jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yifan Li | Axel Gomez | Kehan Cai | Chunyi Zhang | Li Fu | Weile Jia | Yotam M. Y. Feldman | Ofir Blumer | Jacob Higer | Barak Hirshberg | Shenzhen Xu | Axel Kohlmeyer | Roberto Car
Source: Open MIND (preprint, 2026-02-14)
Field: Materials Science — Materials Chemistry

fix pimd/langevin: An Efficient Implementation of Path Integral Molecular Dynamics in LAMMPS Path integral molecular dynamics (PIMD), which maps a quantum particle onto a fictitious classical system of ring polymers and propagates the "beads" of this extended classical system using molecular dynamics, is widely used to capture nuclear quantum effects (NQEs) in molecular simulations. Accurate PIMD calculations typically require a large number of beads and are therefore computationally demanding. While software packages such as i-PI offer comprehensive PIMD functionality, the high efficiency of simulations driven by machine learning interatomic potentials, such as Deep Potential (DP), calls for more efficient PIMD implementations that fully exploit modern massively parallel supercomputers. Here we present fix pimd/langevin, an efficient PIMD implementation in LAMMPS that supports commonly used features and leverages the Message Passing Interface architecture of LAMMPS to achieve high computational efficiency. We demonstrate the usage and validate the correctness of our code using liquid water as a representative example, and provide a comprehensive overview of the supported features. Then we discuss several important technical aspects of the implementation. Using DP simulations of water as a benchmark, we show that our implementation achieves several-fold acceleration compared to i-PI. Finally, we report strong and weak scaling results that demonstrate the favorable parallel performance of our code. Correctness ; Massively parallel ; Molecular dynamics ; Software ; Path (computing) ; Interface (matter) ; Domain (mathematical analysis) Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Block Copolymer Self-Assembly
Authors: 김진수 | Jae Won Kim | Hyun-Sung Yun | Hee Jeong Jeong | Kyung Mun Yeom | Soo Bin Yoo | Dong Gun Oh | Bong Joo Kang | Nam Joong Jeon | Juhwan Noh | Jino Im
Source: ChemRxiv (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Data-driven discovery of green solvents for sustainable, high-performance perovskite solar cells Eco-friendly solvents are essential for the mass production of solution-processed perovskite solar cells (PSCs). However, identifying greener alternatives to toxic solvents such as N,N-dimethylformamide (DMF) remains challenging. Here, we present a data-driven workflow integrating machine-learning (ML) predictions, density-functional-theory (DFT) calculations, and experimental validation. This approach enables rapid screening of a vast chemical space (~169 million PubChem molecules) using ML-predicted physicochemical and environmental properties, followed by DFT evaluation of solvent-precursor interactions and device-level testing. This workflow identified dimethyl malonate (DMM) as a CHEM21-Recommended, low-toxicity solvent. A 1:4 mixture of DMM and dimethyl sulfoxide (DMSO) yielded highquality FAPbI₃ films and PSCs with 25.0% PCE (V OC = 1.19 V, J SC = 24.7 mA cm⁻², FF = 0.85), comparable to DMF-based devices. To our knowledge, this is the first demonstration of DMM for perovskite synthesis. By integrating ML-based discovery with DFT, this study establishes a generalizable pathway toward sustainable PSC manufacturing. Perovskite (structure) ; Dimethyl sulfoxide ; PubChem ; Workflow ; Perovskite solar cell ; Chemical space Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Kohei Tada | Yukichika Kitano | Hiroyuki Ozaki | Yasutaka Kitagawa | Tetsu Kiyobayashi
Source: Advanced Theory and Simulations (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Graph Neural Network‐Based Interatomic Potential Calculations Combined With Grand Canonical Monte Carlo Simulation to Predict the Electrochemical Potential Profile: A Model Study Using Spinel‐Type Titanates ABSTRACT Density functional theory (DFT) is an effective approach for the in silico design of battery materials; however, DFT calculations are usually performed at 0 K, and the resulting electrochemical potential profiles are stepwise and do not resemble the continuous profiles obtained experimentally. Therefore, the estimation of temperature effects on electrochemical potential profiles remains challenging. Recently, theoretical calculations based on graph‐neural‐network‐based interatomic potentials (GNNPs) have garnered attention because of their computational speed, enabling energy estimation several orders of magnitude faster than DFT. However, because GNNPs are trained on DFT data, simulating and studying continuous energy profiles using only GNNPs has remained impossible. Herein, GNNP calculations were performed to predict electrochemical potential profiles by combining grand canonical Monte Carlo simulations, using a spinel‐type titanate as the model compound. GNNPs trained solely on Materials Project data without dispersion force correction did not reproduce experimental results even qualitatively, whereas GNNP with the correction reproduced experimental results. Furthermore, a GNNP trained on both stable and unstable structures could be utilised for electrochemical potential profile prediction. These results indicate that handling unstable structures is important for capturing thermodynamic properties, and that inadequate sampling of unstable structures can be mitigated by dispersion force corrections. Interatomic potential ; Monte Carlo method ; Potential energy ; Electrochemical potential ; Potential method ; Potential of mean force ; Molecular dynamics ; Electrochemistry ; Energy (signal processing) Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; X-ray Diffraction in Crystallography
Authors: Yifan Li | Axel Gomez | Kehan Cai | Chunyi Zhang | Li Fu | Weile Jia | Yotam M. Y. Feldman | Ofir Blumer | Jacob Higer | Barak Hirshberg | Shenzhen Xu | Axel Kohlmeyer | Roberto Car
Source: ArXiv.org (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

fix pimd/langevin: An Efficient Implementation of Path Integral Molecular Dynamics in LAMMPS Path integral molecular dynamics (PIMD), which maps a quantum particle onto a fictitious classical system of ring polymers and propagates the "beads" of this extended classical system using molecular dynamics, is widely used to capture nuclear quantum effects (NQEs) in molecular simulations. Accurate PIMD calculations typically require a large number of beads and are therefore computationally demanding. While software packages such as i-PI offer comprehensive PIMD functionality, the high efficiency of simulations driven by machine learning interatomic potentials, such as Deep Potential (DP), calls for more efficient PIMD implementations that fully exploit modern massively parallel supercomputers. Here we present fix pimd/langevin, an efficient PIMD implementation in LAMMPS that supports commonly used features and leverages the Message Passing Interface architecture of LAMMPS to achieve high computational efficiency. We demonstrate the usage and validate the correctness of our code using liquid water as a representative example, and provide a comprehensive overview of the supported features. Then we discuss several important technical aspects of the implementation. Using DP simulations of water as a benchmark, we show that our implementation achieves several-fold acceleration compared to i-PI. Finally, we report strong and weak scaling results that demonstrate the favorable parallel performance of our code. Correctness ; Massively parallel ; Molecular dynamics ; Software ; Path (computing) ; Interface (matter) ; Domain (mathematical analysis) Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Block Copolymer Self-Assembly
Authors: Dorota Wilk-Kolodziejczyk | Valentina Colla | Sandra Gajoch | Łukasz Marcjan
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Comparison of the performance of models based on decision trees, LSTM networks, and XAI algorithms in predicting mass changes of metallic materials under high-temperature corrosion conditions Decision tree ; Set (abstract data type) ; Variable (mathematics) ; Feature (linguistics) ; Tree (set theory) ; Data set ; Process (computing) ; Regression analysis ; Artificial neural network ; Regression Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Mingzhi Zhao | Xinrui Shen | Junyao Wu | Wei Gao
Source: ACS Omega (article, 2026-03-12)
Field: Chemistry — Inorganic Chemistry

Multiscale Computational Enzymology of CYP450 Biotransformation of Organic Halogenated Pollutants: Methods and Environmental Perspectives Halogenated organic pollutants (HOPs) represent a persistent class of environmental contaminants with high stability, bioaccumulation potential, and toxicity. Cytochrome P450 enzymes (CYP450s) play a pivotal role in their oxidative biotransformation, yet mechanistic details often remain unresolved by experimental methods alone. To bridge this gap, this review synthesizes recent advances in multiscale computational enzymology to establish an integrated workflow. This approach links molecular docking and molecular dynamics (MD) for conformational sampling with quantum mechanics and molecular mechanics (QM/MM) and density functional theory (DFT) for electronic structure analysis. We critically evaluate how this multiscale framework complements experimental data to resolve enzyme-substrate recognition, conformational gating, and bond-activation energetics. These are mechanistic details that single-scale approaches cannot provide. Furthermore, we discuss emerging data-driven tools while addressing critical limitations, including training-data bias in machine learning and the lack of standardization. By connecting atomistic mechanistic insights to broader environmental implications, we propose a roadmap for developing predictive models to support HOP risk assessment and bioremediation strategies. Biotransformation ; Quantitative structure–activity relationship ; Organic chemicals ; Human health ; Organic solvent Vanadium and Halogenation Chemistry ; Metal-Catalyzed Oxygenation Mechanisms ; Environmental remediation with nanomaterials
Authors: Xiao Liang | Hanqi Wang | Xichen Zhou | Qianyong Zhu | Cheng Zhang | Wenlong Xiao | Yiqian Guo | Zhen Li | Yu Liu | Peng Kang | Lei Zheng | Hongbo Guo | Shiteng Zhao
Source: Acta Materialia (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Revealing cryo-loading induced twinning and its impact on titanium via correlative data-driven mechanomicroscopy Crystal twinning ; Correlative ; Titanium ; Microstructure ; Titanium alloy Microstructure and mechanical properties ; Titanium Alloys Microstructure and Properties ; Force Microscopy Techniques and Applications
Authors: Jiannian Yin | Jian Zang | Jianrong Liu | Li Zhou
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Modeling of microstructure evolution of Ti65 alloy during high temperature compression Dynamic recrystallization ; Microstructure ; Deformation (meteorology) ; Compression (physics) ; Artificial neural network ; Grain size ; Hot working ; Finite element method ; Titanium alloy Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: Christof Duhme | Chris Lippe | Verena Hoerr | Xiaoyi Jiang
Source: arXiv (Cornell University) (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

Transformer-based Parameter Fitting of Models derived from Bloch-McConnell Equations for CEST MRI Analysis Chemical exchange saturation transfer (CEST) MRI is a non-invasive imaging modality for detecting metabolites. It offers higher resolution and sensitivity compared to conventional magnetic resonance spectroscopy (MRS). However, quantification of CEST data is challenging because the measured signal results from a complex interplay of many physiological variables. Here, we introduce a transformer-based neural network to fit parameters such as metabolite concentrations, exchange and relaxation rates of a physical model derived from Bloch-McConnell equations to in-vitro CEST spectra. We show that our self-supervised trained neural network clearly outperforms the solution of classical gradient-based solver. Artificial neural network ; Sensitivity (control systems) ; Relaxation (psychology) ; Magnetic resonance imaging ; SIGNAL (programming language) ; Saturation (graph theory) ; Resolution (logic) ; Limiting Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Molecular Sensors and Ion Detection
Authors: Yunyun Liu | Heng Zhang | Rui Cao | Haojie Gao | Qiang Yu | Meng An
Source: Fuel (article, 2026-02-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable machine learning for optimizing the specific capacitance of biomass-derived supercapacitors Gradient boosting ; Random forest ; Boosting (machine learning) ; Capacitance ; Overfitting ; Decision tree ; Regression ; Categorical variable ; Hyperparameter Supercapacitor Materials and Fabrication ; Catalysis for Biomass Conversion ; Thermochemical Biomass Conversion Processes
Authors: Ziyi Wang | Luneng Zhao | Yuan Chang | Chunqiang Zhuang | Hongsheng Liu | Jiaxu Liu | Tao Liu | Junfeng Gao
Source: Advanced Energy Materials (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

The Reconstruction Mechanism and NH <sub>3</sub> ‐SCR Kinetics of Mn Clusters on MoS <sub>2</sub> by Ab Initio Data Driven Machine Learning Simulations ABSTRACT The strong metal‐support interactions (SMSI) between supported clusters and substrate can drive the local structure reconstructions, which stabilize the supported clusters to avoid migration and aggregation. Such reconstruction and stabilization mechanism are crucial to construct the atomically dispersed catalysts (ADCs), but they are too complex to simulate in most catalyst theoretical studies for a relative long time. Herein, an accurate machine learning potential (MLP) is employed into Monte Carlo simulation on the Mn N (1 ≤ N ≤ 7) clusters supported on MoS 2 layer. The adsorption, reconstruction and thermodynamic and kinetical stabilization of Mn clusters on perfect and defective MoS 2 are compared studied. The results indicate that the S vacancies can effectively anchor Mn clusters and are feasible to control the dual‐atom catalysts (DACs) on the MoS 2 surface. Besides, Comparative analysis reveals that the Mn 2 @MoS 2 ‐S 2 V exhibits superior NH 3 ‐SCR catalytic activity. The complete reaction process of Mn 2 @MoS 2 ‐S 2 V following the “Fast‐SCR” mechanism and the NO 2 reduction pathway is the dominant route, with a rate‐determining barrier of 1.03 eV. This work provides a pioneer way to disclose the very complex reconstruction of supported clusters with SMSI in simulation, which is indeed helpful to design real atomic structure of ADCs. Catalysis ; Ab initio ; Mechanism (biology) ; Substrate (aquarium) ; Kinetics ; Kinetic Monte Carlo ; Work (physics) Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction
Authors: Sameer Al‐Dahidi | Ma’en S. Sari
Source: Advances in Mechanical Engineering (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Critical buckling load prediction in tapered axially functionally graded nanobeams using machine learning under different boundary conditions This study presents a machine learning (ML) framework for predicting the critical buckling loads of tapered axially functionally graded (AFG) nanobeams resting on elastic foundations, governed by Eringen’s nonlocal elasticity theory. Due to their position-dependent stiffness and complex boundary conditions, closed-form solutions for such nanobeams are computationally expensive and analytically intractable. To address this, four supervised data-driven regression models are developed: linear regression (LR), polynomial regression (PR), support vector machine (SVM), and artificial neural network (ANN). These models are used to predict buckling loads for three boundary conditions: clamped–clamped (C–C), simply supported (S-S), and clamped-simply supported (C–S). A comprehensive dataset is generated using a finely discretized parametric sweep of the beam’s geometric and material properties. Pearson correlation coefficient (PCC) and variance inflation factor (VIF) analyses are conducted to ensure feature relevance and independence. Results show that the ANN model consistently outperform other test models, achieving coefficient of determination ( R 2 ) values exceeding 99%, root mean square error (RMSE) values below 1.1 (in normalized units), and mean absolute error (MAE) values below 0.67 (in normalized units) across all boundary conditions. The findings demonstrate that ML and ANN offer a robust and computationally efficient surrogate to traditional methods, with strong generalization capabilities. Buckling ; Boundary value problem ; Artificial neural network ; Parametric statistics ; Support vector machine ; Axial symmetry ; Mean squared error ; Stiffness ; Polynomial ; Reduction (mathematics) Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Composite Material Mechanics
Authors: Gbolahan Joseph Adekoya | Oluwasegun Chijioke Adekoya | Mpho Muloiwa | Emmanuel Rotimi Sadiku | Williams Kehinde Kupolati | Yskandar Hamam
Source: Advanced Composites and Hybrid Materials (article, 2026-02-27)
Field: Materials Science — Polymers and Plastics

Computational strategies for designing PEDOT-Based electrodes in electronic applications Poly(3,4-ethylenedioxythiophene) (PEDOT) and its derivatives have emerged as critical materials in modern electronic applications due to their exceptional electrical conductivity, biocompatibility, and processability. This comprehensive review examines the state-of-the-art computational strategies employed for designing and optimizing PEDOT-based electrodes across various electronic applications. We systematically assess multiscale modeling approaches, from quantum mechanical calculations to macroscopic device simulations, including Density Functional Theory (DFT), atomistic and coarse-grained Molecular Dynamics (MD), Finite Element Method (FEM), and emerging Machine Learning/Artificial Intelligence techniques. The review elucidates how these computational methods provide critical insights into PEDOT’s electronic structure, charge transport mechanisms, morphological characteristics, and interfacial behaviors. Particular emphasis is placed on structure-property relationships, including the aromatic-to-quinoid transition upon doping, the formation of polarons and bipolarons, the influence of π-π stacking on charge mobility, and the critical role of counterions in modulating electronic performance. We demonstrate their application in designing optimized electrodes for supercapacitors, organic electrochemical transistors, and flexible electronics. Finally, we analyze existing limitations in current computational frameworks and identify promising future directions, including multiscale integration, improved force fields, and quantum machine learning, that will accelerate the rational design of next-generation PEDOT-based materials with tailored functionalities. This review highlights the integration of computational strategies like DFT, MD, FEM, and ML/AI in designing PEDOT-based electrodes, emphasizing their role in understanding electronic structure, charge transport, morphology, and mechanical behavior. It explores recent advancements, applications, limitations, and future directions for optimizing PEDOT electrodes in electronic devices. Stacking ; Electronics ; Multiscale modeling ; Computational model ; Density functional theory ; Electronic structure ; Finite element method ; Quantum Conducting polymers and applications ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Changjun Cheng | Daniel Persaud | KANGMING LI | Michael J. Moorehead | Natalie Page | C. Lavoie | Beatriz D. Moreno | A. Couet | S. E. Lofland | Jason Hattrick-Simpers
Source: Applied Materials Today (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

High-throughput validation of phase formability and simulation accuracy of cantor alloys • Perform high-throughput in-situ XRD on co-sputtered FeNiMnCr HEA thin films. • Introduce a modified logistic function to calibrate DFT-predicted transformation temperatures. • Propose a quantitative confidence metric linking high-throughput experiments and computations • Enable systematic assessment of computational models to guide future experiments. High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a function of composition and temperature. However, the integration of computational predictions with experimental validation remains challenging in high-throughput studies. In this work, we introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations, providing a quantitative measure of the confidence of machine learning models trained on either DFT or CALPHAD input in accounting for experimental evidence. The experimental dataset was generated via high-throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries, heated from room temperature to ∼1000 °C. Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation. This integrated approach demonstrates where strong overall agreement exists between computation and experiment, while also identifying key discrepancies, particularly in FCC/BCC predictions at Mn-rich regions to inform future model refinement. Formability ; CALPHAD ; Computation ; Metric (unit) ; Phase (matter) ; Stability (learning theory) ; Function (biology) ; Transformation (genetics) ; Measure (data warehouse) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Dorota Wilk-Kolodziejczyk | Valentina Colla | Sandra Gajoch | Łukasz Marcjan
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Comparison of the performance of models based on decision trees, LSTM networks, and XAI algorithms in predicting mass changes of metallic materials under high-temperature corrosion conditions Decision tree ; Set (abstract data type) ; Variable (mathematics) ; Feature (linguistics) ; Data set ; Tree (set theory) ; Process (computing) ; Regression analysis ; Artificial neural network ; Regression Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals