jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lang Xiang | Pengfei Qu
Source: ACS Omega (article, 2025-08-08)
Field: Chemistry — Spectroscopy

Kinetic Modeling and Multiobjective Optimization of Ibuprofen Synthesis Using Machine Learning This study presents a comprehensive application of integrated machine learning tools for modeling and optimizing the ibuprofen synthesis process. Initially, a database of 39,460 input combinations is created using chemical reaction theory and validated with experimental data. The CatBoost meta-model, optimized by the snow ablation optimizer, outperforms conventional algorithms in predicting reaction time, conversion rate, and production cost. Importance analyses through SHAP values identify critical input variables, notably, the concentration of the catalyst precursor (L<sub>2</sub>PdCl<sub>2</sub>), hydrogen ions (H<sup>+</sup>), and water (H<sub>2</sub>O), validating known catalytic principles and providing quantitative parameter guidance through data-driven analysis. Multiobjective optimization using NSGA-II generates a Pareto front of solutions, from which four industrial strategies are derived: balanced performance, maximum output, maximum yield, and minimum cost, each suitable for different production scenarios. The results identify optimal catalyst concentration ranges (0.002-0.01 mol/m<sup>3</sup>) that achieve high conversion rates while maintaining low costs. Uncertainty analysis conducted through Monte Carlo simulation reveals that reaction time exhibits particularly high sensitivity to parameter fluctuations, with a distinctive nonlinear response peaking at moderate perturbation levels (σ = 0.3). This study provides valuable insights for the rational design of ibuprofen synthesis conditions and demonstrates the effectiveness of integrating physics-based modeling with machine learning for chemical process optimization. Ibuprofen ; Multi-objective optimization ; Computer science ; Artificial intelligence ; Mathematical optimization ; Biochemical engineering ; Machine learning ; Engineering ; Mathematics ; Biology Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Arif Ullah | Rajibul Islam | Ghulam Hussain | Zahir Muhammad | Xiaoguang Li | Ming Yang
Source: arXiv (Cornell University) (preprint, 2025-11-06)
Field: Materials Science — Materials Chemistry

TXL Fusion: A Hybrid Machine Learning Framework Integrating Chemical Heuristics and Large Language Models for Topological Materials Discovery Topological materials--including insulators (TIs) and semimetals (TSMs)--hold immense promise for quantum technologies, yet their discovery remains constrained by the high computational cost of first-principles calculations and the slow, resource-intensive nature of experimental synthesis. Here, we introduce TXL Fusion, a hybrid machine learning framework that integrates chemical heuristics, engineered physical descriptors, and large language model (LLM) embeddings to accelerate the discovery of topological materials. By incorporating features such as space group symmetry, valence electron configurations, and composition-derived metrics, TXL Fusion classifies materials across trivial, TSM, and TI categories with improved accuracy and generalization compared to conventional approaches. The framework successfully identified new candidates, with representative cases further validated through density functional theory (DFT), confirming its predictive robustness. By uniting data-driven learning with chemical intuition, TXL Fusion enables rapid and interpretable exploration of complex materials spaces, establishing a scalable paradigm for the intelligent discovery of next-generation topological and quantum materials. Scalability ; Heuristics ; Topological insulator ; Generalization ; Topological data analysis ; Quantum chemical ; Topology (electrical circuits) ; Fusion Machine Learning in Materials Science ; Topological Materials and Phenomena ; Topological and Geometric Data Analysis
Authors: Guangyi Dong | Zhihui Wang
Source: ArXiv.org (preprint, 2025-11-14)
Field: Materials Science — Materials Chemistry

Toward Multi-Fidelity Machine Learning Force Field for Cathode Materials Machine learning force fields (MLFFs), which employ neural networks to map atomic structures to system energies, effectively combine the high accuracy of first-principles calculation with the computational efficiency of empirical force fields. They are widely used in computational materials simulations. However, the development and application of MLFFs for lithium-ion battery cathode materials remain relatively limited. This is primarily due to the complex electronic structure characteristics of cathode materials and the resulting scarcity of high-quality computational datasets available for force field training. In this work, we develop a multi-fidelity machine learning force field framework to enhance the data efficiency of computational results, which can simultaneously utilize both low-fidelity non-magnetic and high-fidelity magnetic computational datasets of cathode materials for training. Tests conducted on the lithium manganese iron phosphate (LMFP) cathode material system demonstrate the effectiveness of this multi-fidelity approach. This work helps to achieve high-accuracy MLFF training for cathode materials at a lower training dataset cost, and offers new perspectives for applying MLFFs to computational simulations of cathode materials. Cathode ; Field (mathematics) ; Artificial neural network ; Computational complexity theory ; Computational model ; Battery (electricity) ; Potential field Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advancements in Battery Materials
Authors: Souad Tayane | Jaafar Gaber
Source: Springer Link (Chiba Institute of Technology) (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

SymChemAI: A Symbolically Informed Neural Network for Physicochemical Modeling of Reaction Systems Artificial-intelligence modeling of chemical reactions is advancing rapidly but typically depends on large, costly experimental datasets. We introduce SymChemAI, a chemically informed neural network that simulates reaction dynamics directly from symbolic equations. SymChemAI automatically maps balanced reactions to a system of ordinary differential equations derived from fundamental physicochemical laws. Its objective functions explicitly enforce kinetic rate laws, thermodynamic constraints, mass conservation, non-negativity of concentrations, and compliance with initial conditions. This physics-anchored design contrasts with conventional PINN or Transformer approaches by prioritizing hard scientific constraints and improving chemical interpretability. Without requiring experimental supervision, SymChemAI predicts conversions, yields, and full concentration–time profiles with consistent physical behavior. The framework unifies symbolic reasoning and deep learning, enabling virtual screening, process optimization, and robust reaction design in silico. A forthcoming digital workflow further supports transparency and reproducibility. This work is available as a preprint on ChemRxiv [1], accelerating community feedback and benchmarking. Overall, SymChemAI offers a modular, data-light route to credible reaction simulation that adheres to first principles while retaining the flexibility and expressivity of modern neural networks. Workflow ; Artificial neural network ; Flexibility (engineering) ; Process (computing) ; Transparency (behavior) ; Deep neural networks ; Ordinary differential equation ; Work in process Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Anis Ismail (22450495) | Joe Tekli (22450498) | Brigitte Wex (2513407)
Source: Figshare (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Automating Deep Learning-Based Generation and Evaluationof De Novo Chemical Reaction with ChemRxnSAGE The generation and evaluation of chemical reactions remain challenging with limited comprehensive studies addressing these issues. We introduce the <b>Chem</b>ical Reaction (<b>Rxn</b>) <b>S</b>ystematic <b>A</b>ssessment of <b>G</b>eneration and <b>E</b>valuation (<b>ChemRxnSAGE</b>) framework, an adaptable end-to-end approach for evaluating the quality, validity, and diversity of machine-generated chemical reactions. Combining automated validity filters with quality metrics and expert insights, ChemRxnSAGE systematically eliminates invalid reactions. We tested its robustness using generative models, including Recurrent Neural Networks and Variational Autoencoders, followed by validation using a chemical “Turing test” with domain experts. Additionally, we assess reaction feasibility through thermodynamic analysis and compare the generated reactions against existing literature to ensure relevance and novelty. By combination of computational tools with expert-driven metrics, ChemRxnSAGE offers a comprehensive and extendable solution that advances the state of chemical reaction generation and evaluation. Robustness (evolution) ; Artificial neural network ; Relevance (law) ; Chemical process ; Domain (mathematical analysis) ; Chemical reaction ; Applicability domain Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Md. Tanbir Hasan | Mahmudul Hasan Rohan | Md Mahfuzur Rahman | Shahriar Islam Sium | Abdullah Mohammad Khaled | Tanvir Hossain | Chaity Dey | Akteruzzaman Nadim
Source:  (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

A Machine Learning Approach to Predict the Tensile Strength of Aluminum Alloys This study explores a comprehensive machine learning (ML) framework for predicting the tensile strength of aluminum alloys using chemical composition and processing without the high cost of endless, trail-and-error experiments. An Extreme Gradient Boosting (XGBoost) algorithm, trained on a 1145 sample dataset we compiled from the Mendeley data repository. The data is a mix of chemical inputs (wt.% of Al, Cu, Zn) and processing history (like “Strain hardened” or Artificial Aged”). The goal was to see if the model could determine the complex, non-linear relationships that govern an alloy’s final strength. The results were very strong. On unseen data the model achieved an R2 of 0.9416, and the error (RSME) 37.30 MPa on the test set, demonstrating very good prediction accuracy across a wide range of tensile strength values (100-800 MPa). We were run a permutation importance analysis to see what it learned. We found the three most influential alloying elements were Copper (Cu), Aluminum (Al), and Zinc (Zn) respectively in determining tensile strength. We also run diagnostic check to confirmed the model is statistically sound and unbiased. The study yields significant insights into composition-property correlation and offers a reliable tool for accelerate aluminum alloy design and optimization. Keywords: Aluminum Alloys, Tensile Strength, Machine Learning, Extreme Gradient Boosting, Alloy Design Ultimate tensile strength ; Aluminium ; Deformation (meteorology) ; Aluminium alloy Machine Learning in Materials Science ; Metal Forming Simulation Techniques ; Material Selection and Properties
Authors: Xi He | Shuo Peng | Jinde Liu | Chenlong Qin | Yingying Chen | Yushu Yu | Liang Zhao | Gang Jiang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of mechanical properties of magnesium alloys based on machine learning Aerospace ; Artificial neural network ; Predictive modelling ; Field (mathematics) ; Magnesium ; Property (philosophy) ; Magnesium alloy ; Alloy Machine Learning in Materials Science ; Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials
Authors: Mahyar Rajabi Kochi | Hamid Rezaei | Sartaaj Takrim Khan | Bhanu Teja Mamillapalli | Maryam Ebrahimiazar | Haoming Ye | Rose Moosavian | Mohammad Zargartalebi | David Sinton | Seyed Mohamad Moosavi
Source: ChemRxiv (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

Thermodynamics-informed machine learning for predicting temperature-dependent chemical properties Emerging energy and electronic systems rely on the thermodynamic properties of chemical and cooling fluids. These properties are a function of both chemical structure and temperature. For instance, the dynamic viscosity of a fluid can vary by orders of magnitude across the operating range of a cooling system. However, capturing this behavior remains a challenge for experimental and modelling approaches. Machine learning models, although powerful for fixed temperatures, fail to generalize across temperatures due to a lack of data and a lack of embedded physical constraints. Here, we introduce a thermodynamics-informed machine learning framework that incorporates established physical relationships, such as the Arrhenius equation, to capture both chemical diversity and temperature dependence. We show that decoupling chemistry from thermodynamic conditions enables accurate prediction of temperature-dependent dynamic viscosity, which we validated experimentally. Through a materials-discovery campaign for cooling applications, we show that neglecting temperature effects can cause over 90% errors in performance evaluation, leading to inaccurate materials ranking and suboptimal fluid selection. Finally, we extend the framework to other properties, such as vapor pressure and diffusion coefficient, highlighting a generalizable strategy for accelerating fluid property prediction and design for sustainable technologies. Thermodynamics ; Chemistry ; Materials science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Lang Xiang (22021592) | Pengfei Qu (760507)
Source: Figshare (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Kinetic Modelingand Multiobjective Optimization ofIbuprofen Synthesis Using Machine Learning This study presents a comprehensive application of integrated machine learning tools for modeling and optimizing the ibuprofen synthesis process. Initially, a database of 39,460 input combinations is created using chemical reaction theory and validated with experimental data. The CatBoost meta-model, optimized by the snow ablation optimizer, outperforms conventional algorithms in predicting reaction time, conversion rate, and production cost. Importance analyses through SHAP values identify critical input variables, notably, the concentration of the catalyst precursor (L<sub>2</sub>PdCl<sub>2</sub>), hydrogen ions (H<sup>+</sup>), and water (H<sub>2</sub>O), validating known catalytic principles and providing quantitative parameter guidance through data-driven analysis. Multiobjective optimization using NSGA-II generates a Pareto front of solutions, from which four industrial strategies are derived: balanced performance, maximum output, maximum yield, and minimum cost, each suitable for different production scenarios. The results identify optimal catalyst concentration ranges (0.002–0.01 mol/m<sup>3</sup>) that achieve high conversion rates while maintaining low costs. Uncertainty analysis conducted through Monte Carlo simulation reveals that reaction time exhibits particularly high sensitivity to parameter fluctuations, with a distinctive nonlinear response peaking at moderate perturbation levels (σ = 0.3). This study provides valuable insights for the rational design of ibuprofen synthesis conditions and demonstrates the effectiveness of integrating physics-based modeling with machine learning for chemical process optimization. Multi-objective optimization ; Nonlinear system ; Monte Carlo method ; Sensitivity (control systems) ; Process (computing) ; Response surface methodology ; Pareto optimal Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Process Optimization and Integration
Authors: Xiaoyun Yu | Guang Yang | Zhuoying Zhao | Junhua Li | Xinyu Xiao | Xin Zhang | Jie Liu | Pinghui Mo
Source: Journal of Chemical Theory and Computation (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

NVNMD-v2: Scalable and Accurate Deep Learning Molecular Dynamics Model Based on Non-Von Neumann Architectures Molecular dynamics (MD) simulations have emerged as a transformative computational microscope for probing atomic interactions spanning catalysis, energy storage, biotechnology, and beyond. However, existing machine-learning MD (MLMD) frameworks face a trilemma in balancing accuracy, scalability, and energy efficiency, particularly in compositionally complex systems like high-entropy alloys and multiferroic perovskites. Here, we introduce NVNMD-v2, a co-designed algorithm-hardware architecture that integrates a generalized deep neural-network potential (GDNNP) within a processing-in-memory (PIM) accelerator. Building on the foundation of NVNMD-v1, which was limited to four-element systems, NVNMD-v2 employs optimized type-embedding descriptors to support multielement systems with up to 32 species, eliminating species-dependent parameter scaling. Deployed on a single FPGA, NVNMD-v2 maintains DFT-level accuracy while achieving a flat per-atom computational cost (∼10<sup>-7</sup> s/step/atom), enabling simulations of system up to 20 million atoms─a 10<sup>3</sup> × scale-up over DeePMD on an NVIDIA V100 GPU, with ∼120 × energy reduction. These advances unlock quantum-accurate MD for multielement materials, from semiconductor heterostructures to biomolecular assemblies, bridging the gap between atomic fidelity and industrial-scale simulations. Scalability ; Computer science ; Deep learning ; Dynamics (music) ; Von Neumann architecture ; Molecular dynamics ; Artificial intelligence ; Computational science ; Data science ; Computer architecture ; Chemistry ; Physics ; Computational chemistry ; Database Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Akram Abedi Orang | Mojtaba Alaei | Artem R. Oganov
Source: ArXiv.org (preprint, 2025-09-22)
Field: Materials Science — Materials Chemistry

Predicting the Curie Temperature of Magnetic Materials with Machine Learning: Descriptor Engineering, Graph Neural Networks, and the Role of Curated Data Predicting the Curie temperature ($T_\mathrm{C}$) of magnetic materials is crucial for advancing applications in data storage, spintronics, and sensors. We present a machine learning (ML) framework to predict $T_{\mathrm{C}}$ using a curated dataset of 2,500 ferromagnetic compounds, employing two types of elemental descriptor-based features: one based on stoichiometry-weighted descriptors, and the other leveraging Graph Neural Networks (GNNs). CatBoost trained on the stoichiometry-weighted descriptors achieved an $R^2$ score of 0.87, while the use of GNN-based representations led to a further improvement, with CatBoost reaching an $R^2$ of 0.91, highlighting the effectiveness of graph-based feature learning. We also demonstrated that using an uncurated dataset available online leads to poor predictions, resulting in a low $R^2$ score of 0.66 for the CatBoost model. We analyzed feature importance using tools such as Recursive Feature Elimination (RFE), which revealed that ionization energies are a key physicochemical factor influencing $T_\mathrm{C}$. Notably, the use of only the first 10 ionization energies as input features resulted in high predictive accuracy, with $R^2$ scores of up to 0.85 for statistical models and 0.89 for the GNN-based approach. These results highlight that combining robust ML models with thoughtful feature engineering and high-quality data, can accelerate the discovery of magnetic materials. Our curated dataset is publicly available on GitHub. Feature (linguistics) ; Curie temperature ; Graph ; Artificial neural network ; Feature engineering ; Pattern recognition (psychology) Machine Learning in Materials Science
Authors: Ming Xuan
Source: Preprints.org (preprint, 2025-04-24)
Field: Materials Science — Materials Chemistry

The More Complex, The Deeper: SchNet+ as an Improved Neural Network for Complexity in Quantum Materials Quantum materials present significant challenges due to their inherent complexity in physical systems. With the rapid development of deep learning, it is accessible to make prediction of the complicated behaviors in quantum materials. This paper proposed a new neural network SchNet+ to better narrate the complexity of systems, which can not be easily solved by traditional mathematical equations. Quantum ; Artificial neural network ; Computer science ; Cognitive science ; Artificial intelligence ; Psychology ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Ivan Žugec | Tin Hadži Veljković | M. Alducin | J. I. Juaristi
Source: ArXiv.org (preprint, 2025-04-04)
Field: Materials Science — Materials Chemistry

Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics Molecular Dynamics (MD) simulations are vital for exploring complex systems in computational physics and chemistry. While machine learning methods dramatically reduce computational costs relative to ab initio methods, their accuracy in long-lasting simulations remains limited. Here we propose dynamic training (DT), a method designed to enhance accuracy of a model over extended MD simulations. Applying DT to an equivariant graph neural network (EGNN) on the challenging system of a hydrogen molecule interacting with a palladium cluster anchored to a graphene vacancy demonstrates a superior prediction accuracy compared to conventional approaches. Crucially, the DT architecture-independent design ensures its applicability across diverse machine learning potentials, making it a practical tool for advancing MD simulations. Artificial neural network ; Molecular dynamics ; Ab initio ; Graph ; Training (meteorology) ; System dynamics ; Complex system ; Dynamics (music) Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Hongchen Ji | Anita Rágyanszki | René Fournier
Source: ChemRxiv (preprint, 2025-02-19)
Field: Chemistry — Spectroscopy

A Machine Learning and Quantum Chemistry Approach for Identifying Prebiotic Molecules in the Interstellar Medium The discovery of molecules in the interstellar medium (ISM) plays a key role in understanding prebiotic chemistry. Relatively few (~250) molecules have been confirmed in the ISM, and detecting additional species is crucial for expanding our knowledge of astrochemical processes. We present a strategy for predicting possible prebiotic molecules in the ISM that combines machine learning and high-accuracy quantum chemistry calculations. Using a reaction dataset of over 153,000 possible combinations of known interstellar molecules, we applied a machine learning model to estimate reaction energy barriers and identify those with low or zero barriers that are most likely to occur in the ISM. From this screening process, 24 molecules were identified, five of which have already been observed in interstellar space. For the remaining 19 molecules, we conducted density functional theory (DFT) and coupled cluster theory calculations to determine the most stable conformers, spectroscopic parameters, and predict their detectability through spectroscopy. We present data to guide future observational searches for new interstellar species, contributing to the ongoing exploration of complex organic molecules in space and their potential role in prebiotic chemistry. Astrobiology ; Interstellar medium ; Prebiotic ; Molecule ; Chemistry ; Chemical physics ; Nanotechnology ; Physics ; Astronomy ; Materials science ; Organic chemistry ; Food science Molecular spectroscopy and chirality ; Molecular Spectroscopy and Structure