jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: To Anh Duc
Source: Journal of Science Natural Science (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

MACHINE LEARNING ACCELERATED PROPERTY PREDICTION AND SCREENING OF STABLE LITHIUM-BASED BATTERY MATERIALS Rapid screening of battery materials is critical to meeting the growing demand for high-performance energy-storage solutions. In this study, we employ a data-driven approach to evaluate and characterize stable lithium-containing compounds with potential applications in next-generation batteries. We retrieved a dataset of 1,846 stable lithium-based materials from the Materials Project database using the mp-api. By leveraging the matminer library, we generated a comprehensive set of composition-based and structure-based features, including elemental- property and density descriptors. A Random Forest Regressor was trained to predict the band gap of these materials, a key electronic property that determines their suitability as electrolytes or electrodes. The model achieved a mean coefficient of determination (R2) of 0.77 ± 0.04 and a mean absolute error (MAE) of 0.48 ± 0.05 eV across 10-fold cross-validation, demonstrating robust predictive capability. Feature- importance analysis revealed that electronegativity and Mendeleev number are the most significant predictors of band-gap energy. These findings underscore the effectiveness of machine learning in accelerating property prediction for battery materials and provide a pathway to more efficient experimental targeting. Electronegativity ; Random forest ; Property (philosophy) ; Battery (electricity) ; Key (lock) ; Stability (learning theory) ; Training set Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Jia Liu | Dan Wu | Longlong Ma | Lungang Chen
Source: ChemistrySelect (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Machine Learning‐Driven Innovations in Energy and Environmental Catalysis: A Minireview ABSTRACT The integration of machine learning (ML) with density functional theory (DFT) is transforming the landscape of catalysis research, offering new avenues for the design and optimization of catalytic materials. This review explores the application of ML techniques in both nanoscale and macroscale catalyst design, highlighting how these methods predict catalyst performance, optimize reaction conditions, and accelerate high‐throughput screening. This review discusses the role of DFT in providing detailed insights into electronic structures and reaction mechanisms, which, when combined with ML, enhance the efficiency and precision of catalyst development. Case studies, including methanation reactions and selective catalytic reduction, illustrate the successful implementation of ML‐driven models in real‐world applications. Furthermore, the challenges and limitations of this combined approach were addressed, such as data quality, descriptor selection, model interpretability, and experimental validation. This review underscores the potential of ML and DFT to advance energy and environmental catalysis, while also identifying key areas for future research. Methanation ; Catalysis ; Key (lock) ; Energy (signal processing) ; Density functional theory ; Scale (ratio) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Ankit Ankit | Amritpal Singh
Source: Indian Journal of Heterocyclic Chemistry (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Integrating artificial intelligence and machine learning VM allocation in the chemical industry using heterocyclic chemistry - A narrative mini-review The chemical industry is rapidly transitioning toward cloud-enabled and high-performance computational infrastructures to support large-scale simulations, molecular modeling, reaction optimization, and data-intensive research. Efficient virtual machine (VM) allocation has become essential for improving throughput, scalability, costefficiency, and energy utilization for quantum chemical calculations, molecular docking, and virtual screening involving heterocyclic libraries. This narrative review examines the integration of artificial intelligence (AI) and machine learning (ML) techniques in VM allocation within chemical industry settings, with particular emphasis on heterocyclic compound research and computational chemistry applications. Drawing upon numerous documented applications of VM-based cloud deployment in chemical and materials science workflows, the review synthesizes current advances in AI-driven scheduling algorithms, predictive resource provisioning, reinforcement learning-based allocation models, intelligent load balancing, and adaptive auto-scaling strategies. It further discusses how ML-enhanced VM orchestration supports computational tasks in heterocyclic chemistry, including reaction pathway prediction, molecular property modeling, drug discovery simulations, and process optimization. Overall, integrating AI and ML with VM allocation represents a promising and scalable approach for advancing intelligent, efficient, and sustainable computational infrastructures within the modern chemical industry.. KEYWORDS :Artificial intelligence, Machine learning, Virtual machines, Cloud computing, Resource allocation, Chemical industry, Heterocyclic compounds, Computational chemistry, High-performance computing, Predictive modeling. Cloud computing ; Chemical database ; Scheduling (production processes) ; Orchestration ; Hyper-heuristic ; Virtual machine ; Virtual screening ; Computational model ; Scalability ; Reinforcement learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Martín Otero-Lema | Raúl Lois-Cuns | Ángel Míguez-Roel | E. Lomba | Trinidad Méndez‐Morales | L. González Varela | Hadrián Montes‐Campos
Source: ChemRxiv (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Neural Network-based Simulations of Solid Electrolyte Interfaces The solid-electrolyte interface (SEI) is a crucial component of electrochemical devices, but its computational description remains challenging due to the many chemical reactions that lead up to its formation. In this paper, we use a neural network forcefield, NeuralIL, to carry out molecular dynamics simulations of the organic layers of a model SEI based on ethylcarbonate and different metal cations, namely lithium, sodium and potassium. The need for an explicit interface is bypassed by introducing reduced electrolyte molecules into the system, whose presence starts a subsequent reaction chain. A series of reaction pathways are identified, leading to a predicted composition of the SEI that is in very good agreement with experimental data from the literature. Moreover, analysis of the structural and dynamic properties reveals key differences between the different metals, such as the presence of additional reactions and chemical species, shifts in the charge distribution of the different molecules, changes in the solvation shells of the metal cations and their impact on the diffusion coefficients. Electrolyte ; Solvation ; Molecular dynamics ; Electrochemistry ; Diffusion ; Chemical reaction ; Molecule ; Component (thermodynamics) ; Interface (matter) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Electrostatics and Colloid Interactions
Authors: Soumyadipta Pal | Ayan Kumar Panja
Source: International Journal of Modern Physics B (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Machine learning technique for prediction of crystal structure in austenitic and martensitic phase of Ni–Mn-based Heusler alloys Crystal structure prediction in multifunctional materials such as Ni–Mn-based Heusler alloys is paramount to increasing applications in spintronics magnetic refrigeration and smart systems. This work provides a machine learning (ML) model for classifying these alloys’ austenitic and martensitic phases based on structural compositional and thermal properties. Several supervised learning models such as Decision Tree Naive Bayes k-Nearest Neighbors (kNN) and Artificial Neural Network (ANN) were trained and tested using a dataset compiled from experimental and first-principles literature. In order to enhance prediction accuracy, an ensemble classifier using majority voting was also proposed. The performance of the models was enhanced by feature selection and hyperparameter optimization. With 97.2% accuracy, the ensemble model outperformed any individual classifier. The results demonstrate the ability of machine learning (ML) to understand complex phase behaviors and offer a reliable route to accelerating the design and discovery of high-performing Heusler alloys. Artificial neural network ; Austenite ; Decision tree ; Hyperparameter ; Naive Bayes classifier ; Ensemble learning ; Hyperparameter optimization ; Crystal structure Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties
Authors: YunZheng Zhou | Minwei Chen
Source: Microchemical Journal (article, 2026-03-06)
Field: Chemistry — Analytical Chemistry

AI-driven chemometric deep learning for monitoring oxidative degradation in polymer sports protective materials Partial least squares regression ; Interpretability ; Polymer ; Convolutional neural network ; Fourier transform infrared spectroscopy ; Principal component analysis ; Chemometrics ; Degradation (telecommunications) ; Deep learning Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Machine Learning in Materials Science
Authors: Biying Yan | Xiaowei Hou
Source: IET conference proceedings. (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Application and optimization of deep learning algorithms for mechanistic prediction of physicochemical reactions Accurate prediction of physical-chemical reaction mechanisms is a critical foundation for understanding and controlling chemical reaction processes, as well as advancing the development of new materials and drug design. Traditional methods based on quantum chemistry and molecular simulation face high computational costs, low efficiency, and limited applicability when dealing with complex reaction systems. As a key branch of artificial intelligence, deep learning has emerged as an important tool in reaction mechanism prediction due to its powerful feature extraction and pattern recognition capabilities. This paper provides a systematic review of the research progress and current applications of deep learning algorithms in the prediction of physical-chemical reaction mechanisms, with a focus on analyzing the advantages and limitations of deep learning models in reaction pathway prediction, reaction kinetics simulation, and reaction energy calculation. Addressing issues such as data scarcity, insufficient interpretability, and low computational efficiency in existing models, this paper proposes a systematic optimization approach encompassing data-driven optimization, model performance enhancement, and interpretability improvement. Combining the cross-disciplinary trends of artificial intelligence and physical chemistry, the paper also explores the broad application prospects of deep learning technology in automated reaction design, virtual screening, and the construction of closed-loop systems for experimental verification. This paper aims to provide new insights and technical support for future research on physical chemistry reaction mechanisms and to promote the continuous development of intelligent chemical research. Interpretability ; Deep learning ; Feature (linguistics) ; Mechanism (biology) ; Key (lock) ; Focus (optics) ; Identification (biology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Ryoichi Uchiyama | Yuya Nakajima | Yuta Tanaka | Junji Seino
Source: arXiv (Cornell University) (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Impact of Local Descriptors Derived from Machine Learning Potentials in Graph Neural Networks for Molecular Property Prediction In this study, we present a framework aimed at enhancing molecular property prediction through the integration of local descriptors obtained from large-scale pretrained machine learning potentials into three-dimensional graph neural networks (3D GNNs). As an illustration, we developed an EGNN-PFP model by integrating descriptors derived from the preferred potential (PFP) features, acquired through Matlantis, into an equivariant graph neural network (EGNN), and evaluated its effectiveness. When tested on the QM9 dataset, comprising small organic molecules, the proposed model demonstrated superior accuracy compared to both the original EGNN models and the baseline models without PFP-derived descriptors for 11 out of the 12 molecular properties. Furthermore, when evaluated on the tmQM dataset, which encompasses transition metal complexes, notable enhancements in performance were observed across all five target properties, indicating the significance of the local atomic environment surrounding transition metals. In essence, the proposed methodology is adaptable to any 3D GNN architecture, and further enhancements in prediction accuracy are anticipated when integrated with continually evolving GNN architectures. Artificial neural network ; Property (philosophy) ; Graph ; Pattern recognition (psychology) ; Deep neural networks ; Molecular graph ; Baseline (sea) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Yujing Wei | John L. Weber | James M. Stevenson | Zachary K. Goldsmith | Xiaowei Xie | Leif D. Jacobson | Richard A. Friesner
Source: ChemRxiv (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

An Accurate Charge-Aware Machine-Learning Interatomic Potential for the Reduction of Li-ion Battery Electrolytes in Solution Machine learning interatomic potentials (MLIPs), also known as machine learning force fields (MLFFs), offer scalable means of simulating complex systems and processes at \textit{ab initio} level accuracy. One such process is the critical yet still poorly understood formation of the solid electrolyte interphase (SEI) at the anode of a Li-ion battery (LIB) during the first charge cycle, where electrochemical reduction of the electrolyte leads to the generation of decomposition products. MLIPs are uniquely poised to atomistically describe these electrochemical processes, as they are not as affected by the same limitations in bonding and electron transfer as classical force fields. Nonetheless, training MLIPs to run accurate dynamics of a condensed phase with two different oxidation states, such as in electrochemistry, is challenging for many architectures. In this work, we show that by using MPNICE, a message passing MLIP architecture with iterative charge equilibration, we are able to accurately (within 1 kcal/mol) train models along two potential energy surfaces (reduced and unreduced) for LIB-relevant electrolyte systems. Importantly, we demonstrate strategies for sampling and training to examples of anion radicals of these species, which often are not centered on any atom (off-center radicals, or OCRs). We additionally discuss well known limitations of global charge equilibration (Qeq) algorithms in erroneously de-localizing charge, and test methods to alleviate the impact on resulting dynamics. Simulations using these models reveal new insights into electrolyte reduction and considerations for the realistic simulation of electron transfer processes in the condensed phase. Electrolyte ; Battery (electricity) ; Reduction (mathematics) ; Electrochemistry ; Charge (physics) ; Anode ; Molecular dynamics ; Phase (matter) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Electrocatalysts for Energy Conversion
Authors: Zihan Yan | Shengjie Tang | Yizhou Zhu
Source: Open MIND (preprint, 2026-03-08)
Field: Materials Science — Materials Chemistry

A Perspective on Training Machine Learning Force Fields for Solid-State Electrolyte Materials Machine learning force fields enable high-accuracy modeling of solid-state electrolytes (SSEs). This perspective evaluates dataset size, reference quality, and model architectures. We show that rigid SSE frameworks favor efficient learning, prioritizing data quality over quantity. Crucially, force RMSE does not reliably predict transport performance. By analyzing locality and benchmarking frameworks, we provide practical guidelines to accelerate the development of next-generation solid-state batteries. Benchmarking ; Perspective (graphical) ; Quality (philosophy) ; Training (meteorology) ; Locality ; Field (mathematics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Valentin Gradisteanu | Elliot W. Chan | Lester O. Hedges | Meritxell Malagarriga | Rolf David | Miguel de la Puente | Damien Laage | Iñaki Tuñón | Marc W. van der Kamp | Kirill Zinovjev
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Simulating enzyme catalysis with electrostatically embedded machine learning potentials To simulate enzyme reactions, multiscale quantum mechanics/molecular mechanics (QM/MM) approaches are well established and popular. However, accurately and efficiently estimating enzyme activity is a challenge, because in general, precise methods are too computationally expensive. Here, we demonstrate that enzyme catalysis can be captured by coupling efficient, reactive machine-learned potentials (MLPs) trained on gas phase data to the wider enzyme environment using electrostatic machine learning embedding (EMLE). The EMLE scheme is first applied to the natural Diels-Alderase AbyU, showing that it correctly differentiates the catalytic action on different enzyme-substrate conformations. Then, we show that training a reaction-specific EMLE model allows us to accurately capture the enzyme catalytic effects of the conversion of chorismate to prephenate, a reaction with a highly polarizable and charged transition state. In both cases, in contrast to mechanical embedding approaches, the EMLE scheme allows accurate and efficient predictions of enzyme catalysis, agreeing with high-level QM/MM reference calculations. This approach facilitates the use of gas phase-trained MLPs in MLP/molecular mechanics (ML/MM) simulations and should thus be highly beneficial for computational activity screening of enzyme biocatalysts. Embedding ; Catalysis ; Coupling (piping) ; Enzyme catalysis ; Electrostatics ; Electrostatic interaction Machine Learning in Materials Science ; Protein Structure and Dynamics ; CO2 Reduction Techniques and Catalysts
Authors: William H. Green | Jackson Burns | Akshat Shirish Zalte | Charlles R. A. Abreu | Jochen Sieg | Christian Feldmann | Miriam Mathea
Source: Research Square (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

Deep Learning Foundation Models from Classical Molecular Descriptors Deep learning ; Foundation (evidence) ; Benchmark (surveying) ; Random forest ; Bridge (graph theory) ; Deep neural networks Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Felix Döppel | Tim Kircher | Martin Votsmeier
Source: Chemical Engineering Journal (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Formation-consumption neural networks for efficient chemical kinetics modeling Artificial neural network ; Chemical kinetics ; Constraint (computer-aided design) ; Scaling ; Process (computing) ; Representation (politics) ; Chemical reaction engineering ; Simple (philosophy) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Model Reduction and Neural Networks
Authors: Sagar Ghorai | Ruben Staub | Yu Harabuchi | Takashi Nakano | Alexandre Varnek | Satoshi Maeda
Source: Journal of Chemical Theory and Computation (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Toward Reaction Vessel Mimicry: Machine Learning-Assisted Automated Exploration of Alkene Polymerization and Its Transferability Automated reaction path network exploration and product identification through kinetic analysis are essential for mimicking real reaction vessels. A common practice involves using inexpensive semiempirical methods for initial exploration, followed by energy refinement using more accurate density functional theory (DFT) methods. However, semiempirical methods often yield less accurate reaction kinetics, making them unsuitable for efficient exploration and reliable product prediction. Here, we demonstrate the advantages of iterative training of a delta-learning neural network potential (ΔNNP) for automated reaction path exploration. Using ethylene polymerization catalyzed by the [ZrCp<sub>2</sub>CH<sub>3</sub>]<sup>+</sup> catalyst as a model system, we achieve DFT-level accuracy by learning the energy difference between DFT and semiempirical methods. Training the ΔNNP on reaction path networks involving one and two ethylene molecules with the catalyst successfully captures all key elementary steps─initiation, propagation, and termination─which can then be extended to study the polymerization of up to six ethylene molecules. Furthermore, a minimally trained ethylene polymerization model provides a robust foundation for propylene polymerization. We also explore the influence of a cocatalyst on the polymerization elementary step network through additional iterative training. Beyond polymerization, this framework can incorporate other ZrCp<sub>2</sub>-mediated chemistry, such as metallacycle formation, with minimal additional training─yielding several new metallacycle structures. Overall, this iterative training framework is particularly effective for reactions involving repeated analogous elementary steps, such as polymer growth. The approach enables the model to handle increasingly complex reactions, representing an important step toward realistic mimicking of reaction vessels. Polymerization ; Alkene ; Path (computing) ; Ethylene ; Catalysis ; Polymer ; Radical polymerization ; Reaction rate Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Advanced Polymer Synthesis and Characterization
Authors: Rama Baggu | Amr M. Abdelkader | Mohammad Alyoubi | Carland Lopez
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

DISCHARGE PATTERN MINING AND CATALYST PREDICTION IN Li-CO2 BATTERIES USING MACHINE LEARNING Support vector machine ; Artificial neural network ; Cathode ; Extreme learning machine ; Energy (signal processing) ; Regression ; Linear discriminant analysis ; Voltage Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research