jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew Reinhard
Source: ArXiv.org (preprint, 2025-12-10)
Field: Materials Science — Materials Chemistry

Predicting Polymer Solubility in Solvents Using SMILES Strings Understanding and predicting polymer solubility in various solvents is critical for applications ranging from recycling to pharmaceutical formulation. This work presents a deep learning framework that predicts polymer solubility, expressed as weight percent (wt%), directly from SMILES representations of both polymers and solvents. A dataset of 8,049 polymer solvent pairs at 25 deg C was constructed from calibrated molecular dynamics simulations (Zhou et al., 2023), and molecular descriptors and fingerprints were combined into a 2,394 feature representation per sample. A fully connected neural network with six hidden layers was trained using the Adam optimizer and evaluated using mean squared error loss, achieving strong agreement between predicted and actual solubility values. Generalizability was demonstrated using experimentally measured data from the Materials Genome Project, where the model maintained high accuracy on 25 unseen polymer solvent combinations. These findings highlight the viability of SMILES based machine learning models for scalable solubility prediction and high-throughput solvent screening, supporting applications in green chemistry, polymer processing, and materials design. Polymer ; Solubility ; Solvent ; Generalizability theory ; Representation (politics) ; Backpropagation ; Chemometrics ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Elyar Tourani | Brian J. Edwards | Bamin Khomami
Source: The Journal of Chemical Physics (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Machine learning workflow for analysis of high-dimensional order parameter space: A case study of polymer crystallization from molecular dynamics simulations Currently, identification of crystallization pathways in polymers is being carried out using molecular simulation-based data on a preset cutoff point on a single order parameter (OP) to define nucleated or crystallized regions. Aside from sensitivity to the cutoff, each of these OPs introduces its own systematic biases. In this study, an integrated machine learning workflow is presented to quantify accurately crystallinity in polymeric systems using atomistic molecular dynamics simulation data. Each atom is represented by a high-dimensional feature vector that combines geometric, thermodynamic-like, and symmetry-based descriptors. Low-dimensional embeddings are employed to expose latent structural fingerprints within atomic environments. Subsequently, unsupervised clustering on the embeddings is used to identify crystalline and amorphous atoms with high fidelity. After generating high-quality labels with multidimensional data, we use supervised learning techniques to identify a minimal set of order parameters that can fully capture this label. Various tests were conducted to reduce the feature set, and it is shown that using only three order parameters, namely q6, S̄i, and p2, is sufficient to recreate the crystallization labels with great accuracy. Based on these observed OPs, the crystallinity index (C-index) is introduced as the logistic regression model's probability of crystallinity. This measure remains bimodal at all stages of the process and achieves >98% classification performance. Notably, a model trained on one or a few snapshots enables efficient on-the-fly computation of crystallinity. Lastly, we demonstrate how the optimal C-index fit evolves during various stages of crystallization, supporting the hypothesis that entropy dominates early nucleation, while q6 gains relevance in the later stages. This workflow yields a data-driven strategy for OP selection and provides a generalizable metric to monitor structural transformations in large-scale polymer simulations. Workflow ; Cluster analysis ; Molecular dynamics ; Supervised learning ; Crystallinity ; Unsupervised learning ; Crystallization ; Crystallization of polymers ; Feature (linguistics) Machine Learning in Materials Science ; Polymer crystallization and properties ; Block Copolymer Self-Assembly
Authors: Makhmadzoir Kayumov | Shamsuddin Alomuddinovich Bozorov | A R Ashurov
Source: “Polytechnic Bulletin” Series “Intellect Innovations Investments” (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

MACHINE LEARNING FOR CLASSIFICATION AND CLUSTER ANALYSIS OF LUMINESCENT MATERIALS WITH AND WITHOUT RARE EARTH ELEMENTS This study presents a data analysis approach for classifying inorganic phosphores and identifying functional substitutes for rare earth elements (RE). Using a dataset of 3911 phosphorous materials, where machine learning methods were applied to classify materials and perform cluster analysis based on their optical properties. The main result was the identification of spectral regions in which activators without RE (Mn2⁺, Bi3⁺) exhibit excitation and emission profiles identical to traditional RE (Tb3⁺, Eu3⁺) dopants, confirming their potential as stable alternatives for solid-state lighting. Rare earth ; Cluster (spacecraft) ; Identification (biology) ; Pattern recognition (psychology) ; Luminescence ; Statistical classification Luminescence Properties of Advanced Materials ; Lanthanide and Transition Metal Complexes ; Thermal Expansion and Ionic Conductivity
Authors: Bianchi, Michele Giovanni | Fiorentin, Michele Re | Risplendi, Francesca | Pirri, Candido Fabrizio | Parrinello, Michele | Bonati, Luigi | Cicero, Giancarlo
Source: ArXiv.org (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Electrochemical Interfaces at Constant Potential: Data-Efficient Transfer Learning for Machine-Learning-Based Molecular Dynamics Simulating electrified metal/water interfaces with explicit solvent under constant potential is essential for understanding electrochemical processes, yet remains prohibitively expensive with ab initio methods. We present TRECI, a data-efficient workflow for constructing machine learning force-fields (ML-FFs) that achieve ab initio-level accuracy in electronically grand-canonical molecular dynamics. By leveraging transfer learning from general-purpose and domain-specific models, TRECI enables stable and accurate simulations across a wide potential range using a reduced number of reference configurations. This efficiency allows the use of high-level meta-GGA functionals and rigorous surface-electrification schemes. Applied to Cu(111)/water, models trained on just one thousand configurations yield accurate molecular dynamics simulations, capturing bias-dependent solvent restructuring effects not previously reported. TRECI offers a general strategy for characterising diverse materials and interfacial chemistries, significantly lowering the cost of realistic constant-potential simulations and expanding access to quantitative electrochemical modelling. Constant (computer programming) ; Molecular dynamics ; Workflow ; Yield (engineering) ; Electrochemistry ; Range (aeronautics) ; Ab initio Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Couzinie Yannick | Daimon Shunsuke | Nishi Hirofumi | Ito Natsuki | Harazono Yusuke | Matsushita Yu-ichiro
Source: Institutional Repositories DataBase (IRDB) (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Towards Improved Quantum Machine Learning for Molecular Force Fields This study explores the use of equivariant quantum neural networks (QNN) for generating molecular force fields, focusing on the rMD17 dataset. We consider a QNN architecture based on previous research and point out shortcomings in the parametrization of the atomic environments, that limits its expressivity as an interatomic potential and precludes transferability between molecules. We propose a revised QNN architecture that addresses these shortcomings. While both QNNs show promise in force prediction, with the revised architecture showing improved accuracy, they struggle with energy prediction. Further, both QNNs architectures fail to demonstrate a meaningful scaling law of decreasing errors with increasing training data. These findings highlight the challenges of scaling QNNs for complex molecular systems and emphasize the need for improved encoding strategies, regularization techniques, and hybrid quantum-classical approaches. Scaling ; Point (geometry) ; Quantum ; Architecture ; Force field (fiction) ; Encoding (memory) ; Regularization (linguistics) ; Transferability Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Physical and Chemical Molecular Interactions
Authors: Hari Prasad Bhatta | Hyo‐Kyung Han | Ravi Maharjan | Seong Hoon Jeong
Source: Preprints.org (preprint, 2025-08-05)
Field: Chemistry — Analytical Chemistry

Advanced Techniques to Improve Amorphous Dispersion Performance with Quality Design, Physicochemical Monitoring, Molecular Simulation, and Machine Learning Amorphous solid dispersions (ASDs) represent a promising formulation strategy for improving the solubility and bioavailability of poorly water-soluble drugs, a major challenge in pharmaceutical development. This review provides a comprehensive analysis of the physicochemical principles underlying ASD stability, with a focus on drug–polymer miscibility, molecular mobility, and thermodynamic properties. The main manufacturing techniques—including hot-melt extrusion, spray drying, and KinetiSol® dispersing—are discussed for their impact on formulation homogeneity and scalability. Recent advances in excipient selection, molecular modeling, and in silico predictive approaches have transformed ASD design, reducing dependence on traditional trial-and-error methods. Furthermore, machine learning and artificial intelligence (AI)-based computational platforms are reshaping formulation strategies by enabling accurate predictions of drug–polymer interactions and physical stability. Advanced characterization methods such as solid-state NMR, IR, and dielectric spectroscopy provide valuable insights into phase separation and recrystallization. Despite these technological innovations, ensuring long-term stability and maintaining supersaturation remain significant challenges for ASDs. Integrated formulation design frameworks, including PBPK modeling and accelerated stability testing, offer potential solutions to address these issues. Future research should emphasize interdisciplinary collaboration, leveraging computational advancements together with experimental validation to refine formulation strategies and accelerate clinical translation. The scientists can unlock the full therapeutic potential with emerging technologies and a data–driven approach. Dispersion (optics) ; Quality (philosophy) ; Computer science ; Amorphous solid ; Materials science ; Process engineering ; Engineering ; Chemistry ; Physics ; Optics ; Crystallography Analytical chemistry methods development ; Cultural Heritage Materials Analysis ; Microfluidic and Capillary Electrophoresis Applications
Source: International Research Journal of Modernization in Engineering Technology and Science (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Predictions of Lattice Parameters in NiTi High-Entropy Shape Memory Alloys Using Different Machine Learning Models This work applied three machine learning (ML) models-linear regression (LR), random forest (RF), and support vector regression (SVR)-to predict the lattice parameters of the monoclinic B19 phase in two distinct training datasets: previously published NiTi-based high-entropy shape-memory alloys (HESMAs).Our findings showed that LR provided the most accurate predictions for ac, am, bm, and cm in NiTi-based HESMAs, while RF excelled in computing m for both datasets.SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets.A combination approach of RF and LR models enhanced the accuracy of predicting lattice parameters of martensitic phases in various shape-memory materials for stable high-temperature applications. Nickel titanium ; Shape-memory alloy ; Lattice (music) ; Titanium alloy Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Machine Learning in Materials Science
Authors: Zichang Lin | Wenjie Chen | Yitao Lin | Xinxin Zhang | Yuegang Zhang
Source: arXiv (Cornell University) (preprint, 2025-12-29)
Field: Materials Science — Materials Chemistry

Universal and Experiment-calibrated Prediction of XANES through Crystal Graph Neural Network and Transfer Learning Strategy Theoretical simulation is helpful for accurate interpretation of experimental X-ray absorption near-edge structure (XANES) spectra that contain rich atomic and electronic structure information of materials. However, current simulation methods are usually too complex to give the needed accuracy and timeliness when a large amount of data need to be analyzed, such as for in-situ characterization of battery materials. To address these problems, artificial intelligence (AI) models have been developed for XANES prediction. However, instead of using experimental XANES data, the existing models are trained using simulated data, resulting in significant discrepancies between the predicted and experimental spectra. Also, the universality across different elements has not been well studied for such models. In this work, we firstly establish a crystal graph neural network, pre-trained on simulated XANES data covering 48 elements, to achieve universal XANES prediction with a low average relative square error of 0.020223; and then utilize transfer learning to calibrate the model using a small experimental XANES dataset. After calibration, the edge energy misalignment error of the predicted S, Ti and Fe K edge XANES is significantly reduced by about 80%. The method demonstrated in this work opens up a new way to achieve fast, universal, and experiment-calibrated XANES prediction. XANES ; Artificial neural network ; Graph ; Spectral line ; Absorption (acoustics) ; Enhanced Data Rates for GSM Evolution Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; X-ray Diffraction in Crystallography
Authors: Yihua Qian | Tingyu Sun | Qing Wang | Qibin Xia | Hongxia Xi
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Machine Learning and Molecular Simulation driven Design of Antioxidants for Ester Insulation Oils Antioxidants are critical additives in both petroleum and bio-based ester insulating oils as they inhibit oxidative degradation and extend fluid life. In this study, 91 antioxidant derivatives are designed by integrating machine learning and molecular simulation, aiming to enhance their practical applications in power systems. Key antioxidant descriptors—including Frontier molecular orbitals energy gap(HOMO-LUMO gap), the minimum absolute charge of the hydrogen atom (Abs_H_Charge), and ionization potential (IP)—are analyzed to assess antioxidant performance. Among these derivatives, VE_CN_3 (functionalized vitamin-E by cyano group at the third active site) is identified as the most promising candidate, demonstrating superior oxidative stability. To further predict antioxidant performance, 12 machine learning regression models are developed using the key molecular descriptors. Among these models, Random Forest (RF) and Gradient Boosting (GBoost) exhibit the highest predictive accuracies (with R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> achieving 0.83), highlighting the potential of machine learning in capturing the relationship between molecular structure and antioxidant efficacy. SHAP (SHapley Additive exPlanations) analysis further reveals that specific molecular features, such as discontinuous methylene groups and ring structures, play critical roles in influencing antioxidant performance. Future research will aim to expand the molecular dataset to further validate and refine the predictive models, including the incorporation of a more comprehensive range of antioxidant functional groups and additional physicochemical parameters directly relevant to Hydrogen Atom Transfer (HAT) and Single Electron Transfer (SET) mechanisms. Furthermore, in order to rigorously assess metal-antioxidant interactions and corrosion effects, subsequent studies will integrate descriptors related to metal coordination propensity and evaluate candidate molecules through accelerated aging experiments employing copper and silver strips. Antioxidant ; Molecular descriptor ; Random forest ; Support vector machine ; Hydrogen atom ; Molecule Free Radicals and Antioxidants ; Corrosion Behavior and Inhibition ; Power Transformer Diagnostics and Insulation
Authors: JeongMin Shin | Sangbaek Park | JinHyeok Cha
Source: Journal of the Korean Society for Precision Engineering (article, 2025-12-01)
Field: Chemistry — Inorganic Chemistry

Comparative Study of CO2 Diffusion in Multiple Metal-organic Frameworks via Neural Network Potential Molecular Dynamics Simulation Carbon capture and storage is a vital strategy for mitigating rising atmospheric carbon dioxide, and metal–organic frameworks (MOFs) have gained attention as promising sorbents. Numerous simulations have examined factors governing CO2 capture in MOFs—such as diffusion in MOF-74 under varying temperatures and process modeling of MOF-5—but most were limited to specific structures or conditions, hindering a systematic understanding of diffusion across diverse MOFs. Conventional computational methods also face constraints: density functional theory mainly provides static energy evaluations, while molecular dynamics relies on fixed force fields with poor transferability and an inability to describe reactive events. To overcome these limitations, this study employs molecular dynamics simulations driven by neural network potentials to evaluate CO2 diffusivity in 17 types of MOFs. Results reveal significant variation in transport behavior, with zeolitic-imidazolate framework-3 showing the highest diffusivity and MOF-74 the lowest—an approximately 19-fold difference. These findings highlight the capability of neural-network-based molecular dynamics to deliver consistent and quantitative assessments of CO2 transport in MOFs, providing a reliable framework for the rational design of next-generation capture materials. Molecular dynamics ; Transferability ; Process (computing) ; Diffusion ; Thermal diffusivity ; Artificial neural network ; Force field (fiction) ; Potential energy Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Lauren Way (17887327) | Catalin D. Spataru (1427518) | Reese E. Jones (6627935) | Dallas R. Trinkle (12274778) | Andrew J. E. Rowberg (6263474) | Joel B. Varley (1292586) | Robert B. Wexler (5108318) | Christopher M. Smyth (4509253) | Tyra C. Douglas (8810597) | Sean R. Bishop (1611256) | Elliot J. Fuller (3916769) | Anthony H. McDaniel (1610323) | Stephan Lany (1752925) | Matthew D. Witman (1949530)
Source: Figshare (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Defect DiffusionGraph Neural Networks for MaterialsDiscovery in High-Temperature Energy Applications The migration of crystallographic defects dictates material properties and performance for a plethora of technological applications. Density functional theory (DFT)-based nudged elastic band (NEB) calculations are a powerful computational technique for predicting defect migration activation energy barriers, yet they become prohibitively expensive for high-throughput screening of defect diffusivities. Without introducing hand-crafted (i.e., chemistry- or structure-specific) descriptors, we propose a generalized deep learning approach to train surrogate models for NEB energies of vacancy migration by hybridizing graph neural networks with transformer encoders and simply using pristine host structures as input. With sufficient training data, computationally efficient and simultaneous inference of vacancy defect thermodynamics and migration activation energies can be obtained to compute temperature-dependent vacancy diffusivities and to down-select candidates for more thorough DFT analysis or experiments. Thus, as we specifically demonstrate for potential water-splitting materials, candidates with desired defect thermodynamics, kinetics, and host stability properties can be more rapidly targeted from open-source databases of experimentally validated or hypothetical materials. Vacancy defect ; Inference ; Artificial neural network ; Density functional theory ; Graph ; Encoder ; Graph theory Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Inorganic Chemistry and Materials
Authors: Xuhe Gong | Hengbo Zhao | Fu Xiao | Jiangfang Lian | Qifan Yang | Ran Li | Ruijuan Xiao | Tao Zhang | Hong Li
Source: ArXiv.org (preprint, 2025-08-16)
Field: Materials Science — Materials Chemistry

Accelerating Amorphous Alloy Discovery: Data-Driven Property Prediction via General-Purpose Machine Learning Interatomic Potential While traditional trial-and-error methods for designing amorphous alloys are costly and inefficient, machine learning approaches based solely on composition lack critical atomic structural information. Machine learning interatomic potentials, trained on data from first-principles calculations, offer a powerful alternative by efficiently approximating the complex three-dimensional potential energy surface with near-DFT accuracy. In this work, we develop a general-purpose machine learning interatomic potential for amorphous alloys by using a dataset comprising 20400 configurations across representative binary and ternary amorphous alloys systems. The model demonstrates excellent predictive performance on an independent test set, with a mean absolute error of 5.06 meV/atom for energy and 128.51 meV/Å for forces. Through extensive validation, the model is shown to reliably capture the trends in macroscopic property variations such as density, Young's modulus and glass transition temperature across both the original training systems and the compositionally modified systems derived from them. It can be directly applied to composition-property mapping of amorphous alloys. Furthermore, the developed interatomic potential enables access to the atomic structures of amorphous alloys, allowing for microscopic analysis and interpretation of experimental results, particularly those deviating from empirical trends.This work breaks the long-standing computational bottleneck in amorphous alloys research by developing the first general-purpose machine learning interatomic potential for amorphous alloy systems. The resulting framework provides a robust foundation for data-driven design and high-throughput composition screening in a field previously constrained by traditional simulation limitations. Amorphous solid ; Interatomic potential ; Amorphous metal ; Ternary operation ; Bottleneck ; Energy (signal processing) ; Binary number ; Work (physics) Machine Learning in Materials Science ; 3D Shape Modeling and Analysis