jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yilei Wu | Xiangyang Li | Rong Guo | Rui Xu | Ming-Gang Ju | Jinlan Wang
Source: National Science Review (review, 2025-03-04)
Field: Materials Science — Materials Chemistry

How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method? The development of novel functional materials has attracted widespread attention to meet the constantly growing demand for addressing the major global challenges facing humanity, among which experimental synthesis emerges as one of the crucial challenges. Understanding the synthesis processes and predicting the outcomes of synthesis experiments are essential for increasing the success rate of experiments. With the advancements in computational power and the emergence of machine learning (ML) techniques, computational guidelines and data-driven methods have significantly contributed to accelerating and optimizing material synthesis. Herein, a review of the latest progress on the computation-guided and ML-assisted inorganic material synthesis is presented. First, common synthesis methods for inorganic materials are introduced, followed by a discussion of physical models based on thermodynamics and kinetics, which are relevant to the synthesis feasibility of inorganic materials. Second, data acquisition, commonly utilized material descriptors, and ML techniques in ML-assisted inorganic material synthesis are discussed. Third, applications of ML techniques in inorganic material synthesis are presented, which are classified according to different material data sources. Finally, we highlight the crucial challenges and promising opportunities for ML-assisted inorganic materials synthesis. This review aims to provide critical scientific guidance for future advancements in ML-assisted inorganic materials synthesis. Computer science ; Materials science ; Computational science ; Process engineering ; Engineering Machine Learning in Materials Science
Authors: Nils Gönnheimer | Karsten Reuter | Johannes T. Margraf
Source: Journal of Chemical Theory and Computation (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation The development of machine learning interatomic potentials (MLIPs) has revolutionized computational chemistry by enhancing the accuracy of empirical force fields while retaining a large computational speed-up compared to first-principles calculations. Despite these advancements, the calculation of Hessian matrices for large systems remains challenging, in particular because analytical second-order derivatives are often not implemented. This necessitates the use of computationally expensive finite-difference methods, which can furthermore display low precision in some cases. Automatic differentiation (AD) offers a promising alternative to reduce this computational effort and makes the calculation of Hessian matrices more efficient and accurate. Here, we present the implementation of AD-based second-order derivatives for the popular MACE equivariant graph neural network architecture. The benefits of this method are showcased via a high-throughput prediction of heat capacities of porous materials with the MACE-MP-0 foundation model. This is essential for precisely describing gas adsorption in these systems and was previously possible only with bespoke ML models or expensive first-principles calculations. We find that the availability of foundation models and accurate analytical Hessian matrices offers comparable accuracy to bespoke ML models in a zero-shot manner and additionally allows for the investigation of finite-size and rounding errors in the first-principles data. Computer science ; Automatic differentiation ; Order (exchange) ; Computational science ; Numerical differentiation ; Computational chemistry ; Chemistry ; Algorithm ; Mathematics ; Computation ; Mathematical analysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: R. Bhattacharya | Mohit Kumar Chattopadhyay | Sourav Datta | Priyabrata Banerjee
Source: ChemistrySelect (review, 2025-07-01)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks in Action: A Comprehensive Review on Fluoremetric Sensing and Adsorption of Organophosphorus Pesticides Abstract Pesticides are a major backbone of the agriculture sector. However, uncontrolled use of pesticides by untrained individuals, especially organophosphorus pesticides (OPPs), leads to significant environmental release of these residuals. The persistence of such residual chemicals causes environmental pollution, harms human health by damaging the respiratory system, and may lead to mutagenesis, carcinogenesis, and death. This issue will not remain concealed from the world for long; therefore, as a chemist, addressing these problems is crucial by tracing and removing residuum pesticides from the environment and food sources. Contextually, tailored‐structured porous metal–organic frameworks (MOFs) as highly tunable luminescent materials have gained significant recognition as a viable alternative for detecting and remediating these OPPs. The proper synthetic route and the necessary building units with appropriate dangling groups facilitate MOFs in this context. Concerning this, the review paper investigates the synthesis and potential of versatile MOFs for fluorimetric detection and adsorption of OPPs. It further elucidates their underlying mechanistic pathways and highlights the critical need for these applications. Additionally, artificial intelligence (AI) and machine learning (ML) assisted future initiatives emphasize the MOF‐based solutions for decreasing OPP‐related environmental and human health hazards, opening the door for new and long‐term pesticide management strategies. Context (archaeology) ; Pesticide ; Human health ; Metal-organic framework ; Environmental science ; Adsorption ; Biochemical engineering ; Computer science ; Risk analysis (engineering) ; Environmental chemistry ; Business ; Chemistry ; Engineering ; Environmental health ; Ecology ; Biology ; Medicine ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Molecular Sensors and Ion Detection
Authors: Haili Jia | Yiming Chen | Gi‐Hyeok Lee | Jacob Smith | Miaofang Chi | Wanli Yang | Maria K. Y. Chan
Source: ArXiv.org (preprint, 2025-01-15)
Field: Chemistry — Analytical Chemistry

Revealing Local Structures through Machine-Learning- Fused Multimodal Spectroscopy Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as x-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS, but most of these frameworks rely on a single data stream, which is often insufficient. In this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond improving prediction accuracy, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures. Spectroscopy ; Computer science ; Artificial intelligence ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Xing Xin | Lingxuan Kong | Liren Chen | Long Zhao | Junjie Li | Yusheng Xiao | Min Ding | Peng Wang | Linping Sun | Yafeng Li | Bingbing Li
Source: Journal of Molecular Liquids (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Fmoc-d-phenylalanine solubility in twelve solvents: Solvent effects, molecular simulation, thermodynamic modeling vs. machine learning regression, and comparison of structurally similar substances Solubility ; Solvent ; Phenylalanine ; Chemistry ; Regression ; Thermodynamics ; Regression analysis ; Organic chemistry ; Computational chemistry ; Materials science ; Machine learning ; Computer science ; Mathematics ; Amino acid ; Physics ; Statistics Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Kateřina Plachká | Veronika Pilařová | Taťána Gazárková | Jean‐Christophe Garrigues | František Švec | Lucie Nováková
Source: Analytical Chemistry (article, 2025-05-10)
Field: Chemistry — Spectroscopy

Artificial Neural Networks: An Innovative Approach Used for Elucidation of Ionization Processes in Supercritical Fluid Chromatography-Mass Spectrometry Understanding and predicting mass spectrometry responses in supercritical fluid chromatography-mass spectrometry (SFC-MS) is critical for optimizing detection across diverse analytes and solvent compositions. We present a novel approach using artificial neural networks (ANN) to explore the complex relationships between molecular descriptors of analytes and MS responses in different makeup solvent compositions enabling SFC-MS coupling. 226 molecular descriptors were evaluated for compounds under standardized SFC conditions, with 24 makeup solvent compositions. These makeup solvents included pure alcohols and methanol with varying concentrations of volatile additives. Our results highlight distinct ionization processes for the two most commonly used soft ionization techniques: (i) electrospray ionization (ESI), primarily involving proton or cation transfer, and (ii) atmospheric pressure chemical ionization (APCI), associated with charged ion transfer. Principal component analysis of weights assigned to molecular descriptors reveals that, in positive detection mode, these descriptors effectively differentiate ionization efficiency between ESI and APCI. In contrast, this differentiation is less pronounced in negative mode, where the variance explained is more homogeneously distributed, with stronger discrimination observed when NH<sub>3</sub> is used as an additive to the organic modifier. These findings provide critical insights into the influence of molecular descriptors and solvent composition on ionization efficiency, serving as a foundation for future investigations into SFC-MS optimization. This proof-of-concept underscores the feasibility of using predictive models to advance understanding of ionization efficiency and offers a valuable framework for refining SFC-MS workflows in analytical chemistry. Chemistry ; Mass spectrometry ; Supercritical fluid chromatography ; Chromatography ; Supercritical fluid ; Gas chromatography ; Organic chemistry Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Sangyeon Pak | John Hong | SeungNam Cha
Source: ACS Nano (review, 2025-09-23)
Field: Materials Science — Materials Chemistry

Recent Contact Strategies for Two-Dimensional Electronics Two-dimensional (2D) semiconductors such as monolayer MoS<sub>2</sub> and WSe<sub>2</sub> have rapidly gained attention as next-generation channel materials for ultrascaled electronics owing to their exceptional electrostatic control and atomic-scale thickness. Despite their promise, the high contact resistance and Fermi-level pinning at the metal-semiconductor interface continue to hinder optimal device performance and scalability. This review highlights recent breakthroughs in contact engineering─including van der Waals metal transfer, semimetallic and edge contacts, contact doping, strain engineering, and self-healing electrodes─that collectively enhance carrier injection, reduce Schottky barriers, and improve interface stability. We also examine complementary metal-oxide semiconductor-compatible integration strategies and the growing role of computational screening and machine learning in accelerating the discovery of optimal contact materials. These advances have enabled record-setting 2D field-effect transistor performance at sub-50 nm gate lengths, underscoring the increasing readiness of 2D materials for high-volume, energy-efficient applications beyond the silicon era. Schottky barrier ; Electronics ; Interface (matter) ; Transistor ; Contact resistance ; van der Waals force ; Monolayer ; Semiconductor ; Silicon 2D Materials and Applications ; Adhesion, Friction, and Surface Interactions ; Advanced Sensor and Energy Harvesting Materials
Authors: Pranoy Ray | Kamal Choudhary | Surya R. Kalidindi
Source: Integrating materials and manufacturing innovation (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Lean CNNs for Mapping Electron Charge Density Fields to Material Properties Electron ; Charge (physics) ; Materials science ; Physics ; Nuclear physics ; Particle physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices
Authors: Xintianyang Wang | Lijuan Liao | Chenguang Huang | Xianqian Wu
Source: Acta Mechanica Sinica (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Prediction of mechanical properties of cross-linked polymer interface by graph convolution network Convolution (computer science) ; Graph ; Interface (matter) ; Materials science ; Computer science ; Composite material ; Theoretical computer science ; Artificial intelligence ; Artificial neural network Machine Learning in Materials Science ; Injection Molding Process and Properties ; Industrial Vision Systems and Defect Detection
Authors: Faisal Al‐Akayleh | Ahmed S.A. Ali Agha | Ali R. Olaimat | Nidal A. Qinna
Source: Preprints.org (preprint, 2025-07-08)
Field: Chemistry — Spectroscopy

Rationalizing Polysaccharide Extraction with Deep Eutectic Solvents: From Supramolecular Architecture to Emerging AI-Guided Solvent Design Deep eutectic solvents (DESs) have emerged as sustainable and tunable alternatives to conventional solvents for the extraction polysaccharides. This review presents a struc-ture-informed framework linking DES composition to polysaccharide solubility, em-phasizing the differential responsiveness of amorphous, interfacial, and crystalline domains. Amorphous polysaccharides are efficiently extracted under mild DES condi-tions, while crystalline polymers often require stronger hydrogen bond acceptors or thermal/mechanical activation. Beyond dissolution, DESs modulate key properties of the extracted polysaccharides, including molecular weight, monomer composition, and bioactivity. Comparative analysis highlights how acidic, basic, or metal-coordinating DESs selectively target distinct polymer classes. Emerging innovations, such as in situ DES formation, mechanochemical systems, and switchable solvents, enhance efficiency and reduce downstream processing demands. Furthermore, the integration of machine learning and COSMO-RS modeling enables predictive solvent design, reducing reliance on empirical screening. By combining mechanistic insight, compositional tailoring, and computational tools, this review provides a scientifically grounded perspective for ad-vancing DES-mediated extraction processes and enabling structure-preserving, appli-cation-oriented recovery of polysaccharides in food, pharmaceutical, and biorefinery domains. Deep eutectic solvent ; Eutectic system ; Supramolecular chemistry ; Extraction (chemistry) ; Solvent ; Architecture ; Solvent extraction ; Chemistry ; Polysaccharide ; Chemical engineering ; Materials science ; Organic chemistry ; Engineering ; Art ; Molecule Analytical Chemistry and Chromatography ; Carbohydrate Chemistry and Synthesis ; Mesoporous Materials and Catalysis
Authors: Ahsan Ali | Mohammad Usman | Hafız Muhammad Ali | Uzair Sajjad | Md. Abdul Aziz | M. Nasiruzzaman Shaikh
Source: International Journal of Thermofluids (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Bayesian surrogate assisted neural network model to predict the hydrogen storage in 9-ethylcarbazole • Deep neural network is applied to predict the hydrogen storage capacity. • Pearson, Spearman, and Kendal correlations are used to indicate the optimal parameter. • Time, pressure and temperature are found to be influential parameters. • Bayesian Surrogate Gaussian Process model predicted the hydrogen storage capacity accurately (R 2 =0.89). Optimization of the reaction conditions for hydrogen storage in 9-ethylcarbazole, an efficient liquid organic hydrogen carrier, is essential for advancing hydrogen energy applications. In this study, a deep neural network (DNN) model was developed to analyze the effects of key parameters such as temperature, initial pressure, catalyst type and dosage, and stirring speed on hydrogen storage capacity. Correlational analysis using Pearson, Spearman, and Kendal correlations identified time (r =0.83) as the most influential factor impacting the hydrogen storage positively, whereas catalyst dosage (r=-0.75) exhibited a strong negative correlation. Temperature (r=0.15) exhibited a weak relationship, suggesting nonlinear dependencies. To enhance the predictive accuracy, Bayesian Surrogate Random Forest (BSRF), Bayesian Surrogate Gaussian Process (BSGP), and Bayesian Surrogate Gradient Boost Regression Trees (BSGBRT) were integrated with deep neural networks (DNNs). Among these, the BSGP-DNN model exhibited the highest predictive performance, achieving an R 2 value of 0.89. The reliability of the model is further supported by a low Mean Absolute Error (0.0101), mean square error (0.0001), and relative deviation (3.2%). The error density curve centered around emphasized the model’s accuracy and uniform error distribution. This study employs a deep neural framework to analyze the effects of various reaction parameters on hydrogen storage capacity. Correlational analysis (using Pearson, Spearman, and Kendal correlations) is performed to find the optimal parameters as the inputs of the model. Moreover, we employed Bayesian Surrogate Random Forest (BSRF), Bayesian Surrogate Gaussian Process (BSGP), and Bayesian Surrogate Gradient Boost Regression Trees (BSGBRT) with deep neural networks (DNNs) for predicting hydrogen storage capacity (wt.%). Hydrogen storage ; Artificial neural network ; Bayesian network ; Bayesian probability ; Computer science ; Artificial intelligence ; Hydrogen ; Chemistry ; Organic chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advanced Battery Technologies Research
Authors: H. Seki | Kenji Nawa | Chiharu Mitsumata | Toyo Kazu Yamada
Source: ACS Applied Nano Materials (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Tuning Epitaxial Growth of Atomically Thin MnTe Films on Fe(001) for Creating van der Waals 2D Magnets Layered two-dimensional (2D) van der Waals magnetic crystals have predominantly been synthesized using 3d transition metals and chalcogenides via chemical vapor transport. However, for device applications, it remains critical to establish growth conditions that enable epitaxial, layer-by-layer film formation under vacuum, particularly on atomically flat solid substrates. In this study, we investigate manganese (Mn) and tellurium (Te) as a model system, due to the layered Mn–Te compound’s intriguing topological and magnetic properties. A clean body-centered-cubic Fe(001) substrate was used because it supports the epitaxial, layer-by-layer growth of atomically flat body-centered tetragonal Mn(001) films. Our findings using scanning tunneling microscopy and spectroscopy (STM/STS) combined with density functional theory calculation reveal that the direct growth of layered Mn/Te films on the ferromagnetic Fe(001) substrate is challenging, even at 300 K. The resulting surface comprises a mixture of Te, Te/Mn, Mn/Te, and MnTe alloy regions. However, post-annealing up to ∼650 K induces a transformation from these metastable states into a thermodynamically stable MnTe alloy phase, forming an atomically flat 2D surface. This study further highlights how combining machine learning with STM/STS enables precise chemical classification and accurate identification of multiple local density of states types, even within structurally complex or mixed-phase systems. van der Waals force ; Materials science ; Epitaxy ; Thin film ; Magnet ; Condensed matter physics ; Nanotechnology ; Chemical engineering ; Chemistry ; Physics ; Layer (electronics) ; Molecule ; Quantum mechanics 2D Materials and Applications ; Magnetic properties of thin films ; Quantum and electron transport phenomena
Authors: Jerome Workman | Howard Mark
Source: Spectroscopy (article, 2025-02-01)
Field: Chemistry — Analytical Chemistry

From Classical Regression to AI and Beyond: The Chronicles of Calibration in Spectroscopy: Part I The analysis of chemical data has undergone a profound transformation, from early basic statistical methods to the modern era of machine learning (ML) and artificial intelligence (AI). This progression is particularly evident in the field of spectroscopy, where multivariate analysis techniques, such as classical regression, principal component analysis (PCA), and partial least squares (PLS), laid the foundation for today’s more advanced or automated ML calibration modeling techniques. This “Chemometrics in Spectroscopy” column traces the historical and technical development of these methods, emphasizing their application in calibrating spectrophotometers for predicting measured sample chemical or physical properties—particularly in near-infrared (NIR), infrared (IR), Raman, and atomic spectroscopy—and explores how AI and deep learning are reshaping the spectroscopic landscape. In this two-part series, we take a look back into the history of chemometrics (Part I) then peer into the future for an estimation of where chemometrics might be going (Part II). Calibration ; Regression ; Spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Statistics ; Mathematics ; Chromatography ; Physics ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems