jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Deshpande, Aaryesh
Source: ArXiv.org (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm The convergence of statistical learning and molecular physics is transforming our approach to modeling biomolecular systems. Physics-informed machine learning (PIML) offers a systematic framework that integrates data-driven inference with physical constraints, resulting in models that are accurate, mechanistic, generalizable, and able to extrapolate beyond observed domains. This review surveys recent advances in physics-informed neural networks and operator learning, differentiable molecular simulation, and hybrid physics-ML potentials, with emphasis on long-timescale kinetics, rare events, and free-energy estimation. We frame these approaches as solutions to the "biomolecular closure problem", recovering unresolved interactions beyond classical force fields while preserving thermodynamic consistency and mechanistic interpretability. We examine theoretical foundations, tools and frameworks, computational trade-offs, and unresolved issues, including model expressiveness and stability. We outline prospective research avenues at the intersection of machine learning, statistical physics, and computational chemistry, contending that future advancements will depend on mechanistic inductive biases, and integrated differentiable physical learning frameworks for biomolecular simulation and discovery. Differentiable function ; Artificial neural network ; Intersection (aeronautics) ; Inference ; Consistency (knowledge bases) ; Frame (networking) ; Convergence (economics) ; Computational learning theory ; Deep learning Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Mahboube Shirani | Qamar Salamat | Mahtab Rezaei Payam | Mohammadjavad Jahanshahi | Mohammad Saleh Barghi Jahromi | Mahboobe Amirani Poor | Mustafa Soylak
Source: OICC Press Journals (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Green synthesis of Ag2O/B@OP-CDs nanocomposite for pipette tip solid-phase extraction (PT-SPE) of mercury from food samples: optimization by artificial neural networks (ANN) and evolutionary polynomial regression (EPR) Mercury contamination in food remains a critical public health concern due to its extreme toxicity and bioaccumulation in the environment. Developing rapid, sensitive, and environmentally sustainable methods for its detection is of great importance. In this study, a biodegradable nanocomposite comprising silver oxide nanoparticles and boron-doped carbon quantum dots derived from orange peel waste was introduced. This material served as a high- performance sorbent for pipette-tip solid-phase extraction of inorganic mercury from food samples. Synthesized via a green hydrothermal process using agricultural waste, the resulting ternary nanohybrid leverages the synergistic affinity of Ag2O and OP-CDs toward Hg (II), yielding exceptional extraction efficiency. Notable advantages included rapid extraction kinetics, minimal consumption of sorbent and solvent, and the elimination of toxic reagents, thereby fully aligning with the principles of green analytical chemistry. Extraction parameters were optimized using chemometric and statistical approaches, specifically artificial neural networks and evolutionary polynomial regression, to ensure robust performance. Under optimized conditions, the method represented trace-level detection capability and excellent precision in diverse food matrices. Comprehensive green metric evaluation confirms the sustainability of the approach. This sustainable, AI-enhanced extraction strategy provides an efficient and practical solution for trace mercury analysis in food, with broad potential in food safety monitoring. Mercury (programming language) ; Sorbent ; Nanocomposite ; Extraction (chemistry) ; Bioaccumulation ; Nanoparticle ; Response surface methodology ; Environmental remediation Analytical chemistry methods development ; Mercury impact and mitigation studies ; Carbon and Quantum Dots Applications
Authors: Wanying Zhang | Xiaoyuan Wang | Hao Yu | Yibo Ai | Weidong Zhang
Source: Computational Materials Science (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Cross-scale prediction of oxidation behavior of uranium alloys based on deep learning Uranium alloys are irreplaceable structural and functional materials in strategic equipment. However, the strong correlation characteristics of 5f electrons unique to actinide elements result in high chemical reactivity and environmental sensitivity, making the investigation of their corrosion behavior critically important. This study focuses on the oxidation kinetics of the U-2.5Nb alloy and proposes a deep learning-based cross-scale prediction method for the oxidation performance of uranium alloys. Microscopic features of the uranium alloy were extracted through molecular dynamics (MD) simulations and combined with experimental oxidation weight gain data to construct a predictive model bridging microscopic structures and macroscopic oxidation behavior. In the MD simulations, a supercell model of U-2.5Nb was first built, and oxygen molecules were introduced on its surface. Molecular dynamics simulations were then performed to obtain key microscopic parameters. To enable cross-scale modeling, a random forest algorithm was employed to evaluate the importance of multiple MD-derived features. Multi-model comparative experiments were designed and implemented to train these features, and finally, an Long Short-Term Memory (LSTM) neural network was utilized to predict the oxidation behavior curves from limited microscopic data. The model's performance was comprehensively evaluated using metrics such as root mean square error, coefficient of determination, and mean absolute error. • A cross-scale framework correlates atomistic MD features with U–2.5Nb alloy macroscopic oxidation behavior. • MD-derived features (diffusion, potential energy, VACF) are filtered via random forest for deep learning. • LSTM accurately predicts oxidation kinetics from limited microdata, enabling robust actinide alloy oxidation modeling. Uranium ; Deep learning ; Iron alloys ; Deep drawing Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Machine Learning in Materials Science
Authors: Shuo Yang | Yunhao Yang | Chen Huang | Ran Bai | Leitao Cao | Jing Ren | Shengjie Ling
Source: Nano Letters (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Physics-Informed Graph Neural Networks for Predicting Deformation in Disordered Fibrous Materials Disordered fibrous networks play vital mechanical roles but are difficult to model due to their sparse connectivity and complex nonaffine deformation. We introduce a physics-informed, graph-learning-based Network Mechanics Prediction (GNMP) that predicts deformation and stress-strain responses of 2D semiflexible networks with reliable accuracy and >10× efficiency gains over molecular dynamics. GNMP integrates graph-attention message passing, multiscale physical embeddings, and a bond-length-guided scheduler to capture key nonaffine rearrangements. Experiments on Poisson-ratio-aligned 3D-printed networks show consistent deformation motifs, descriptor trends, and comparable log-area changes (∼0.46 vs ∼0.24) while reducing the geometry-inference time to ∼0.13 s per step. GNMP offers a generalizable route for rapid, topology-aware mechanical prediction in fibrous network materials and supports the accelerated design of biomimetic soft tissues, flexible conductors, and related network-based systems. Deformation (meteorology) ; Artificial neural network ; Multiscale modeling ; Key (lock) ; Graph ; Network model ; Mechanical system ; Network structure Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Sensor and Energy Harvesting Materials
Authors: Di-Fan Liu (17600673) | Wen-Zhuo Dong (17600676) | Xin-Jie Wang (21405903) | Shao-Long Zhong (14313117) | Zhi-Min Dang (1713628)
Source: Figshare (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

Generating High-TemperaturePolymer Dielectrics witha Graph Convolution and Reinforcement Learning Framework Generating molecular structures with certain desired properties is a fundamental problem for material science research. Deep learning models have demonstrated big potential to identify novel materials by exploring large chemical spaces efficiently. Here, we demonstrate the generation of hypothetical polymers with a high glass-transition temperature (<i>T</i><sub>g</sub>) and a wide band gap (<i>E</i><sub>g</sub>) using a general graph convolutional network-based model through reinforcement learning. Two forward property prediction models on <i>T</i><sub>g</sub> and <i>E</i><sub>g</sub> are employed to provide domain-specific rewards. We further incorporate prior knowledge in the form of adversarial rewards given by a generative adversarial network framework via a data set of exemplar polymer structures. The domain-specific rewards and adversarial rewards are optimized in a reinforcement learning framework through policy gradient methods. Rules of chemistry are incorporated in the molecule generation environment as hard constraints. The generation process is guided to generate promising high-temperature polymer dielectrics for energy storage applications by property targeting and constrained property optimization. This work demonstrates potential for exploring large chemical spaces for the discovery of polymers with desirable properties. Reinforcement learning ; Property (philosophy) ; Adversarial system ; Process (computing) ; Graph ; Set (abstract data type) ; Generative grammar Machine Learning in Materials Science ; Dielectric materials and actuators ; Advanced Graph Neural Networks
Authors: Marwa Farag
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

( <i>Invited</i> ) Accelerated Quantum Supercomputer for Battery Materials Discovery As the automotive industry shifts from fossil fuels towards electric vehicles (EV), there is a need for advanced rechargeable lithium-ion batteries (LIB). Developing novel electrodes and electrolytes for LIBs with increased energy and power density will boost the driving distance and charging rate of EVs. Additionally, these innovations should also improve LIBs life spans and safety. Chemistry simulations and machine learning can be employed to guide the development of novel battery materials with superior performance. Current state-of-the-art employs DFT to solve the electronic structure problem and predict battery materials properties. However, DFT has fundamental limitations. That makes its application in battery materials challenging. Accurate methods such as full configuration interaction scale exponentially on classical computers, which makes its application for materials impossible. Accurate tools striking the balance between accuracy and efficiency are needed. AI supercomputer and quantum computing promises to bridge this gap. In this talk, I will introduce the accelerated quantum supercomputing and how AI supercomputer together with quantum computing will accelerate the battery materials discovery. I will demonstrate a hybrid application for battery materials simulation as well as molecule generation. We employ NVIDIA’s hybrid quantum classical framework, CUDA-Q, to simulate battery materials at scale, aiming to deepen our understanding of their structure-property relationships and improve their design. We performed the largest numerical study to date, seamlessly integrating quantum-classical computing, which can be easily scaled to larger system sizes as quantum hardware matures Supercomputer ; Battery (electricity) ; Quantum computer ; Quantum ; Bridge (graph theory) ; Power (physics) ; Electronics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Physical and Chemical Molecular Interactions
Authors: Norbert Kovács | I.B.C.M. Rocha | F.D. van der Meer | C. Furtado | P. Camanho
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification in Multiscale Modeling of Composite Materials Using Physically Recurrent Neural Networks Computer science ; Artificial neural network ; Composite number ; Uncertainty quantification ; Multiscale modeling ; Artificial intelligence ; Machine learning ; Algorithm ; Chemistry Material Properties and Failure Mechanisms ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Linhan Yu (20765506) | Shijie Jiang (4115776) | Chenlong Hu (12601312) | Yumin Wang (226808) | Liuyang Xu (8871038) | Hu He (2144476) | Yu Wang (12152) | Xuesen Du (1681672)
Source: Figshare (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Machine Learningfor Hydrogen Storage Properties ofTiFe Alloys Based on Literature-Curated Data Set: Prediction and Limitation This study establishes an interpretable machine learning framework to elucidate the composition–property relationships of TiFe-based hydrogen storage alloys. Beyond elemental compositions, two physical descriptors (Size 1/Size 2) were first introduced. XGBoost performed superiorly in predicting hydrogen absorption and desorption plateau pressures. SHAP analysis revealed that Fe content and temperature are the principal variables affecting platform pressure, a result that is consistent with empirical observations. However, hydrogen storage capacity prediction showed limited accuracy In addition to the features utilized in the present study, there is a lack of supplementary descriptive features that can robustly characterize hydrogen storage capacity. Potentially relevant attributes may encompass phase composition, synthesis methodology, and chemical information on the material. The lack of incorporation of such data constrains the predictive performance and utility of machine learning data sets of TiFe alloys. This work provides actionable insights for alloy design while exposing inherent limitations of the Literature-curated Data set by coupling machine learning with experimental data. The convergence of interpretable AI and materials science demonstrates transformative potential to overcome longstanding challenges in hydrogen storage material development, particularly through hybrid data-physics modeling strategies. Hydrogen storage ; Principal component analysis ; Work (physics) ; Hydrogen ; Data set ; Transformative learning ; Experimental data Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Yue Qi | Md Jamil Hossain | Tilas Kabengele | Qisheng Wu
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Incorporating the Effect of Electric Double Layer (EDL) and Solid Electrolyte Interphase (SEI) Formation in Lithium-Ion Batteries with Machine Learning Accelerated Workflow Electrolytes, comprising salts, solvents, diluents, and various additives, must form a stable solid electrolyte interphase (SEI) to ensure the performance and durability of lithium (ion) batteries. However, the structure of the electric double layer (EDL) near charged surfaces remains unresolved, despite its critical role in determining which species can be reduced to form the SEI near the negative electrode. A recent hierarchical modeling framework [1] was developed to address two fundamental questions: (1) Which species accumulate at the negatively charged electrode surface in a complex multicomponent electrolyte? (2) At what voltage do these local EDL species undergo reduction, contributing to SEI formation? The first question is answered using classical molecular dynamics (MD) to simulate EDL structures. Statistically representative local solvation clusters are then used as input for density functional theory (DFT) calculations to address the second question. As the number of electrolyte components increases, the combinatorial growth in possible clusters within the EDL requiring DFT calculations creates a computational bottleneck. To overcome this, a gradient boosting regression model, initialized with a linear regression model, is implemented to accelerate the reduction voltage predictions with balanced interpolation and extrapolation performance. This DFT–MD–data hierarchical model has been successfully applied to various electrolytes, including 1 M carbonate-based systems, [1] low- and high-concentration ether electrolytes, localized high-concentration electrolytes (LHCEs), [2.3] and seven fluorinated formulations, demonstrating its utility in guiding SEI design through EDL understanding. [1] J. Am. Chem. Soc. 145, 4, 2473–2484 (2023) [2] Nature Materials 22, 1531–1539 (2023) [3] Energy Environ. Sci., 18, 3036-3046 (2025) Electrolyte ; Extrapolation ; Density functional theory ; Voltage ; Solvation ; Electrode ; Interphase ; Linear regression ; Double layer (biology) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Shuan Chen | Gunwook Nam | Alán Aspuru‐Guzik | Yousung Jung
Source: Chemical Science (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

SynTwins: a retrosynthesis-guided framework for synthesizable molecular analog generation The disconnect between AI-generated molecules with desirable properties and their synthetic feasibility remains a critical bottleneck in computational discovery of drugs and materials. While generative AI has accelerated the proposal of candidate molecules, many of these structures prove challenging or impossible to synthesize using established chemical reactions. Here, we introduce SynTwins, a novel retrosynthesis-guided molecule design framework that finds synthetically accessible molecular analogs by emulating expert chemists' strategies in three steps: retrosynthesis, searching similar building blocks, and virtual synthesis. Using a search algorithm instead of a stochastic data-driven generator, SynTwins outperforms state-of-the-art machine learning models at exploring synthetically accessible analogs while maintaining high structural similarity to original target molecules. Furthermore, when integrated into existing molecular property-optimization frameworks, our hybrid approach produces synthetically feasible analogs with minimal loss in property scores. Our comprehensive benchmarking across diverse molecular datasets demonstrates that SynTwins effectively bridges the gap between computational design and experimental synthesis, providing a practical solution for accelerating the discovery of synthesizable molecules with desired properties for a wide range of applications. Bottleneck ; Virtual screening ; Similarity (geometry) ; Range (aeronautics) ; Benchmarking ; Generative grammar Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Atsuo Yamada | Norio Takenaka | Taiga Iwata
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

( <i>Invited</i> ) Machine Learning Force Field MD Simulation of SEI Formation on Lithium Metal Li metal batteries are attracting attention as next-generation high energy density energy storage devices for the realization of a carbon-neutral society. The practical utilization of Li metal in Li-metal batteries faces challenges due to its high reactivity with the electrolytes. In order to suppress such high reactivity, it is necessary to form a stable protection film known as the solid electrolyte interphase (SEI) at the interface based on rational electrolyte design. However, the SEI formation mechanism on the Li metal remains unclear. In this study, the lithium bis(fluorosufonyl)imide (LiFSI)/ethylene carbonate (EC) electrolyte decomposition process on Li metal surface was investigated with molecular dynamics simulations using a machine-learning force field. A newly developed automatic program successfully obtained the main pathways among the numerous reaction events during the simulations. Notably, the main products in the SEI formation were identified as LiF and Li 2 O, which is consistent with the experimental tendency. Electrolyte ; Interphase ; Lithium (medication) ; Lithium metal ; Metal ; Realization (probability) ; Molecular dynamics ; Decomposition ; Reactivity (psychology) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Physical and Chemical Molecular Interactions
Authors: José Ferraz-Caetano | Filipe Teixeira | M. Natália D. S. Cordeiro | Tomoyuki Miyao
Source: Journal of Catalysis (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

Inverse ligand design: a generative data-driven model for optimizing vanadyl-based epoxidation catalysts We present a machine learning (ML) model for the inverse, de novo generative design of vanadyl-based catalyst ligands for the epoxidation of small alkenes and alcohols. Leveraging molecular descriptors calculated using the RDKit library, our model achieves high performance in validity (64.7 %), uniqueness (89.6 %), and RDKit similarity (91.8 %) after training on a curated dataset of six million structures. Focusing on the modular nature of vanadyl catalyst scaffolds – VOSO 4 , VO(O i Pr) 3 and VO(acac) 2 – the model generates feasible ligands optimized for catalytic performance. The VOSO 4 ligands were consistent with high-yield reactions, while VO(O i Pr) 3 and VO(acac) 2 revealed greater structure variability. Unlike conventional generative approaches, the ligand inverse design also aims to co-design the reaction system, including substrate SMILES and reaction conditions. The model framework was investigated using multiple generative model architectures, resulting in the deep-learning transformer as the most powerful, and uncovering clustering patterns in electronic and structural descriptors related to yield predictions. High synthetic accessibility scores supported the feasibility of the generated ligands. Although the experimental dataset lacks modularity and has few negative data, the model compensates this by using structured descriptor encoding and compatibility scoring. This generative ML framework shows a scalable approach to catalyst ligand design, paving the way for efficient data optimization in synthetic chemistry. Generative model ; Catalysis ; Inverse ; Generative grammar ; Ligand (biochemistry) ; Scalability ; Modular design ; Modularity (biology) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Polyoxometalates: Synthesis and Applications