegetables Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products. Hyperspectral imaging ; Computer science ; Artificial intelligence ; Commercialization ; Machine learning ; Pesticide residue ; Imaging spectrometer ; Machine vision ; Imaging spectroscopy ; Pesticide ; Spectrometer ; Agronomy ; Physics Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Laser-induced spectroscopy and plasma
Authors: Asterios Theofilou | Stefanos A. Nastis | Anastasios Michailidis | Thomas Bournaris | Konstadinos Mattas
Source: Sustainability (review, 2025-06-13)
Field: Chemistry — Analytical Chemistry

Predicting Prices of Staple Crops Using Machine Learning: A Systematic Review of Studies on Wheat, Corn, and Rice According to the FAO, wheat, corn, and rice are staple crops that support global food security, providing 50% of the world’s dietary energy. The ability to predict accurately these key food crop agricultural commodity prices is important in stabilizing markets, supporting policymaking, and informing stakeholders’ decisions. To this aim, machine learning (ML), ensemble learning (EL), deep learning (DL), and time series methods (TS) have been increasingly used for forecasting due to the rapid development of computational power and data availability. This study presents a systematic literature review (SLR) of peer-reviewed original research articles focused on forecasting the prices of wheat, corn, and rice using machine learning (ML), deep learning (DL), ensemble learning (EL), and time series techniques. The results of the study help uncover suitable forecasting methods, such as hybrid deep learning models that consistently outperform traditional methods, and they identify important limitations in model interpretability and the use of region-specific datasets, highlighting the need for explainable and generalizable forecasting solutions. This systematic review adheres to the PRISMA 2020 reporting guidelines. Agricultural engineering ; Agronomy ; Staple food ; Engineering ; Agriculture ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Zhihao Wang | Wentao Li | Siying Wang | Xiaonan Wang
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design ABSTRACT With the increasing global demand for energy transition and environmental sustainability, catalysts play a vital role in mitigating global climate change, as they facilitate over 90% of chemical and material conversions. It is important to investigate the complex structures and properties of catalysts for enhanced performance, for which artificial intelligence (AI) methods, especially graph neural networks (GNNs) could be useful. In this article, we explore the cutting‐edge applications and future potential of GNNs in intelligent catalyst design. The fundamental theories of GNNs and their practical applications in catalytic material simulation and inverse design are first reviewed. We analyze the critical roles of GNNs in accelerating material screening, performance prediction, reaction pathway analysis, and mechanism modeling. By leveraging graph convolution techniques to accurately represent molecular structures, integrating symmetry constraints to ensure physical consistency, and applying generative models to efficiently explore the design space, these approaches work synergistically to enhance the efficiency and accuracy of catalyst design. Furthermore, we highlight high‐quality databases crucial for catalysis research and explore the innovative application of GNNs in thermocatalysis, electrocatalysis, photocatalysis, and biocatalysis. In the end, we highlight key directions for advancing GNNs in catalysis: dynamic frameworks for real‐time conditions, hierarchical models linking atomic details to catalyst features, multi‐task networks for performance prediction, and interpretability mechanisms to reveal critical reaction pathways. We believe these advancements will significantly broaden the role of GNNs in catalysis science, paving the way for more efficient, accurate, and sustainable catalyst design methodologies. Catalysis ; Computer science ; Artificial neural network ; Graph ; Combinatorial chemistry ; Chemistry ; Artificial intelligence ; Theoretical computer science ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Tao Yang | Jie Liang | Yao Qin | Xiaokun Zhou | Jing He | Yuan Wang | Yuanqin Zhu | Zengxi Wei | Shuangliang Zhao
Source: Chemical Engineering Journal (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Prediction of Ethylene Homopolymerization Reaction Performance: FI Catalyst Activity, Polyethylene Molecular Weight, and Molecular Weight Distribution Polyethylene ; Ethylene ; Molar mass distribution ; Catalysis ; Chemical engineering ; Materials science ; Polymer chemistry ; Chemistry ; Polymer ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; Organometallic Complex Synthesis and Catalysis
Authors: Jian Chang | Shuze Zhu
Source: npj Computational Materials (article, 2025-03-02)
Field: Materials Science — Materials Chemistry

MGNN: Moment Graph Neural Network for Universal Molecular Potentials The quest for efficient and robust deep learning models for molecular systems representation is increasingly critical in scientific exploration. The advent of message passing neural networks has marked a transformative era in graph-based learning, particularly in the realm of predicting chemical properties and expediting molecular dynamics studies. We present the Moment Graph Neural Network (MGNN), a rotation-invariant message passing neural network architecture that capitalizes on the moment representation learning of 3D molecular graphs, is adept at capturing the nuanced spatial relationships inherent in three-dimensional molecular structures. From benchmark tests on public datasets, MGNN delivers multiple state-of-the-art results on QM9, revised MD17 and MD17-ethanol. Its generalizability and efficiency are also tested in additional systems including 3BPA and 25-element high-entropy alloys. The prowess of MGNN also extends to dynamic simulations, accurately predicting the structural and kinetic properties of complex systems such as amorphous electrolytes, with results that closely align with those from ab-initio simulations. The application of MGNN to the simulation of molecular spectra exemplifies its potential to offer a promising alternative to traditional electronic structure methods. Moment (physics) ; Computer science ; Graph ; Artificial neural network ; Neuroscience ; Physics ; Artificial intelligence ; Theoretical computer science ; Psychology ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Daniil A. Boiko | Thiago Reschützegger | Benjamín Sánchez-Lengeling | Samuel M. Blau | Gabriel dos Passos Gomes
Source: Nature Machine Intelligence (preprint, 2025-05-23)
Field: Materials Science — Materials Chemistry

Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs Computer science ; Artificial intelligence ; Natural language processing Machine Learning in Materials Science ; Fractal and DNA sequence analysis ; Quantum-Dot Cellular Automata
Authors: Saleh Ahmat Ibrahim | Shengyan Meng | Charles Milhans | Magda H. Barecka | Yilang Liu | Qiang Li | Jiaqi Yang | Yabing Sha | Yanhui Yi | Fanglin Che
Source: Nature Chemical Engineering (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

Interpretable machine learning-guided plasma catalysis for hydrogen production Catalysis ; Decomposition ; Hydrogen production ; Ammonia production ; Hydrogen ; Ammonia ; Plasma ; Ruthenium Machine Learning in Materials Science ; Data Quality and Management ; Topic Modeling
Authors: Yutong Song | Yewei Ding | Junyi Su | J. Li | Yuanhui Ji
Source: Angewandte Chemie International Edition (article, 2025-03-12)
Field: Materials Science — Materials Chemistry

Unlocking the Potential of Machine Learning in Co‐crystal Prediction by a Novel Approach Integrating Molecular Thermodynamics Co-crystal engineering is of interest for many applications in pharmaceutical, chemical, and materials fields, but rational design of co-crystals is still challenging. Although artificial intelligence has revolutionized decision-making processes in material design, limitations in generalization and mechanistic understanding remain. Herein, we sought to improve prediction of co-crystals by combining mechanistic thermodynamic modeling with machine learning. We constructed a brand-new co-crystal database, integrating drug, coformer, and reaction solvent information. By incorporating various thermodynamic models, the predictive performance was significantly enhanced. Benefiting from the complementarity of thermodynamic mechanisms and structural descriptors, the model coupling three thermodynamic models achieved optimal predictive performance in coformer and solvent screening. The model was rigorously validated against benchmark models using challenging independent test sets, showcasing superior performance in both coformer and solvent predicting with accuracy over 90%. Further, we employed SHAP analysis for model interpretation, suggesting that thermodynamic mechanisms are prominent in the model's decision-making. Proof-of-concept studies on ketoconazole validated the model's efficacy in identifying coformers/solvents, demonstrating its potential in practical application. Overall, our work enhanced the understanding of co-crystallization and highlighted the strategy that integrates mechanistic insights with data-driven models to accelerate the rational design and synthesis of co-crystals, as well as various other functional materials. Machine learning ; Computer science ; Artificial intelligence ; Benchmark (surveying) ; Biochemical engineering ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Moses Ogbaje | Vinayak Bhat | Chad Risko
Source: Annual Review of Materials Research (article, 2025-03-26)
Field: Materials Science — Materials Chemistry

Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning Organic semiconductors (OSCs) offer the capacity for distinctive and finely tuned electronic, optical, thermal, and mechanical properties, making them of interest across a range of energy generation and storage, sensor, lighting, display, and electronics applications. The pathway from molecular building block design to material, however, is complicated by complex synthesis– processing–structure–property–function relationships that are inherent to OSCs. The adoption of artificial intelligence (AI) tools, including the subset of AI referred to as machine learning (ML), into the materials design and discovery pipeline offers significant potential to overcome the multifaceted roadblocks along this pathway. Here, we review recent advances in the application of AI/ML for OSCs, with a focus on the development and use of ML. We present a brief primer on ML models and then highlight efforts wherein ML is used to predict molecular and material properties and discover new molecular building blocks and OSCs. Organic semiconductor ; Materials science ; Nanotechnology ; Semiconductor ; Engineering physics ; Computer science ; Engineering ; Optoelectronics Machine Learning in Materials Science ; Conducting polymers and applications
Authors: Joseph Pateras | Colin Zhang | Shriya Majumdar | Ayush Pal | Preetam Ghosh
Source: Scientific Reports (article, 2025-03-07)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning for automatic model reduction in chemical reaction networks Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computational costs of detailed mechanistic models while leveraging the predictive power of machine learning. This study applies this fusion to the biomedical challenge of Aβ fibril aggregation, a key factor in Alzheimer's disease. Central to the research is the introduction of an automatic reaction order model reduction framework, designed to optimize reduced-order kinetic models. This framework represents a shift in model construction, automatically determining the appropriate level of detail for reaction network modeling. The proposed approach significantly improves simulation efficiency and accuracy, particularly in systems like Aβ aggregation, where precise modeling of nucleation and growth kinetics can reveal potential therapeutic targets. Additionally, the automatic model reduction technique has the potential to generalize to other network models. The methodology offers a scalable and adaptable tool for applications beyond biomedical research. Its ability to dynamically adjust model complexity based on system-specific needs ensures that models remain both computationally feasible and scientifically relevant, accommodating new data and evolving understandings of complex phenomena. Computer science ; Scalability ; Artificial intelligence ; Machine learning ; Reduction (mathematics) ; Artificial neural network ; Fidelity Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Chenyu Song | Yintao Shi | Li Meng | Lin Wu | Xiaorong Xiong | Jianyun Liu | Dongsheng Xia
Source: Journal of Environmental Management (article, 2025-02-24)
Field: Materials Science — Materials Chemistry

An efficient catalyst screening strategy combining machine learning and causal inference Inference ; Causal inference ; Machine learning ; Computer science ; Artificial intelligence ; Econometrics ; Economics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Joung Sik Suh | Jae Hoon Jang | Byeong‐Chan Suh | Jae‐Yeon Kim
Source: Materials & Design (article, 2025-07-29)
Field: Materials Science — Biomaterials

Reverse design of Mg-Zn-Mn-Sr-Ca alloys for biodegradable implants by interpretable machine learning and genetic algorithm • A reverse design approach was employed for biodegradable Mg-Zn-Mn-Sr-Ca alloy. • Interpretable machine learning and multi-objective genetic algorithm were employed. • Four of six optimized alloys met strength and degradation criteria experimentally. • The results support data-driven design of biodegradable Mg alloys for implants. Biodegradable Mg alloys for implants must meet both mechanical strength and biologically compatible degradation rate to promote bone healing. The present study proposes a reverse engineering approach that utilizes interpretable machine learning and a multi-objective genetic algorithm to design biodegradable Mg-Zn-Mn-Sr-Ca (ZMJX) alloys for load-bearing orthopedic implants. The training of neural networks was facilitated by a dataset comprising 1044 data points, which contained chemical compositions, ultimate compressive strengths (UCS), and in vitro degradation rates (DR). The optimized neural network models predicted UCS and DR with a coefficient of determination exceeding 0.92 on testing data. Shapley additive explanations identified Zn as the most influential element affecting both UCS and DR. A total of six optimal alloys were identified from the Pareto front of two fitness functions (UCS ≥ 240 MPa, DR ≤ 1 mm/y). Four of these alloys satisfied the conflicting dual target criteria, although notable performance discrepancies were observed between reverse design and experimental results. The findings emphasize the necessity to incorporate microstructural and textural characteristics into existing databases, thereby enhancing model accuracy and enabling more reliable, data-driven design of biodegradable Mg alloys for load-bearing implant applications. Materials science ; Genetic algorithm ; Metallurgy ; Computer science ; Machine learning Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics