jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siösteen, Casper | Vatankhah, Ilia
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for materials design in metal additive manufacturing Advancements of machine-learning models create opportunities for implementations of surrogate models for physical experiments. This spares not only resources and raw materials, but also time. This report follows the development of a machine learning model based on a neural network that predicts certain system properties through input data from CALPHAD calculated using Thermo-Calc software. The machine learning model is developed with the open-source framework TensorFlow. The goal is to predict the values for the partition coefficient k and liquidus slope m in different systems to calculate the growth restriction factor Q. This factor is of great importance in Ni-based alloys which are used in additive manufacturing. Q promotes isotropic properties within materials that are generally desired; Nibased alloys also show a lot of potential as they are commonly used is harsh environments. The surrogate model performs very well with the two provided datasets: an initial dataset for a ternary component system and an additional dataset for a multicomponent system. These show remarkably different results, as the multicomponent system seems to be much more dependent on noise-management and identifying outliers. This proves to be right, as when a noisemanagement method is added, the model’s performance with the multicomponent dataset improves substantially. The model is built on a neural network which on some principles replicates the human brain in a much more primitive manner. The network consists of nodes in layers that communicate to each other through transfer of data. The input layer receives data which is sent to the hidden layer(s). The nodes in the hidden layer perform functions that determine whether the data should be transferred, and if so to which node. The output layer generates a result which is a prediction by the model. There are different ways of regression analysis evaluation to determine whether the generated predictions are well determined by the model or not. Two of such methods are R2 and MAE. These evaluate how well the predictions are made based on the true data from the datasets. The model proves to have an adequate performance when put under regression analysis evaluation. Liquidus ; Artificial neural network ; Component (thermodynamics) ; Layer (electronics) ; Ternary operation ; Surrogate model ; Partition (number theory) ; Complex system ; Data modeling Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Additive Manufacturing and 3D Printing Technologies
Authors: Daniel Julian | Jesús Pérez-Ríos
Source: Physical Review Research (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Machine learning prediction of a chemical reaction over 8 decades of energy Recent progress in machine learning has sparked increased interest in utilizing this technology to predict the outcomes of chemical reactions. The ultimate aim of such endeavors is to develop a universal model that can predict products for any chemical reaction given reactants and physical conditions. In pursuit of ever more universal chemical predictors, machine learning models for atom-diatom and diatom-diatom reactions have been developed, yet no such models exist for termolecular reactions. Accordingly, we introduce neural networks trained to predict opacity functions of atom recombination reactions. Our models predict the recombination of <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:msup> <a:mrow> <a:mi>Sr</a:mi> </a:mrow> <a:mo>+</a:mo> </a:msup> <a:mspace width="4pt"/> <a:mo>+</a:mo> <a:mspace width="4pt"/> <a:mrow> <a:mi>Cs</a:mi> <a:mspace width="0.16em"/> <a:mo>+</a:mo> </a:mrow> <a:mspace width="4pt"/> <a:mrow> <a:mi>Cs</a:mi> <a:mo>→</a:mo> </a:mrow> <a:mspace width="4pt"/> <a:msup> <a:mrow> <a:mi>SrCs</a:mi> </a:mrow> <a:mo>+</a:mo> </a:msup> <a:mspace width="4pt"/> <a:mo>+</a:mo> </a:math> Cs and <h:math xmlns:h="http://www.w3.org/1998/Math/MathML"> <h:msup> <h:mrow> <h:mi>Sr</h:mi> </h:mrow> <h:mo>+</h:mo> </h:msup> <h:mspace width="4pt"/> <h:mo>+</h:mo> <h:mspace width="4pt"/> <h:mrow> <h:mi>Cs</h:mi> <h:mspace width="0.16em"/> <h:mo>+</h:mo> </h:mrow> <h:mspace width="4pt"/> <h:mrow> <h:mi>Cs</h:mi> <h:mo>→</h:mo> </h:mrow> <h:mspace width="4pt"/> <h:msub> <h:mi>Cs</h:mi> <h:mn>2</h:mn> </h:msub> <h:mspace width="4pt"/> <h:mo>+</h:mo> <h:mspace width="4pt"/> <h:msup> <h:mrow> <h:mi>Sr</h:mi> </h:mrow> <h:mo>+</h:mo> </h:msup> </h:math> over multiple orders of magnitude of energy, yielding overall results with a relative error <p:math xmlns:p="http://www.w3.org/1998/Math/MathML"> <p:mrow> <p:mo>≲</p:mo> <p:mn>10</p:mn> <p:mo>%</p:mo> </p:mrow> </p:math> . Even far beyond the range of energies seen during training, our models predict the atom recombination reaction rate accurately. As a result, the machine is capable of learning the physics behind the atom recombination reaction dynamics. Artificial neural network ; Range (aeronautics) ; Atom (system on chip) ; Chemical reaction ; Energy (signal processing) ; Recombination ; Chemical energy Machine Learning in Materials Science ; Diatoms and Algae Research ; Algal biology and biofuel production
Authors: Lowie Tomme | István Lengyel | Florence H. Vermeire | Christian V. Stevens | Kevin M. Van Geem
Source: Machine Learning Science and Technology (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Accelerating reaction rate predictions: a machine learning approach using a novel quantum chemical property for nucleophilic aromatic substitutions Abstract Accurately predicting reaction rate coefficients is crucial for various chemical engineering tasks, such as kinetic modeling and drug synthesis planning. Traditional methods like group additivity and rate rules have limitations, prompting the exploration of machine learning methods to predict these coefficients. These machine learning models are often combined with quantum chemical calculations to improve their performance. While often accurate, these approaches slow down predictions due to the need for quantum chemical calculations, particularly for transition states. This study addresses the issue by introducing a quantum chemical property that does not require transition state calculations but still correlates well with the rate coefficient. We further enhance the prediction speed by training a machine learning model to predict this property. Finally, we propose two approaches using this machine learning model to predict the rate coefficients of nucleophilic aromatic substitution reactions in fractions of a second. Nucleophile ; Quantum chemical ; Property (philosophy) ; Quantum ; Computer science ; Chemistry ; Biological system ; Computational chemistry ; Artificial intelligence ; Physics ; Biology ; Organic chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Shengjun Huo | Zhiwei Xiong | Xin Zhang | Meng Yu | Xiaoyan Liu | Meiling Hou
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerated Screening of Hydrogen Evolution Reaction Catalysts in Carbon Nanotube Materials Based on Dft Calculations and Machine Learning Catalysis ; Carbon nanotube ; Hydrogen ; Materials science ; Nanotechnology ; Nanotube ; Carbon fibers ; Chemical engineering ; Chemical physics ; Chemistry ; Engineering ; Organic chemistry ; Composite material Machine Learning in Materials Science
Authors: Li, Zicun | Gong, Huanjing | Xiao, Ruijuan | Ren, Xinguo
Source: ArXiv.org (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Influence of Exchange-Correlation Functionals and Neural Network Architectures on Li$^+$-Ion Conductivity in Solid-State Electrolyte from Molecular Dynamics Simulations with Machine-Learning Force Fields With the rapid advancement of machine learning techniques for materials simulations, machine-learned force fields (MLFFs) have become a powerful tool that complements first-principles calculations by enabling high-accuracy molecular dynamics simulations over extended timescales. Typically, MLFFs are trained on data generated from density functional theory (DFT) using a specific exchange-correlation (XC) functional, with the goal of reproducing DFT-level properties. However, the uncertainties in MLFF-based simulations--arising from variations in both MLFF model architectures and the choice of XC functionals--remain insufficiently understood. In this work, we construct MLFF models of different architectures trained on DFT data from both semilocal and hybrid functionals to describe Li$^+$ diffusion in the solid-state electrolyte Li$_6$PS$_5$Cl. We systematically investigate how different XC functionals influence the Li$^+$ diffusion coefficient. To reduce statistical uncertainty, the mean squared displacements are averaged over 300 independent molecular dynamics (MD) trajectories of 70 ps each, yielding statistical variations below $1\%$. This enables a clear assessment of the respective influences of the functional and the MLFF model. Due to its tendency to underestimate band gaps and migration barriers, the semilocal functional predicts consistently higher Li$^+$ diffusion coefficients, compared to the hybrid functional. Furthermore, comparisons among various neural network methods reveal that the differences in predicted diffusion coefficients arising from different network architectures are of the same order of magnitude as those caused by different functionals, indicating that the choice of the network model itself substantially influences the MLFF predictions. This observation calls from an urgent need for standardized protocols to minimize model-dependent biases in MLFF-based MD. Diffusion ; Molecular dynamics ; Artificial neural network ; Density functional theory ; Field (mathematics) ; Work (physics) ; Electrolyte ; Force field (fiction) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: H. Y. Mark Liao | Yen-Hsiang Lin | Chi‐How Peng | Yi‐Pei Li
Source: ChemRxiv (preprint, 2025-07-24)
Field: Chemistry — Analytical Chemistry

Directed Message Passing Neural Networks for Accurate Prediction of Polymer-Solvent Interaction Parameters Accurate prediction of polymer-solvent interactions is essential for applications such as polymer processing, drug delivery, and membrane separations. The Flory-Huggins interaction parameter (χ parameter) serves as a key descriptor for polymer-solvent compatibility; however, its experimental determination is often costly and time-consuming. In this study, we develop a machine learning framework based on Directed Message Passing Neural Networks (D-MPNNs) to predict χ parameters directly from molecular structures, temperature, and volume fraction. Our approach systematically evaluates different feature representations, pooling methods, and empirical equation integrations to optimize prediction accuracy. Among the tested models, D-MPNN-TC, which incorporates both temperature and volume fraction, demonstrates strong predictive performance (MAE = 0.092, RMSE = 0.162, R² = 0.926), outperforming descriptor-based models that rely on precomputed molecular fingerprints and handcrafted chemical features. Additionally, integrating the Flory-Huggins equation into the classification framework enables highly accurate miscibility predictions, with F1 scores of 0.915. Further analysis using t-SNE visualization reveals that D-MPNNs effectively capture key structural features, such as aromaticity and cyclic structures, that influence polymer-solvent interactions. Our findings highlight the advantages of graph-based molecular representations over traditional fingerprinting methods and underscore the importance of volume fraction information in predicting polymer-solvent compatibility. This study provides a scalable and interpretable framework for leveraging machine learning in polymer science, facilitating data-driven solvent selection and polymer design. Artificial neural network ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Water Quality Monitoring and Analysis
Authors: Qinghe Jia | Hongguang Liu | Xue‐Ping Wang | Qiantu Tao | Lifeng Zheng | Junjie Li | Wei Wang | Ziteng Liu | Xu Gu | Tianyu Shen | Shaoyi Hou | Zhong Jin | Jing Ma
Source: Angewandte Chemie (article, 2025-02-11)
Field: Materials Science — Materials Chemistry

Machine Learning‐Driven Mass Discovery and High‐Throughput Screening of Fluoroether‐Based Electrolytes for High‐Stability Lithium Metal Batteries Abstract Developing novel fluoroether electrolytes with high‐voltage stability is an effective strategy to improve the performance of lithium metal batteries (LMB). However, the vast chemical space of fluoroether is underexplored due to the absence of effective tools to evaluate the potential used in high‐voltage LMB. Herein, a framework was developed in combination of Voting ensemble algorithms and graph convolution neural network (GCNN), allowing the fast assessment of oxidative stability of non‐aqueous liquid electrolytes, synthesizability of solvents as well as the solvation ability of them to dissolve lithium salts. Potential fluoroether solvent candidates for high‐voltage LMB were screened out from a virtual library comprising 5576 electrolytes constructed by a combination of 1510 solvents and 4 salts. Among them, two fluorinated ethers, 1,1,1,3,3,3‐hexafluoro‐2‐(2‐methoxyethoxy) propane and 7,7,8,8‐tetrafluoro‐3,12‐dimethoxy‐2,5,10,13‐tetraoxatetradecane, were successfully synthesized and showed satisfactory high‐voltage stability, sufficient solvation ability and satisfactory cycling with almost 99.5 % coulombic efficiency in Li||NMC811 full cell. This work provided an efficient framework for the discovery of solvents with high‐voltage tolerance in a vast structural space prior to experimental synthesis, accelerating the development of advanced electrolyte for high‐energy‐density rechargeable batteries. Throughput ; Lithium metal ; Lithium (medication) ; High-throughput screening ; Electrolyte ; Chemistry ; Nanotechnology ; Drug discovery ; Materials science ; Computer science ; Electrode ; Biology ; Telecommunications Machine Learning in Materials Science ; Advanced Battery Materials and Technologies
Authors: Yutong Song | Yewei Ding | Junyi Su | Jian Li | Yuanhui Ji
Source: Angewandte Chemie (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 Abstract 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. Chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Li, Zicun | Gong, Huanjing | Xiao, Ruijuan | Ren, Xinguo
Source: arXiv (Cornell University) (preprint, 2025-12-12)
Field: Materials Science — Materials Chemistry

Influence of Exchange-Correlation Functionals and Neural Network Architectures on Li$^+$-Ion Conductivity in Solid-State Electrolyte from Molecular Dynamics Simulations with Machine-Learning Force Fields With the rapid advancement of machine learning techniques for materials simulations, machine-learned force fields (MLFFs) have become a powerful tool that complements first-principles calculations by enabling high-accuracy molecular dynamics simulations over extended timescales. Typically, MLFFs are trained on data generated from density functional theory (DFT) using a specific exchange-correlation (XC) functional, with the goal of reproducing DFT-level properties. However, the uncertainties in MLFF-based simulations--arising from variations in both MLFF model architectures and the choice of XC functionals--remain insufficiently understood. In this work, we construct MLFF models of different architectures trained on DFT data from both semilocal and hybrid functionals to describe Li$^+$ diffusion in the solid-state electrolyte Li$_6$PS$_5$Cl. We systematically investigate how different XC functionals influence the Li$^+$ diffusion coefficient. To reduce statistical uncertainty, the mean squared displacements are averaged over 300 independent molecular dynamics (MD) trajectories of 70 ps each, yielding statistical variations below $1\%$. This enables a clear assessment of the respective influences of the functional and the MLFF model. Due to its tendency to underestimate band gaps and migration barriers, the semilocal functional predicts consistently higher Li$^+$ diffusion coefficients, compared to the hybrid functional. Furthermore, comparisons among various neural network methods reveal that the differences in predicted diffusion coefficients arising from different network architectures are of the same order of magnitude as those caused by different functionals, indicating that the choice of the network model itself substantially influences the MLFF predictions. This observation calls from an urgent need for standardized protocols to minimize model-dependent biases in MLFF-based MD. Diffusion ; Molecular dynamics ; Artificial neural network ; Density functional theory ; Field (mathematics) ; Work (physics) ; Electrolyte ; Force field (fiction) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Souad Tayane | Jaafar Gaber
Source: E3S Web of Conferences (article, 2025-01-01)
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 ; Scientific Computing and Data Management
Authors: H. Y. Mark Liao | Yen-Hsiang Lin | Chi‐How Peng | Yi‐Pei Li
Source: ChemRxiv (preprint, 2025-04-24)
Field: Chemistry — Analytical Chemistry

Directed Message Passing Neural Networks for Accurate Prediction of Polymer-Solvent Interaction Parameters Accurate prediction of polymer-solvent interactions is essential for applications such as polymer processing, drug delivery, and membrane separations. The Flory-Huggins interaction parameter (χ parameter) serves as a key descriptor for polymer-solvent compatibility; however, its experimental determination is often costly and time-consuming. In this study, we develop a machine learning framework based on Directed Message Passing Neural Networks (D-MPNNs) to predict χ parameters directly from molecular structures, temperature, and volume fraction. Our approach systematically evaluates different feature representations, pooling methods, and empirical equation integrations to optimize prediction accuracy. Among the tested models, D-MPNN-TC, which incorporates both temperature and volume fraction, achieves the highest predictive performance (MAE = 0.092, RMSE = 0.162, R² = 0.926), outperforming descriptor-based models that rely on precomputed molecular fingerprints and handcrafted chemical features. Additionally, integrating the Flory-Huggins equation into the classification framework enables highly accurate miscibility predictions, with F1 scores of 0.915. Further analysis using t-SNE visualization reveals that D-MPNNs effectively capture key structural features, such as aromaticity and cyclic structures, that influence polymer-solvent interactions. Our findings highlight the advantages of graph-based molecular representations over traditional fingerprinting methods and underscore the importance of volume fraction information in predicting polymer-solvent compatibility. This study provides a scalable and interpretable framework for leveraging machine learning in polymer science, facilitating data-driven solvent selection and polymer design. Artificial neural network ; Computer science ; Message passing ; Artificial intelligence ; Deep neural networks ; Distributed computing Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Water Quality Monitoring and Analysis