jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingang Wang | Yu Liu | Jinyu Guo | Sha Zhou | Yawei Du | Xuejiao Tang
Source: Separations (article, 2025-10-09)
Field: Chemistry — Inorganic Chemistry

Process Design and Optimisation Analysis for the Production of Ultra-High-Purity Phosphine With the increasing demand to scale the chip industry, attention is turning to the vital role that phosphanes and silanes play in semiconductor manufacturing processes such as chemical vapor deposition, plasma etching, and impurity doping. High-performance semiconductors often require a supply of ultra-pure gaseous phosphine (≥99.999%) to ensure the formation of defect-free thin-film structures with high integrity and strong functionality. In recent years, research on high-purity PH3 synthesis methods has mainly focused on two pathways: the acidic route with fewer side reactions, high by-product economics, and higher exergy of high-purity PH3, and the alkaline alternative with greater potential for practical application through lower reaction temperatures and a simpler reaction process. This paper presents the first comparative study and analysis on the preparation of ultra-high-purity PH3 and its process energy consumption. Using Aspen and its related software, the energy consumption and cost issues are discussed, and the process heat exchange network is established and optimised. By combining Aspen Plus V14 with MATLAB 2023, an artificial neural network (ANN) prediction model is established, and the parameters of the distillation section equipment are optimised through the NSGA-II model to solve problems such as low product yield and large equipment exergy loss. After optimisation, it can be found that in terms of energy consumption and cost indicators, the acidic process has greater advantages in large-scale production of high-purity PH3. The total energy consumption of the acidic process is 1.6 × 108 kJ/h, which is only one-third that of the alkaline process, while the cost of the heat exchange equipment is approximately three-quarters that of the alkaline process. Through dual-objective optimisation, the exergy loss of the acidic distillation part can be reduced by 1714.1 kW, and the economic cost can be reduced by USD 3673. Therefore, from the perspective of energy usage and equipment manufacturing, the comprehensive analysis of the acidic process has more advantages than that of the alkaline process. Exergy ; Heat exchanger ; Energy consumption ; Process design ; Waste heat ; Process integration ; Process (computing) ; Distillation ; Yield (engineering) ; Throughput Asymmetric Hydrogenation and Catalysis ; Catalysis for Biomass Conversion ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Rui Ding | Yuxin Chen | Junhong Chen
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Leveraging Data Mining, Active Learning, and Domain Adaptation for Efficient Discovery of Advanced Oxygen Evolution Electrocatalysts Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multi-stage machine learning (ML) approach to streamline the discovery and optimization of complex multi-metallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we demonstrate an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially benchmark in electrocatalysts research. Figure 1 Workflow ; Domain (mathematical analysis) ; Benchmark (surveying) ; Process (computing) ; Adaptation (eye) ; Chemical space ; Oxygen evolution Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Graph Neural Networks
Authors: Hongmei Xing | Qiuxin Si
Source: arXiv (Cornell University) (preprint, 2025-12-22)
Field: Materials Science — Materials Chemistry

Learning Continuous Solvent Effects from Transient Flow Data: A Graph Neural Network Benchmark on Catechol Rearrangement Predicting reaction outcomes across continuous solvent composition ranges remains a critical challenge in organic synthesis and process chemistry. Traditional machine learning approaches often treat solvent identity as a discrete categorical variable, which prevents systematic interpolation and extrapolation across the solvent space. This work introduces the \textbf{Catechol Benchmark}, a high-throughput transient flow chemistry dataset comprising 1,227 experimental yield measurements for the rearrangement of allyl-substituted catechol in 24 pure solvents and their binary mixtures, parameterized by continuous volume fractions ($\% B$). We evaluate various architectures under rigorous leave-one-solvent-out and leave-one-mixture-out protocols to test generalization to unseen chemical environments. Our results demonstrate that classical tabular methods (e.g., Gradient-Boosted Decision Trees) and large language model embeddings (e.g., Qwen-7B) struggle with quantitative precision, yielding Mean Squared Errors (MSE) of 0.099 and 0.129, respectively. In contrast, we propose a hybrid GNN-based architecture that integrates Graph Attention Networks (GATs) with Differential Reaction Fingerprints (DRFP) and learned mixture-aware solvent encodings. This approach achieves an \textbf{MSE of 0.0039} ($\pm$ 0.0003), representing a 60\% error reduction over competitive baselines and a $&gt;25\times$ improvement over tabular ensembles. Ablation studies confirm that explicit molecular graph message-passing and continuous mixture encoding are essential for robust generalization. The complete dataset, evaluation protocols, and reference implementations are released to facilitate data-efficient reaction prediction and continuous solvent representation learning. Extrapolation ; Categorical variable ; Artificial neural network ; Interpolation (computer graphics) ; Graph ; Benchmark (surveying) ; Binary number ; Generalization Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Yunyang Li | Zeyu Xia | Lin Huang | Xinran Wei | Han Yang | Sam Harshe | Zun Wang | Chang Liu | Jia Zhang | Bin Shao | Mark Gerstein
Source: ArXiv.org (preprint, 2025-02-26)
Field: Materials Science — Materials Chemistry

Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems Density Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltonian. Despite its importance, the application of DFT is frequently limited by the substantial computational resources required to construct the Kohn-Sham Hamiltonian. In response to these limitations, current research has employed deep-learning models to efficiently predict molecular and solid Hamiltonians, with roto-translational symmetries encoded in their neural networks. However, the scalability of prior models may be problematic when applied to large molecules, resulting in non-physical predictions of ground-state properties. In this study, we generate a substantially larger training set (PubChemQH) than used previously and use it to create a scalable model for DFT calculations with physical accuracy. For our model, we introduce a loss function derived from physical principles, which we call Wavefunction Alignment Loss (WALoss). WALoss involves performing a basis change on the predicted Hamiltonian to align it with the observed one; thus, the resulting differences can serve as a surrogate for orbital energy differences, allowing models to make better predictions for molecular orbitals and total energies than previously possible. WALoss also substantially accelerates self-consistent-field (SCF) DFT calculations. Here, we show it achieves a reduction in total energy prediction error by a factor of 1347 and an SCF calculation speed-up by a factor of 18%. These substantial improvements set new benchmarks for achieving accurate and applicable predictions in larger molecular systems. Scalability ; Density functional theory ; Hamiltonian (control theory) ; Set (abstract data type) ; Wave function ; Atomic orbital ; Reduction (mathematics) ; Basis set Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Rajat K. Dey | Michael Brocidiacono | Kushal Koirala | Alexander Tropsha | Konstantin Popov
Source: ArXiv.org (preprint, 2025-10-23)
Field: Materials Science — Materials Chemistry

Extending machine learning model for implicit solvation to free energy calculations The implicit solvent approach offers a computationally efficient framework to model solvation effects in molecular simulations. However, its accuracy often falls short compared to explicit solvent models, limiting its use in precise thermodynamic calculations. Recent advancements in machine learning (ML) present an opportunity to overcome these limitations by leveraging neural networks to develop more precise implicit solvent potentials for diverse applications. A major drawback of current ML-based methods is their reliance on force-matching alone, which can lead to energy predictions that differ by an arbitrary constant and are therefore unsuitable for absolute free energy comparisons. Here, we introduce a novel methodology with a graph neural network (GNN)-based implicit solvent model, dubbed Lambda Solvation Neural Network (LSNN). In addition to force-matching, this network was trained to match the derivatives of alchemical variables, ensuring that solvation free energies can be meaningfully compared across chemical species.. Trained on a dataset of approximately 300,000 small molecules, LSNN achieves free energy predictions with accuracy comparable to explicit-solvent alchemical simulations, while offering a computational speedup and establishing a foundational framework for future applications in drug discovery. Solvation ; Artificial neural network ; Limiting ; Energy (signal processing) ; Speedup ; Thermodynamic integration Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Kailai Lin | Matthew J. Coley-O'Rourke | Eran Rabani
Source: npj Computational Materials (article, 2025-12-13)
Field: Materials Science — Materials Chemistry

Deep-learning atomistic semi-empirical pseudopotential model for nanomaterials Abstract The semi-empirical pseudopotential method (SEPM) has been widely applied to provide computational insights into the electronic structure, photophysics, and charge carrier dynamics of nanoscale materials. We present “DeepPseudopot”, a machine-learned atomistic pseudopotential model that extends the SEPM framework by combining a flexible neural network representation of the local pseudopotential with parameterized non-local and spin-orbit coupling terms. Trained on bulk quasiparticle band structures and deformation potentials from GW calculations, the model captures many-body and relativistic effects with very high accuracy across diverse semiconducting materials, as illustrated for silicon and group III-V semiconductors. DeepPseudopot’s accuracy, efficiency, and transferability make it well-suited for data-driven in silico design and discovery of novel optoelectronic nanomaterials. Pseudopotential ; Quasiparticle ; Parameterized complexity ; Representation (politics) ; Nanoelectronics ; Coupling (piping) ; Charge (physics) ; Electronic band structure Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Advanced Physical and Chemical Molecular Interactions
Authors: Divya T L - | Chandrani Chakravorty - | Shashanka - | Shashank Hegde -
Source: International journal of innovative research and creative technology (article, 2025-11-30)
Field: Materials Science — Materials Chemistry

Bridging Density Functional Theory and Machine Learning: Predicting Formation Energies of Oxide Perovskites Perovskite oxides with the general formula ABO3 have formed the cornerstone in creating high functional materials because of their great tunability and versatility. Their application spans solar energy, superconductivity, catalysis, and fuel cells. Thermodynamic stability, expressed through formation energy, governs their usefulness. Conventionally, Density Functional Theory (DFT) is used to compute formation energies, but it is computationally expensive. In this work, we propose a machine learning-based prediction of perovskite formation energies using descriptors derived from elemental and structural features. After feature engineering and selection, several models were evaluated. XGBoost achieved the best R2 score of 0.9586, followed closely by Random Forest. These results demonstrate the potential of ensemble-based machine learning models in accelerating perovskite discovery. Density functional theory ; Bridging (networking) ; Perovskite (structure) ; Oxide ; Feature (linguistics) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Chen Li | Yinqiao Zhang | Fanxing Zhang | Yichun Li | Yao Shi | Xiaoyong Cao | Chunlei Wei | Nan Xu | Yi He
Source: The Journal of Physical Chemistry B (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Discovery of Solubilizers to Enhance the Swelling Kinetics of Epoxy SU-8 via Virtual Screening The slow swelling kinetics of SU-8 epoxy resist hinders its removal in semiconductor fabrication processes. To accelerate solubilizer discovery for swelling, we developed a virtual screening framework integrating molecular dynamics (MD) and machine learning. Using the diffusion coefficient within SU-8 as a key descriptor for solubilizer efficacy, we generated an MD-derived data set to train a graph convolutional neural network (GCN), enabling diffusivity predictions across >20 million compounds. This pipeline identified 4,6-decadienal (4,6-DDA) as a promising candidate. Experimentally, a 10 wt % aqueous solution of 4,6-DDA achieved 80.16% SU-8 swelling within 60 min, which was 3-fold greater than that of propylene carbonate. Simulations confirmed rapid 4,6-DDA penetration and enhanced performance in water, which is consistent with experimental measurements. This work demonstrates a rational simulation-guided strategy for the discovery of functional molecules for industrial processes. Swelling ; Kinetics ; Epoxy ; Virtual screening ; Thermal diffusivity ; Penetration (warfare) ; Fabrication ; Aqueous solution ; Convolutional neural network Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Polymer composites and self-healing
Authors: Qing Li | Haikuan Dong | Penghua Ying | Fan Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Anisotropic and isotropic elasticity and thermal transport in monolayer C$_{24}$ networks from machine-learning molecular dynamics Phonon ; Isotropy ; Anisotropy ; Molecular dynamics ; van der Waals force ; Monolayer ; Intermolecular force ; Elasticity (physics) ; Phase (matter) Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Ramasamy Murugesan | Ewald Janssens | Joris Van de Vondel | Michel Houssa
Source: Physical Chemistry Chemical Physics (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Room temperature dynamics of atomic clusters adsorbed on graphene: a machine-learning force field molecular dynamics study Clusters adsorbed on graphene can alter the electronic and spin-based properties of this two-dimensional material by proximity interactions. However, at room temperature, the adsorbed clusters tend to move freely over the surface. First-principles molecular dynamics simulations are performed to study the motion of size-selected Au<sub><i>n</i></sub> (<i>n</i> = 3 and 6) clusters adsorbed on the graphene surface. While the clusters are found to be diffusive along the surface of pristine graphene, the presence of C-vacancies in graphene drastically suppresses the diffusion of the clusters and "anchors" them to the vacancy site. Besides carbon vacancies, the presence of resist residues like methyl methacrylate, which is commonly used in the fabrication process of nanoscale devices, was also considered in this work. The presence of residues on the surface of graphene strongly reduces the diffusion of the Au<sub><i>n</i></sub> clusters. Graphene ; Molecular dynamics ; Adsorption ; Vacancy defect ; Diffusion ; Nanoscopic scale ; Surface diffusion ; Carbon fibers Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Graphene research and applications
Authors: Swarup Maity (18932604) | Bhanendra Sahu (16612368) | Nishikanta Singh (21953579) | Sudev Kumar Padhi (22786814) | SK Subidh Ali (22786817) | Sanjib Banerjee (1544416)
Source: Figshare (article, 2025-12-05)
Field: Materials Science — Polymers and Plastics

Fluorescent-Tagged Amphiphilic Polymers for Dual-Functional <i>para</i>-Nitrophenol Sensing and Remediation The development of multifunctional polymeric platforms for toxin detection and environmental remediation is crucial for advancing sustainable pollutant monitoring and removal technologies. <i>Para</i>-nitrophenol (PNP) is a highly toxic environmental contaminant, posing severe health risks, including headaches, cyanosis, and, with prolonged exposure, fatal consequences. Despite extensive research on PNP detection and removal, no prior studies have explored amphiphilic polymers for both solution- and solid-state sensing, adsorption, and deep learning-assisted detection. Here, for the first time, we report the synthesis of poly(methacrylic acid-<i>random</i>-7-methacryloyloxycoumarin) (PCM10S), a fluorescent-active amphiphilic polymer designed for selective and highly sensitive PNP detection. The incorporation of a coumarin-based fluorophore within the polymer backbone ensures exceptional selectivity among aromatic compounds, achieving an ultralow limit of detection (LOD = 20 nM). To enhance pollutant removal, a modified variant of PCM10S, with increased hydrophobic content, was synthesized, promoting PNP encapsulation within polymeric aggregates. This structural modification facilitates efficient sedimentation, achieving ∼96% PNP removal from aqueous solutions with a removal capacity of 0.144 mg PNP per mg of the adsorbent. Furthermore, a paper-based sensor was developed for rapid, user-friendly PNP detection. To enhance detection accuracy, deep learning integration was explored, evaluating seven convolutional neural networks (CNNs) for fluorescence-based colorimetric analysis. Among them, InceptionNet achieved the highest classification accuracy, demonstrating the potential of AI-assisted pollutant monitoring. This study introduces a synergistic approach combining fluorescence sensing and deep learning, paving the way for next-generation smart environmental monitoring. The methodology holds promise for detecting and removing a broader range of organic contaminants, offering a scalable and sustainable solution for intelligent wastewater treatment. Amphiphile ; Environmental remediation ; Polymer ; Fluorophore ; Pollutant ; Aqueous solution ; Detection limit Conducting polymers and applications ; Luminescence and Fluorescent Materials ; Covalent Organic Framework Applications
Authors: Yury Gogotsi
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

<i>(Nanocarbons Division Robert C. Haddon Research Award)</i> MXenes and Their Hybrids with Graphene and Other Materials 2D carbides, nitrides, and carbonitrides of early transition metals known as MXenes are the most chemically and structurally diverse family of inorganic materials discovered this century. MXenes are among the few nanomaterials that have jumped into the limelight not only because of their 2D morphology, exotic structures or attractive properties but also because of numerous practical applications. The family of MXenes has been expanding rapidly since the discovery of Ti 3 C 2 at Drexel University in 2011. About 80 different MXenes have been reported, dozens of solid solutions have been made, and the structure and properties of numerous other MXenes have been predicted. The availability of solid solutions on M and X sites, multi-element high-entropy MXenes with up to 9 metals, control of surface terminations, and the discovery of out-of-plane ordered double-M o -MXenes (e.g., Mo 2 TiC 2 ), as well as in-plane ordered i -MAX phases and their i -MXenes offer the potential for producing an almost infinite number of new 2D materials. This presentation will describe the state of the art in the synthesis of those MXenes by wet chemical and molten salt etching of MAX phases, CVD, and topochemical transformation of graphite and oxides. Their delamination into single-layer 2D flakes and assembly into films will also be covered. Synthesis-structure-properties relations of MXenes will be described. Hydrophilic surfaces of wet chemically etched MXenes allow environmentally friendly and scalable manufacturing and processing of MXenes from dispersions in water, with no surfactant or binder added. The hydrophobic halogen-terminated MXenes are better dispersible in organic solvents. The versatile chemistry of the MXene family renders their properties tunable for a large variety of energy-related, electronic, optical, biomedical, and other applications, but the synthesis method, surface termination and delamination largely determine the properties and the range of applications of MXenes. Finally, hybridization of MXenes with graphene, cellulose nanofibers, metals and polymers allows design of multifunctional materials with unique combinations of properties (often, mutually exclusive) that are not available in conventional materials. We expect artificial intelligence and machine learning to guide defelopment of a new generation of multifunctional materials assembled from an almost infinite number of 2D building blocks like MXenes. Division (mathematics) ; MXenes ; Graphene ; Management ; Materials science ; Nanotechnology ; Economics ; Mathematics MXene and MAX Phase Materials ; Nanomaterials for catalytic reactions ; Graphene research and applications