jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jeffrey S. Lowe | Sean T. Dix | Matthew Jones | Taylor R. Garrick
Source: Journal of Electrochemical Energy Conversion and Storage (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Perspective: Driving Electric Vehicle Battery Design With Atomistic Modeling Abstract Electric vehicles (EVs) continue to increase their share of the automotive market. To spur this growth, original equipment manufacturers (OEMs) and battery cell manufacturers have invested in atomistic modeling approaches based on fundamental science. This effort has been successful in improving vehicle performance through modifications to battery cell chemistry. However, it is our view that atomistic modeling can go a step further to affect vehicle battery design. In this perspective, we demonstrate a multiscale modeling approach to link atomic-scale phenomena with full cell predictions relevant for battery design engineers. Recent multiscale modeling approaches undertaken at General Motors are discussed. We show that variation in the predicted diffusivity of lithium ions in the electrolyte leads to variation in final cell temperatures of 3 °C in small-format cells, and that reversible volume change for common cathode materials can be as large as 10% for the full state of lithiation window. Additionally, machine learning (ML) will be presented as another growing area in the literature to drive cell-level battery design. Linking ML approaches with datasets from atomistic modeling represents a key direction of growth for vehicle design. Battery (electricity) ; Electric vehicle ; Automotive industry ; Work (physics) ; Modeling and simulation ; Key (lock) ; Multiscale modeling ; Lithium-ion battery ; State of charge Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: Guangyao Chen (18459329) | Zhilong Wang (248924) | Fengqi You (1360893)
Source: Figshare (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

PSCG-Net: A MultiscaleCrystal Graph Neural Networkfor Accelerated Materials Discovery The discovery of new materials is crucial for progress in energy, electronics, and sustainable technology. Traditional machine learning approaches, including graph neural networks (GNNs), often fall short because they cannot capture long-range interactions in crystalline materials due to fixed cutoff radii. To overcome this limitation, a pair-scalable crystal graph neural network (PSCG-Net) is proposed. This framework incorporates multiscale structural representations inspired by the pair distribution function and uses graphs with various cutoff distances to account for both short-range and long-range atomic interactions. Tested on over 150,000 crystal structures, PSCG-Net outperforms the baseline Crystal Graph Convolutional Neural Network model by achieving a mean absolute error of 0.065 eV in formation energy prediction. The model’s effectiveness is further supported by consistent results across six diverse data sets and confirmed by first-principles calculations using hybrid functionals in band gap-type predictions. Additionally, PSCG-Net is demonstrated to be practical for screening high-performance materials in photovoltaics, dielectrics, and superconductors. By accurately capturing hierarchical atomic interactions, this approach accelerates the design and discovery of materials and offers a versatile framework applicable to multiscale challenges in various scientific disciplines. This framework not only enhances predictive accuracy but also paves the way for breakthroughs in materials science research and technological innovation. Graph ; Artificial neural network ; Cutoff ; Convolutional neural network ; Crystal (programming language) ; Computation ; Function (biology) ; Crystal structure prediction Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Paul Hagemann | Simon Müller | Janine George | Philipp Benner
Source: ArXiv.org (preprint, 2025-12-10)
Field: Materials Science — Materials Chemistry

Transport Novelty Distance: A Distributional Metric for Evaluating Material Generative Models Recent advances in generative machine learning have opened new possibilities for the discovery and design of novel materials. However, as these models become more sophisticated, the need for rigorous and meaningful evaluation metrics has grown. Existing evaluation approaches often fail to capture both the quality and novelty of generated structures, limiting our ability to assess true generative performance. In this paper, we introduce the Transport Novelty Distance (TNovD) to judge generative models used for materials discovery jointly by the quality and novelty of the generated materials. Based on ideas from Optimal Transport theory, TNovD uses a coupling between the features of the training and generated sets, which is refined into a quality and memorization regime by a threshold. The features are generated from crystal structures using a graph neural network that is trained to distinguish between materials, their augmented counterparts, and differently sized supercells using contrastive learning. We evaluate our proposed metric on typical toy experiments relevant for crystal structure prediction, including memorization, noise injection and lattice deformations. Additionally, we validate the TNovD on the MP20 validation set and the WBM substitution dataset, demonstrating that it is capable of detecting both memorized and low-quality material data. We also benchmark the performance of several popular material generative models. While introduced for materials, our TNovD framework is domain-agnostic and can be adapted for other areas, such as images and molecules. Novelty ; Generative grammar ; Novelty detection ; Metric (unit) ; Generative model ; Benchmark (surveying) ; Quality (philosophy) ; Set (abstract data type) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Advanced Electron Microscopy Techniques and Applications
Authors: Jiehai Peng (21458225) | Shoutao Shen (21458228) | Wei Zhu (23646) | Chao Wu (199897) | Lin Huang (288599) | Mingzi Li (9539444) | Jiamin Zhong (13845997) | Nan Zhou (170721) | Shanjiang Xue (21458231) | Kui Du (3488084) | Zhao Chen (92571)
Source: Figshare (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Ground-State DescriptorEnables Machine Learning-AssistedVirtual Screening of AIE-Active Mechanofluorochromic Molecules withHigh Contrast Aggregation-induced emission mechanofluorochromic (AIE-MFC) molecules with high-contrast are in high demand for pressure-sensing devices and optoelectronic devices. However, developing AIE-MFC molecules with high-contrast beyond 100 nm still highly relies on scientific intuition through experimental and traditional trial-and-error methods. Herein, we establish a new electronic descriptor E<sub>DAHL</sub> (the orbital energy of the donor and acceptor under ground-state) to characterize mechanofluorochromic (MFC) properties (preground and postground fluorescence emission wavelengths Oλ<sub>em,max</sub> and Gλ<sub>em,max</sub>). And an MFC property prediction model with high prediction accuracy and less computational complexity is established by combining E<sub>DAHL</sub> with molecular descriptors as inputs. Subsequently, we constructed a closed-loop approach that integrates machine learning (ML), density functional theory (DFT), high-throughput virtual screening (HTVS), and experiment to accelerate the discovery of AIE-MFC molecules with high contrast in a large space. After processing, 37 new high-contrast AIE-MFC molecules beyond 100 nm are rapidly screened out of more than 10000 candidates. Notably, experiments were used to further confirm the reliability of the workflow, opening an avenue for screening of high-contrast AIE-MFC molecule. We believe the new electronic descriptor may be extended to discover other molecules constructed from donors and acceptors with different properties. Intuition ; Molecule ; Virtual screening ; Contrast (vision) ; Density functional theory ; Fluorescence ; Acceptor Luminescence and Fluorescent Materials ; Crystallography and molecular interactions ; Molecular Sensors and Ion Detection
Authors: Mahdi Kchaou | Francesco Contino | Diederik Coppitters
Source: ArXiv.org (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Revealing design archetypes and flexibility in e-molecule import pathways using Modeling to Generate Alternatives and interpretable machine learning Given the central role of green e-molecule imports in the European energy transition, many studies optimize import pathways and identify a single cost-optimal solution. However, cost optimality is fragile, as real-world implementation depends on regulatory, spatial, and stakeholder constraints that are difficult to represent in optimization models and can render cost-optimal designs infeasible. To address this limitation, we generate a diverse set of near-cost-optimal alternatives within an acceptable cost margin using Modeling to Generate Alternatives, accounting for unmodeled uncertainties. Interpretable machine learning is then applied to extract insights from the resulting solution space. The approach is applied to hydrogen import pathways considering hydrogen, ammonia, methane, and methanol as carriers. Results reveal a broad near-optimal space with great flexibility: solar, wind, and storage are not strictly required to remain within 10% of the cost optimum. Wind constraints favor solar-storage methanol pathways, while limited storage favors wind-based ammonia or methane pathways. Flexibility (engineering) ; Set (abstract data type) ; Margin (machine learning) ; Renewable energy ; Supervised learning ; Space (punctuation) Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Carbon dioxide utilization in catalysis
Authors: Guoao Li | Haobo Ling | C. Joanna Su | Zhihong Liu | Guoqiang Wang | Manyi Yang | Shuhua Li
Source: ChemRxiv (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Automated Exploration of Complex Reaction Networks Accelerated by a Data-Efficient Machine Learning Potential Construct (python library) ; Matching (statistics) ; Speedup ; Complex system ; Process (computing) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; CO2 Reduction Techniques and Catalysts
Authors: Ankur Baliyan | Shanti Verma | Koichi Sasakawa | Masashi Matsumoto | Hideo Inoue | Hideo Daimon | Y. Sakurai | Yoshiharu Uchimoto | Hideto Imai
Source: Communications Chemistry (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Multimodal Synchrotron Analysis Workflow for Fuel Cell Electrocatalyst Discovery Synchrotron radiation provides exceptional sensitivity and resolution, enabling the acquisition of highly precise information critical for advancing fuel cell technology. When combined with machine learning-based, data-driven approaches, it offers powerful insights into reaction pathways and is poised to significantly accelerate the discovery of next-generation fuel cell catalysts. However, the singular characterization and complex feature space of synchrotron radiation data, necessitates a novel approach to obtain structural insights into the fuel cell catalyst. In this work, we propose a novel framework for rational electrocatalyst discovery that integrates machine learning with multimodal spectral descriptors derived from advanced synchrotron radiation techniques-XANES, EXAFS, XRD, SAXS, PDF, and HAXPES (Pt3d, Pt4f, and VB). We employed structure-performance prediction machine learning model to identify key multimodality descriptors. By assessing the importance of these modalities, we established a reverse-engineering framework for catalyst discovery, enabling the structural inference of new high-performance catalyst candidates. The experimentally derived descriptor space was validated through physics-based theoretical modelling, effectively narrowing the pool of potential candidates, and enabling the precise identification of the optimal structure-performance electrocatalyst. The proposed framework enables a shift, beyond empirical catalyst screening, toward a more efficient, interpretable, and high-throughput strategy for the discovery and design of next-generation electrocatalysts. Workflow ; Synchrotron radiation ; Electrocatalyst ; Synchrotron ; Fuel cells ; Identification (biology) ; Feature (linguistics) ; Chemical space Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Graph Neural Networks
Authors: Daniel J. Getter | Patrick Obin Sturm | Sam J. Silva
Source: Journal of Geophysical Research Machine Learning and Computation (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Data‐Driven Discovery of Atmospheric Chemical Reactions Abstract Detailed knowledge of chemical processes in the atmosphere is key to our understanding of regional air pollution and global climate change. However, a complete description of all atmospheric chemical reactions is still out of reach. This necessitates the discovery of new reactions for improved predictability and process understanding. Here, we propose a data‐driven, chemical kinetics‐oriented approach for atmospheric chemical reaction discovery. Our approach leverages time series of species abundances and an incomplete chemical mechanism to predict the existence of new chemistry by “completing” the mechanism. Species abundances and the incomplete mechanism serve as inputs to a variant of graph neural networks known as graph autoencoders (GAEs). The GAE learns a low‐dimensional representation of the chemical system to predict the existence of pairwise chemical interactions occurring between species. We assess our model using GEOS‐Chem, a widely used atmospheric chemical mechanism that represents the complex set of chemical interactions in the atmosphere. Our reaction discovery model achieves high predictive performance (0.9085 mean AUC; 90.06% average precision) in recovering unseen reactions and outperforms other competitive baselines. The success of this method solidifies its promise in discovering unknown chemical reactions and warrants further application to additional atmospheric chemistry contexts. Chemical reaction ; Chemical process ; Atmospheric chemistry ; Pairwise comparison ; Chemical species ; Graph ; Representation (politics) ; Mechanism (biology) ; Predictability Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Esma Mutlu | Sèlonou Gautier Kankinou | Ömer Tayfuroğlu | Abdulkadır Kocak
Source: The Journal of Physical Chemistry B (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Data-Efficient Equivariant NNPs Enable DFT-Accurate Simulations and Implicit Solvation Free Energies The use of machine learning (ML) potentials has emerged as a powerful approach in computational chemistry, particularly in computer-aided drug design studies. Neural network potentials (NNPs) provide a more physics-informed estimation of binding and solvation events, bridging the accuracy gap between classical force fields and quantum mechanical methods. Current universal neural network potentials (NNPs) have not yet achieved consistent chemical accuracy required for reliable molecular dynamics simulations. Accurate yet data efficient representation of potential energy surfaces and prediction of solvation free energies are essential for large-scale molecular simulations and drug-design workflows. Here, we utilize data efficient E(3)-equivariant graph neural network potentials that are capable of estimating the solvation free energies (SFEs) of small compounds with density functional theory (DFT)-level accuracy and significantly reduced computational cost. Leveraging the data efficiency of equivariant architectures, our models achieve chemical accuracy with a relatively small training data set. We demonstrate that the method is data-efficient in constructing ML potentials. Our focus is on hydration free energy changes of small compounds from the FreeSolv database. We develop and test two distinct NNPs─one for the gas phase and one for an implicit water model─that can be applied in molecular simulations and SFE calculations using implicit solvation. The Solvation Model based on Density (SMD)-based implicit NNP model achieves an accuracy of 89% while offering a substantial computational speed-up compared to its DFT counterpart, which attains 90% accuracy. Solvation ; Artificial neural network ; Implicit solvation ; Representation (politics) ; Molecular dynamics ; Bridging (networking) ; Density functional theory ; Potential energy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Physical and Chemical Molecular Interactions
Authors: Jinze Xue | Nicholas Terrel | Ignacio Pickering | Adrian Roitberg
Source: ChemRxiv (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

LAMMPS-ANI: Large Scale Molecular Dynamics Simulationswith ANI Neural Network Potential Benchmark (surveying) ; Molecular dynamics ; Work (physics) ; Quantum ; Scale (ratio) ; Artificial neural network ; Interatomic potential Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Gaussian Processes and Bayesian Inference
Authors: Weichen Hou | Jinyang Xie | Mingliang Dou
Source:  (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Non Proportional Integral Derivative Control Methods to Active Learning Directed Evolution for AGI: Enabling Specific Pharmaceutical Synthesis Recent advances in pharmaceutical synthesis have demonstrated that integrating machine-learning methodologies with directed evolution of reaction conditions can overcome challenges posed by complex reaction networks and selectivity issues. In this study, we propose a novel framework that combines non-PID control methods with an active learning-directed optimization workflow to drive an AGI system for the synthesis of specific high-value drugs. Our approach leverages a convolutional neural network (CNN) to model reaction outcomes and optimize conditions, while unsupervised hierarchical clustering is employed to identify unexpected reaction pathways or selectivity anomalies. Experimental results using a highthroughput reaction screening platform indicate significant improvements in yield and product selectivity compared to traditional optimization approaches. This integrative methodology provides a new avenue for the rapid design and optimization of pharmaceutical synthesis routes. Workflow ; Artificial neural network ; Directed evolution ; Active learning (machine learning) ; Cluster analysis ; Deep learning ; Control (management) ; Selectivity Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Process Optimization and Integration
Authors: Ghavaminia, Hoda
Source:  (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

Predicting Hydrogen Uptake in Carbon Nanotubes Using Graph Neural Networks and Molecular Simulations Hydrogen storage in carbon nanomaterials remains a critical challenge for clean energy applications. In this work, we develop a machine learning framework to accurately predict hydrogen uptake in single-walled carbon nanotubes (SWCNTs) by combining Grand Canonical Monte Carlo (GCMC) simulations with a graph neural network (GNN). A dataset of 270 configurations spanning diameters from 0.68 to 1.36 nm, temperatures of 77–350 K, and pressures up to 100 bar was generated using classical Lennard-Jones potentials validated against established force fields. The GNN, operating directly on atomic graph representations, achieves excellent predictive performance with a test R² of 0.98 and RMSE below 0.55 mmol/g. Interpretability analysis via attention mapping reveals that the model preferentially weights carbon atoms on the inner nanotube surface, consistent with the physics of nano confinement-driven physisorption. Our simulations confirm an optimal SWCNT diameter of ~0.81 nm for hydrogen storage at 77 K (~3.2 wt%), while ambient-temperature capacities remain below 0.5 wt%, highlighting the intrinsic limitations of pristine nanotubes. This work demonstrates that physics-informed machine learning, trained on modest yet high-fidelity simulation data, can enable rapid, interpretable, and reliable virtual screening of nanomaterials for hydrogen storage. Carbon nanotube ; Interpretability ; Hydrogen ; Monte Carlo method ; Artificial neural network ; Graph ; Hydrogen storage ; Graph theory Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion
Authors: Esma Mutlu (22742096) | Selonou G. Kankinou (22742099) | Omer Tayfuroglu (4421407) | Abdulkadir Kocak (1529743)
Source: Figshare (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Data-EfficientEquivariant NNPs Enable DFT-AccurateSimulations and Implicit Solvation Free Energies The use of machine learning (ML) potentials has emerged as a powerful approach in computational chemistry, particularly in computer-aided drug design studies. Neural network potentials (NNPs) provide a more physics-informed estimation of binding and solvation events, bridging the accuracy gap between classical force fields and quantum mechanical methods. Current universal neural network potentials (NNPs) have not yet achieved consistent chemical accuracy required for reliable molecular dynamics simulations. Accurate yet data efficient representation of potential energy surfaces and prediction of solvation free energies are essential for large-scale molecular simulations and drug-design workflows. Here, we utilize data efficient E(3)-equivariant graph neural network potentials that are capable of estimating the solvation free energies (SFEs) of small compounds with density functional theory (DFT)-level accuracy and significantly reduced computational cost. Leveraging the data efficiency of equivariant architectures, our models achieve chemical accuracy with a relatively small training data set. We demonstrate that the method is data-efficient in constructing ML potentials. Our focus is on hydration free energy changes of small compounds from the FreeSolv database. We develop and test two distinct NNPsone for the gas phase and one for an implicit water modelthat can be applied in molecular simulations and SFE calculations using implicit solvation. The Solvation Model based on Density (SMD)-based implicit NNP model achieves an accuracy of 89% while offering a substantial computational speed-up compared to its DFT counterpart, which attains 90% accuracy. Solvation ; Artificial neural network ; Implicit solvation ; Representation (politics) ; Molecular dynamics ; Bridging (networking) ; Density functional theory ; Potential energy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies