jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Benjamin Rhoads | Abigail Hogue | Lars Kotthoff | Samrat Choudhury
Source: Materials (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Structure–Property Linkage in Alloys Using Graph Neural Network and Explainable Artificial Intelligence Deep learning tools have recently shown significant potential for accelerating the prediction of microstructure-property linkage in materials. While deep neural networks like convolution neural networks (CNNs) can extract physics information from 3D microstructure images, they often require a large network architecture and substantial training time. In this research, we trained a graph neural network (GNN) using phase field generated microstructures of Ni-Al alloys to predict the evolution of mechanical properties. We found that a single GNN is capable of accurately predicting the strengthening of Ni-Al alloys with microstructures of varying sizes and dimensions, which cannot otherwise be done with a CNN. Additionally, GNN requires significantly less GPU utilization than CNN and offers more interpretable explanation of predictions using saliency analysis as features are manually defined in the graph. We also utilize explainable artificial intelligence tool Bayesian Inference to determine the coefficients in the power law equation that governs coarsening of precipitates. Overall, our work demonstrates the ability of the GNN to accurately and efficiently extract relevant information from material microstructures without having restrictions on microstructure size or dimension and offers an interpretable explanation. Linkage (software) ; Artificial neural network ; Artificial intelligence ; Property (philosophy) ; Graph ; Computer science ; Theoretical computer science ; Biology ; Genetics ; Philosophy ; Gene Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Gang Lu | Xuhao Jiang | Qingyuan Yang | Zhengyang Zhao | Weiwei Gu | Ruiqi Li
Source:  (article, 2025-04-25)
Field: Chemistry — Inorganic Chemistry

Predicting methane adsorption ability of MOFs based on the crystal structural information by convolutional neural network High-throughput computational screening (HTCS) methods have been widely used in addressing the challenges of screening metal-organic framework (MOF) materials with high performance from large datasets. Accurately predicting the performance of MOFs with hypothetical structures, such as adsorption capacity for methane, can be a great help to HTCS. Traditional molecular dynamics simulation methods are computationally cumbersome and costly. Feature engineering-based machine learning methods have a high reliance on expert experience to extract effective features, some of which can be time-consuming to calculate. Although some classical machine learning models can already achieve good performance, the heavy burden for features computation prior to model training would eventually limit their practical applications, flexibility and generality. In this paper, an approach is proposed for predicting the methane adsorption ability of MOFs directly from their crystal structural information recorded in Crystallographic Information File (CIF)-formatted files, without the need for molecular dynamics simulation or calculating any physical or chemical properties. By expressing the structural information in the crystal cells of MOFs as matrices, a deep convolutional neural network model based on residual structure is applied to train an end-to-end model to predict their methane adsorption ability. The experimental results show that the model accomplishes the prediction task with high accuracy, giving the mean square correlation coefficient (R<sup>2</sup>) as 0.925 and the mean absolute error (MAE) as 22.28 cm<sup>3</sup>g<sup>-1</sup>, which demonstrates a good performance over baselines. Convolutional neural network ; Adsorption ; Methane ; Computer science ; Crystal (programming language) ; Artificial intelligence ; Artificial neural network ; Crystal structure ; Materials science ; Pattern recognition (psychology) ; Chemistry ; Crystallography ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications
Authors: Supratim Ghosh | Nupur Jain | Raghavan B. Sunoj
Source: ArXiv.org (preprint, 2025-02-27)
Field: Materials Science — Materials Chemistry

Efficient Machine Learning Approach for Yield Prediction in Chemical Reactions Developing machine learning (ML) models for yield prediction of chemical reactions has emerged as an important use case scenario in very recent years. In this space, reaction datasets present a range of challenges mostly stemming from imbalance and sparsity. Herein, we consider chemical language representations for reactions to tap into the potential of natural language processing models such as the ULMFiT (Universal Language Model Fine Tuning) for yield prediction, which is customized to work across such distribution settings. We contribute a new reaction dataset with more than 860 manually curated reactions collected from literature spanning over a decade, belonging to a family of catalytic meta-C(sp2)-H bond activation reactions of high contemporary importance. Taking cognizance of the dataset size, skewness toward the higher yields, and the sparse distribution characteristics, we developed a new (i) time- and resource-efficient pre-training strategy for downstream transfer learning, and (ii) the CFR (classification followed by regression) model that offers state-of-the-art yield predictions, surpassing conventional direct regression (DR) approaches. Instead of the prevailing pre-training practice of using a large number of unlabeled molecules (1.4 million) from the ChEMBL dataset, we first created a pre-training dataset SSP1 (0.11 million), by using a substructure-based mining from the PubChem database, which is found to be equally effective and more time-efficient in offering enhanced performance. The CFR model with the ULMFiT-SSP1 regressor achieved an impressive RMSE of 8.40 for the CFR-major and 6.48 for the CFR-minor class in yield prediction on the title reaction, with a class boundary of yield at 53 %. Furthermore, the CFR model is highly generalizable as evidenced by the significant improvement over the previous benchmark reaction datasets. chEMBL ; Yield (engineering) ; PubChem ; Class (philosophy) ; Support vector machine ; Cheminformatics ; Distribution (mathematics) ; Probability distribution ; Regression Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Ziyue Li | Guangzhen Pan | Yongfeng Li | TianQi Wang | Yagang Zhang | Xu‐Wen Wang
Source: Materials Today Communications (article, 2025-09-17)
Field: Chemistry — Inorganic Chemistry

Application of attention-BiLSTM within a deep learning framework for forecasting synthesis conditions of MOF materials Deep learning ; Feature (linguistics) ; Nonlinear system ; Mechanism (biology) ; Layer (electronics) ; Complex system Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Elham Rahmanian | Ali Sajedi‐Moghaddam | Mohammad Taha Hoveizavi | Seyed Hamed Aboutalebi
Source: Advanced Powder Materials (article, 2025-10-22)
Field: Chemistry — Electrochemistry

Electrolyte hydration energy as a universal descriptor for ion-specific capacitance: insights from interpretable machine learning The rational design of high-performance electrochemical energy storage devices critically depends on a fundamental understanding of ion-electrode interactions at the molecular scale. Herein, we employ interpretable machine learning (ML) to reveal electrolyte hydration energy as a universal descriptor governing ion-specific capacitance in two-dimensional (2D) materials. Through explainable ML, we elucidate how ion hydration shell stability and size critically influence charge transport and storage at the electrode-electrolyte interface. Our analysis identifies hydration energy – not ionic size – as the primary factor dictating capacitance, challenging prevailing assumptions and providing quantifiable design rules for electrolyte selection. These insights offer a data-driven pathway to optimize 2D materials for supercapacitors and beyond, including batteries and electrocatalytic systems. This work demonstrates the power of explainable artificial intelligence in uncovering molecular-level mechanisms that accelerate the discovery and development of next-generation energy storage technologies. Supercapacitor ; Energy storage ; Electrolyte ; Electrochemical energy storage ; Work (physics) ; Stability (learning theory) ; Energy (signal processing) Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies ; Fault Detection and Control Systems
Authors: Zhe Xue | Rui Tan | Ming Ji | Jinzhong Tian | Hua Hou | Xinyu Zhang | Lianfeng Zou | Yuhong Zhao
Source: Chemical Engineering Journal (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Machine learning-assisted the directed evolution of descriptor for Mg-CO2 batteries Robustness (evolution) ; Transferability ; Support vector machine ; Boosting (machine learning) ; Feature (linguistics) ; Fingerprint (computing) ; Key (lock) Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Advanced battery technologies research
Authors: Zhenyao Fang | Ting-Wei Hsu | Qimin Yan
Source: ACS Nano (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti<sub>3</sub>C<sub>2</sub>T<sub>2-<i>x</i></sub> as a representative system, we find that electrical conductivity exhibits an emergent peak near the order-disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids. Monte Carlo method ; Artificial neural network ; Multiscale modeling ; Phase transition ; Range (aeronautics) ; Ensemble learning ; Optical conductivity ; Spin (aerodynamics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Ming Han | Ge Sun | Juan Pablo
Source: ArXiv.org (preprint, 2025-02-02)
Field: Materials Science — Materials Chemistry

Attention-Based Functional-Group Coarse-Graining: A Deep Learning Framework for Molecular Prediction and Design Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML training. In this study, we report a data-efficient, deep-learning framework for molecular discovery that integrates a coarse-grained functional-group representation with a self-attention mechanism to capture intricate chemical interactions. Our approach exploits group-contribution theory to create a graph-based intermediate representation of molecules, serving as a low-dimensional embedding that substantially reduces the data demands typically required for training. By leveraging the self-attention mechanism to learn subtle chemical context, our method consistently outperforms conventional methods in predicting multiple thermophysical properties. In a case study focused on adhesive polymer monomers, we train on a limited dataset comprising just 6,000 unlabeled and 600 labeled monomers. The resulting chemistry prediction model achieves over 92% accuracy in forecasting properties directly from SMILES strings, exceeding the performance of current state-of-the-art techniques. Furthermore, the latent molecular embedding is invertible, allowing the design pipeline to incorporate a decoder that can automatically generate new monomers from the learned chemical subspace. We illustrate this functionality by targeting high and low glass transition temperatures ($T_g$), successfully identifying novel candidates whose $T_g$ extends beyond the range observed in the training data. The ease with which our coarse-grained, attention-based framework navigates both chemical diversity and data scarcity offers a compelling route to accelerate and broaden the search for functional materials. Granularity ; Group (periodic table) ; Artificial intelligence ; Computer science ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Lei Liu | Jingfu Shi | Di Song | Changqing Miao
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning molecular dynamics simulations of shock response and spallation behavior in PPTA crystals The shock response of poly(<i>p</i>-phenylene terephthalamide) (PPTA) crystals is investigated using molecular dynamics simulations combined with a machine learning potential. Considering the anisotropy of PPTA crystals, the directions dominated by hydrogen bonding and van der Waals forces are examined, respectively. First, a machine learning potential capable of simulating the shock behavior of PPTA is developed and validated. The potential is demonstrated to achieve excellent accuracy, showing high consistency with density functional theory results. Based on the established machine learning potential, multiscale shock techniques are employed to simulate shock compression at various particle velocities. The Hugoniot curves of PPTA crystals reveal three distinct stages of shock response: elastic, plastic, and cross-linking. With increasing particle velocity, the <i>b</i> axis of PPTA crystals is found to exhibit a greater tendency for plastic deformation. Plasticity along the <i>a</i> axis is characterized by the planarization of adjacent benzene rings within the chains, while along the <i>b</i> axis, it involves the breaking and reformation of hydrogen bonds. The spatiotemporal evolution of thermodynamic parameters and spallation during shock wave propagation is further uncovered through non-equilibrium molecular dynamics simulations. The shock response mechanisms of PPTA fibers are elucidated, providing a foundation for subsequent simulations and their application in impact protection structures. Spallation ; Molecular dynamics ; Dynamics (music) ; Shock (circulatory) ; Materials science ; Chemical physics ; Physics ; Chemistry ; Computational chemistry ; Nuclear physics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Advanced Physical and Chemical Molecular Interactions
Authors: Yusuke Hashimoto | Xue Jia | Hao Li | Takaaki Tomai
Source: APL Machine Learning (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

A materials map integrating experimental and computational data via graph-based machine learning for enhanced materials discovery Materials informatics (MI), emerging from the fusion of materials science and data science, has the potential to greatly accelerate material development and discovery. Although MI relies on data from both computational and experimental studies, their integration remains challenging. In our previous study, we addressed this challenge by training a machine learning model on experimental data and applying it to compositional entries in a computational database, thereby creating a unified dataset. In this study, we use these integrated datasets to construct material maps that visualize the relationships between material properties and structural features. The goal is to provide experimental researchers with a practical tool for exploring structurally similar compounds and thus their associated routes. We generate the materials map using the MatDeepLearn (MDL) framework, which represents crystal structures as graphs and employs deep learning for property prediction. Statistical analyses reveal that the MDL equipped with a message passing neural network (MPNN) architecture efficiently captures features related to the structural complexity of materials. Interestingly, this representational advantage does not always lead to higher accuracy in property prediction. We attribute this finding to the strong learning capacity of MPNN, which contributes primarily to the organization of data points within the materials map rather than to incremental gains in predictive precision. Computer science ; Graph ; Machine learning ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Hendrik Kraß | Ju Huang | Seyed Mohamad Moosavi
Source: ArXiv.org (preprint, 2025-07-16)
Field: Materials Science — Materials Chemistry

MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for accelerating atomistic simulations, offering scalable and efficient modeling with accuracy close to quantum calculations. However, their reliability and effectiveness in practical, real-world applications remain an open question. Metal-organic frameworks (MOFs) and related nanoporous materials are highly porous crystals with critical relevance in carbon capture, energy storage, and catalysis applications. Modeling nanoporous materials presents distinct challenges for uMLIPs due to their diverse chemistry, structural complexity, including porosity and coordination bonds, and the absence from existing training datasets. Here, we introduce MOFSimBench, a benchmark to evaluate uMLIPs on key materials modeling tasks for nanoporous materials, including structural optimization, molecular dynamics (MD) stability, the prediction of bulk properties, such as bulk modulus and heat capacity, and guest-host interactions. Evaluating over 20 models from various architectures on a chemically and structurally diverse materials set, we find that top-performing uMLIPs consistently outperform classical force fields and fine-tuned machine learning potentials across all tasks, demonstrating their readiness for deployment in nanoporous materials modeling. Our analysis highlights that data quality, particularly the diversity of training sets and inclusion of out-of-equilibrium conformations, plays a more critical role than model architecture in determining performance across all evaluated uMLIPs. We release our modular and extendable benchmarking framework at https://github.com/AI4ChemS/mofsim-bench, providing an open resource to guide the adoption for nanoporous materials modeling and further development of uMLIPs. Nanoporous ; Benchmarking ; Benchmark (surveying) ; Scalability ; Modular design ; Multiscale modeling ; Molecular dynamics ; Key (lock) Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Musanna Galib | Mewael Isiet | Mauricio Ponga
Source: ArXiv.org (preprint, 2025-01-23)
Field: Materials Science — Materials Chemistry

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science As the atomistic simulations of materials science move from traditional potentials to machine learning interatomic potential (MLIP), the field is entering the second phase focused on discovering and explaining new material phenomena. While MLIP development relies on curated data and flexible datasets from ab-initio simulations, transitioning seamlessly between ab-initio workflows and MLIP frameworks remains challenging. A global survey was conducted to understand the current standing (progress and bottleneck) of the machine learning-guided materials science research. The survey responses have been implemented to design an open-source software to reduce the access barriers of MLIP models for the global scientific community. Here, we present AtomProNet, an open-source Python package that automates obtaining atomic structures, prepares and submits ab-initio jobs, and efficiently collects batch-processed data for streamlined neural network (NN) training. Finally, we compared empirical and start-of-the-art machine learning potential, showing the practicality of using MLIPs based on computational time and resources. Flow (mathematics) ; Computer science ; Physics ; Mechanics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Mohammed Mahshook | Rudra Banerjee
Source: ArXiv.org (preprint, 2025-06-04)
Field: Materials Science — Materials Chemistry

Beyond Diamond: Interpretable Machine Learning Discovery of Coherent Quantum Defect Hosts in Semiconductors Quantum point defects in wide bandgap semiconductors-such as the nitrogen-vacancy (NV) center in diamond are leading candidates for solid-state spin qubits due to their long coherence times and optically addressable spin states. Yet, identifying host materials capable of supporting such deep-level defects remains a significant challenge, owing to the complex interplay between chemical composition, crystal structure, and electronic environment. Here, we present a scalable, interpretable machine learning framework that combines density functional theory (DFT)-informed descriptors with a structure-agnostic ensemble model to predict quantum-compatible defect-host materials. Trained on a curated dataset from the Materials Project and ICSD, our model achieves a high Matthews correlation coefficient (MCC &gt; 0.95) and assigns confidence scores to guide prioritization of candidates. First-principles calculations of coherence-relevant properties, including static dielectric constants and defect formation energetics, validate key predictions. While high dielectric response is a necessary but not sufficient condition for spin coherence, our model successfully recovers known hosts such as diamond and SiC, and reveals previously overlooked candidates such as WS2 , MgO, CaS and TiO2 . This approach establishes a robust path for discovering next-generation quantum materials, bridging data-driven screening with physically interpretable design principles. Bridging (networking) ; Coherence (philosophical gambling strategy) ; Qubit ; Spin (aerodynamics) ; Quantum ; Diamond ; Dielectric ; Semiconductor Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Integrated Circuits and Semiconductor Failure Analysis