jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ying He | Isay Katsman | Sung Woo Sohn | Mark Shattuck | Corey O apos Hern | Anna C. Gilbert | Jan Schroers
Source: Acta Materialia (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Can Machine Learning Predict the Liquidus Temperature of Binary Alloys? Accurate prediction of the liquidus temperature ( ) of alloys remains a challenge despite numerous theoretical models. Here, we explore and analyze the degree to which machine learning, ML, strategies can be used to predict . We use established literature data on liquidus temperatures of 85523 binary alloys to train ML models using various feature vectors to represent the alloys. While our results are comparable to previous studies, the persistent ~8% error underscores the limitations of current ML models for practical usage. The suboptimal accuracy leads us to question how well-defined the problem is and to what degree fundamental limitations prevent us from attaining more accurate predictions. We identify two major challenges in predicting the liquidus temperature of binary alloys through supervised ML algorithms. One challenge is representing the relevant characteristics of an alloy that determines liquidus temperature through appropriate features. The other fundamental challenge is the discreteness of atoms properties. The difference between two elements and thereby alloy systems is significant, which makes it difficult to learn from one alloy system to predict properties of another. We argue that these problems can be reduced to some extent, however these challenges are common in complex materials science problems and constitute a fundamental challenge in applying supervised ML strategies in this context. Liquidus ; Binary number ; Feature (linguistics) ; Binary alloy ; Binary classification ; Degree (music) ; Binary data Machine Learning in Materials Science
Authors: Siya Zhu | ‪Doğuhan Sarıtürk | Raymundo Arróyave
Source: npj Computational Materials (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy PhaseForge is a framework for high-throughput alloy phase diagram prediction using machine learning interatomic potentials (MLIPs) integrated with ATAT and CALPHAD modeling. Includes tools for structure relaxation, molecular dynamics, and TDB generation. Workflow ; Benchmark (surveying) ; Phase diagram ; CALPHAD ; Diagram Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Nuclear Materials and Properties
Authors: Shiv Prakash | Patel, Nandan | Hans‐Arno Jacobsen | Viki Kumar Prasad
Source: ArXiv.org (preprint, 2025-04-03)
Field: Materials Science — Materials Chemistry

A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning We introduce MHNpath, a machine learning-driven retrosynthetic tool designed for computer-aided synthesis planning. Leveraging modern Hopfield networks and novel comparative metrics, MHNpath efficiently prioritizes reaction templates, improving the scalability and accuracy of retrosynthetic predictions. The tool incorporates a tunable scoring system that allows users to prioritize pathways based on cost, reaction temperature, and toxicity, thereby facilitating the design of greener and cost-effective reaction routes. We demonstrate its effectiveness through case studies involving complex molecules from ChemByDesign, showcasing its ability to predict novel synthetic and enzymatic pathways. Furthermore, we benchmark MHNpath against existing frameworks using the PaRoutes dataset, achieving a solution rate of 85.4% and replicating 69.2% of experimentally validated "gold-standard" pathways. Our case studies reveal that the tool can generate shorter, cheaper, moderate-temperature routes employing green solvents, as exemplified by compounds such as dronabinol, arformoterol, and lupinine. Retrosynthetic analysis ; Scalability ; Benchmark (surveying) ; Training set ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Natural Products and Biosynthesis
Authors: Nicholas Ballard
Source: Polymer (article, 2025-09-21)
Field: Materials Science — Materials Chemistry

Rapid training of neural networks as accelerated, differentiable surrogates of kinetic Monte Carlo polymerization models Monte Carlo method ; Artificial neural network ; Kinetic Monte Carlo ; Differentiable function ; Range (aeronautics) ; Monte Carlo molecular modeling Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Eisenhower D. Aldemita II | Paul Rossener R. Regonia | Eduardo R. Magdaluyo
Source: Proceedings of International Exchange and Innovation Conference on Engineering & Sciences (IEICES) (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Optimizing Bulletproof Natural Fiber-Reinforced Polymer Composite Materials Using a Hybrid Computational Simulation and Machine Learning Approach This study investigates the optimization of natural fiber-reinforced polymer composite (NFRPCs) creation for bulletproof applications by integrating computational simulation and machine learning (ML). We incorporate abaca (Musa textilis) or pineapple leaf fibers (Piñatex), along with aramid and carbon fibers, into layered composite plates. Ballistic performance was modeled and predicted using simulated data from ANSYS Explicit Dynamics and validated through live bullet testing. ML models, such as Support Vector Machine (SVM) and Random Forest (RF) with optimized hyperparameters, achieved up to 80% prediction accuracy and an F1-score of 82% for abaca-reinforced composites, closely aligning with experimental results. However, lower prediction accuracy was observed for Piñatex-based composites, due to fiber variability and other factors identified in the study. This hybrid methodology highlights the potential of combining simulation and ML to reduce reliance on extensive live bullet testing, providing a data-driven pathway for the efficient development of high-performance bulletproof composite materials. Computational simulation ; Natural (archaeology) ; Composite number ; Computational learning theory ; Support vector machine ; Computational model Machine Learning in Materials Science ; Composite Material Mechanics ; Epoxy Resin Curing Processes
Authors: Zhuo Su | Bingling Dai | Xue Wang | Yibin Jiang | Wenbin Lin | Cheng Wang
Source: Angewandte Chemie (article, 2025-05-07)
Field: Chemistry — Inorganic Chemistry

Machine Learning Reveals In‐Cavity Versus Surface Activity for Selective C─H Borylation by Metal‐Organic Framework Catalysts Abstract Metal‐organic frameworks (MOFs) provide an expansive and tunable platform for heterogeneous catalysis, yet distinguishing between catalytic reactions occurring within their pores and those on their external surfaces remains a challenge. This study employs interpretable machine learning to elucidate structure‐activity relationships in MOF‐supported nickel (Ni) catalysts for selective sp 3 and sp 2 C─H borylation. By analyzing over 470 000 MOF structures, we developed a set of 45 concise and chemically meaningful descriptors that capture key structural variations across MOFs. These descriptors enabled us to identify the critical factors governing sp 3 versus sp 2 selectivity, revealing distinct activation mechanisms: sp 3 C─H borylation preferentially occurs within MOF cavities via a radical‐mediated hydrogen atom transfer (HAT) mechanism, whereas sp 2 C─H borylation is associated with surface or defect sites, favoring a concerted metalation‐deprotonation (CMD) pathway. Guided by these insights, we designed Ni catalysts that achieve up to 97.8% sp 3 selectivity and 88.7% sp 2 selectivity. This work provides a systematic framework for rational catalyst design and establishes generalizable principles for controlling activity preference in MOF‐supported catalysis. Borylation ; Catalysis ; Metal ; Metal-organic framework ; Chemistry ; Surface (topology) ; Photochemistry ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Boron Compounds in Chemistry
Authors: Xiaonan Liang | Shuai Liu | Huijun Zhang | Yike Liu | Yuhao Yan | Jie Qiu | Chen Fan
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning and Whale Optimization Algorithm for Enhanced Hydrogen Storage Alloy Design Whale ; Optimization algorithm ; Alloy ; Hydrogen storage ; Computer science ; Algorithm ; Artificial intelligence ; Materials science ; Mathematical optimization ; Fishery ; Metallurgy ; Mathematics ; Biology Hydrogen Storage and Materials ; Engineering Applied Research ; Nuclear Materials and Properties
Authors: Tianran Zhang
Source: Journal of Physics Conference Series (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Bandgap prediction of MXene materials via multiple machine learning approaches Abstract Multiple machine learning methods were employed to evaluate their effectiveness in predicting the bandgap of MXene materials. Three representative models—GBDT, LightGBM, and SVR—were systematically compared using performance metrics including MAE, RMSE, and R 2 . The results indicate that the GBDT model achieved the highest prediction accuracy, particularly excelling in MAE and R 2 . Further feature importance analysis revealed that the presence of a bandgap (IsGap) is the most critical factor influencing prediction, significantly outweighing other features. Additionally, structural and electronic descriptors such as lattice constant (LC) and valence band maximum (VBM) were also found to contribute to bandgap determination. These findings suggest that tuning semiconductor-related properties is an effective strategy for bandgap control in MXene materials. Overall, this work provides a robust, low-cost, and efficient machine learning-based framework to facilitate high-throughput screening and rational design of MXene materials. Band gap ; Valence band ; Lattice constant ; Lattice (music) ; Feature (linguistics) ; Work (physics) MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Minglei Zhang | Xiaoya Chen | Quanan Li | Zheng Wu | Shuhao An
Source: Journal of Materials Science (article, 2025-09-08)
Field: Materials Science — Biomaterials

Quantitative analysis of the microstructure of Mg-RE alloys based on deep learning models Microstructure ; Segmentation ; Phase (matter) ; Deep learning ; Solid mechanics ; Alloy ; Feature (linguistics) Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Aluminum Alloy Microstructure Properties
Authors: Wenyuan Zhang | Jason K. Pearson
Source: ACS Omega (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

The Dynamic Connection Layer (DCL): Enhancing Topological Representation in Chemical Graph Neural Networks In graphical-based chemistry machine learning (ML) models, a precise atomic correlation depiction, known as molecular topology, can significantly increase the model's performance. However, owing to the high acquisition cost, existing chemical graph data often use chemical bonds or atomic distances as molecular topology proxies. Such approximation, though practical, inevitably introduces inaccurate information that amplifies the learning difficulty of models and ultimately detracts from their performance. Despite various available data preprocessing techniques that can mitigate potential damage from inaccurate topology, those complex and data-type-tailored methods limit their generalizability in dealing with different types of chemical graph data. This paper introduces the dynamic connection layer (DCL) to address this challenge in a more general framework. This innovative graph neural layer dynamically modifies the input "topological information" (whether represented by chemical bonds or distances) to obtain a more accurate molecular topology description while preserving the generality as a trainable neural layer. We assessed the efficacy of the DCL layer using a data set derived from QM9 and demonstrated its efficiency in processing chemical graph data. Connection (principal bundle) ; Topology (electrical circuits) ; Graph ; Representation (politics) ; Layer (electronics) ; Computer science ; Artificial neural network ; Mathematics ; Theoretical computer science ; Artificial intelligence ; Materials science ; Nanotechnology ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Freederia AI Researcher
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-17)
Field: Chemistry — Analytical Chemistry

Automated Spectral Anomaly Detection and Classification in Polymer Degradation using Fourier-Transform Infrared Spectroscopy and Deep Learning **Abstract:** This paper details a novel system for real-time spectral anomaly detection and classification within Fourier-Transform Infrared (FT-IR) spectra of polymer degradation, specifically focusing on polyethylene (PE) utilizing a 10-layer convolutional neural network (CNN) architecture. Addressing the critical need for proactive polymer maintenance and quality control in industrial processes, our system leverages a multi-modal data ingestion and normalization layer, semantic decomposition, and a self-evaluating deep learning framework to identify and categorize subtle spectral shifts indicative of degradation pathways. This approach anticipates failure, reduces downtime, and enables precise control over polymer processing conditions, exhibiting a >95% accuracy rate compared to traditional manual spectral analysis. Immediate commercialization potential lies in inline quality control systems for polymer extrusion and molding, with a projected 15% reduction in material waste and a quantifiable increase in product lifespan. Convolutional neural network ; Deep learning ; Hyperspectral imaging ; Anomaly detection ; Normalization (sociology) ; Pattern recognition (psychology) ; Polymer ; Spectral signature ; Process control Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Alexander Gusev | Г. В. Маркова
Source: Tsvetnye Metally (article, 2025-03-30)
Field: Materials Science — General Materials Science

Neural network modeling of temperature dependences of elastic modulus of deformable aluminum alloys The elastic modulus (EM) of metals and alloys are the characteristics used in strength calculations of any products. Many parts and assemblies of machines and mechanisms operate at temperatures above room temperature, so it is necessary to have an understanding of the temperature dependences of elastic modulus (TDEM). In the construction sector, for elements of various civil structures made of aluminum alloys, information about the EM value becomes critically important for calculating the bearing capacity in the case of effects of elevated temperatures. However, data on EM at high temperatures are not provided for all aluminum alloys or calculated models of TDEM are presented, and those available in the reference literature are not always confirmed by experimental data. In this regard, it is necessary to adjust the models for individual alloys. The fundamental possibility of using neural networks to predict the elastic modulus of aluminum alloys in the temperature range from 20 to 400 оC has been established. Deformable aluminum alloys of 20 domestic brands and foreign brands of the 5xxx and 6xxx grades have been studied. The array of EM values of aluminum alloys at different temperatures is divided by the neural network into three samples: training, test and control in the proportions of 50&ndash;25&ndash;25% of the total data volume. After completing the training, the neural network evaluated the accuracy of reproducing the EM values, which amounted to 88, 85 and 90% for the three studied samples. The evaluation of the obtained predictive neural network model was performed on aluminum alloys 6061 and 5083, which were not included in the training sample. The type of the resulting model is a &ldquo;multilayer perceptron&rdquo;. The average prediction accuracy of the neural network model was more than 90%. Materials science ; Aluminium ; Elastic modulus ; Composite material ; Artificial neural network ; Modulus ; Computer science ; Artificial intelligence Material Properties and Applications ; Diverse Industrial Engineering Technologies ; Engineering Technology and Methodologies
Authors: Feng Chen | S. Li | Xin Chen | Dennis Wong | Bhaskar Sanyal | Duo Wang
Source: ArXiv.org (preprint, 2025-10-15)
Field: Materials Science — Materials Chemistry

Towards Universal Material Property Prediction with Deep Learning and Single-Descriptor electronic Density Owing to its high scalability and computational efficiency, machine learning methods have been increasingly integrated into various scientific research domains, including ab initio-based materials design. It has been demonstrated that, by incorporating modern machine learning algorithms, one can predict material properties with practically acceptable accuracy. However, one of the most significant limitations that restrict the widespread application of machine learning is its lack of transferability, as a given framework is typically applicable only to a specific property. The origin of this limitation is rooted in the fact that a material's properties are determined by multiple degrees of freedom -- and their complex interplay -- associated with nuclei and electrons, such as atomic type, structural symmetry, and the number and quantum states of the valence electrons, among others. The inherent complexity rules out the possibility of a single machine learning framework providing a full description of these critical quantities. In this paper, we develop a universal machine learning framework based solely on a physically grounded and theoretically rigorous descriptor -- electronic charge density. Our framework not only enables accurate prediction of eight different material properties (with R$^2$ values up to 0.94), but also demonstrates outstanding multi-task learning capability, as prediction accuracy improves when more target properties are incorporated into a single training process, thereby indicating excellent transferability. These results represent a significant step toward realizing the long-standing goal of a universal machine learning framework for the unified prediction of all material properties. Scalability ; Property (philosophy) ; Computational learning theory ; Deep learning ; Degrees of freedom (physics and chemistry) ; Valence (chemistry) ; Algorithmic learning theory Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Reuben Solomon | Oluwatosin Kayode
Source: FUDMA Journal of Sciences (article, 2025-08-14)
Field: Materials Science — Materials Chemistry

EMERGING STRATEGIES IN PHASE-TUNED PEROVSKITE MATERIALS FOR EFFICIENT HYDROGEN EVOLUTION: A MACHINE LEARNING PERSPECTIVE Phase-tuned perovskite materials have emerged as promising candidates for hydrogen production due to their excellent optoelectronic properties, flexible compositions, and structures.Phase engineering strategies, including strain induction, doping, and temperature-driven phase transformations, has significantly enhanced the tuning of bandgaps, charge transport, and catalytic activity.This review synthesizes current strategies for enhancing hydrogen evolution reactions (HER) efficiency in both oxide and halide perovskite systems through phase manipulation.The integration of machine learning (ML) into research accelerates the discovery of efficient HER catalysts by predicting optimal phase configurations, identifying novel compositions, and streamlining experimental efforts.Specifically, ML enhances phase tuning by analyzing vast compositional spaces, uncovering structure-property relationships, and guiding the design of phase-engineered perovskites with improved hydrogen evolution efficiency.Particular attention is directed towards AI-assisted studies on materials such as LaSrFeO and BaTiO, which demonstrate strong correlations between phase configuration and HER performance.This review uniquely bridges the gap between phase engineering and data-driven approaches, highlighting how ML augments rational design by reducing trial-and-error experimentation.This work also outlines key challenges, including phase stability, model interpretability, and data limitations while proposing future directions for the development of robust, scalable perovskite-based photocatalysts.The insights presented serve as a foundation for advancing clean and efficient hydrogen production technologies. Perspective (graphical) ; Perovskite (structure) ; Phase (matter) ; Materials science ; Nanotechnology ; Computer science ; Artificial intelligence ; Engineering ; Chemical engineering ; Physics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Muhammad Usman
Source: Nano Futures (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Towards machine learning assisted discovery of organic topological materials Abstract This perspective article describes our vision and proposal for the design and implementation of organic topological materials by using machine learning (ML) approach. We propose integrating advanced ML approaches with the state-of-the-art electronic simulation techniques to accelerate the discovery of organic topological materials which can lead to a new era of technologies in dissipationless nanoelectronics, solid-state spintronics, and quantum computing. Perspective (graphical) ; Topology (electrical circuits) ; Topological data analysis ; Deep learning ; Artificial neural network ; Quantum computer Machine Learning in Materials Science ; Topological Materials and Phenomena ; Covalent Organic Framework Applications
Authors: Kazuumi Fujioka (9564170) | Ryan Richard (22525626) | Jonathan Waldrop (22525629) | Yuheng Luo (3662062) | Theresa L. Windus (1425502) | Rui Sun (177521)
Source: Figshare (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

VENUSpy: A Chemical Dynamics Simulation Program inthe Era of Machine Learning and Exascale Computing The field of chemical reaction dynamics has evolved considerably since its inception, driven by advances in computational power and theoretical methodologies. While <i>ab initio</i> molecular dynamics (AIMD) simulations offer high accuracy by computing forces directly from electronic structure theory, its high computational cost limits its applicability to small systems and short time scales. Machine learning (ML) potentials and exascale computing offer new ways of developing potential energy surfaces (PESs) for chemical reaction dynamics simulations of longer times scales and larger systems. In this work, we introduce VENUSpy, a Python-based reimplementation and extension of the classical VENUS code, designed to interface with existing ML-based potentials and the exascale-ready quantum chemistry package NWChemEx. While NWChemEx is in development, VENUSpy demonstrates interfacing with its top-level classes and objects for use in reaction dynamics simulations. VENUSpy preserves the large library of initial sampling and classical trajectory propagation of the original, Fortran-based VENUS, while expanding its versatility through integration with modern Python tools. This modular framework enables ML/<i>ab initio</i> hybrid dynamics simulations, which offers distinct advantages to AIMD simulations and MLMD simulations, as well as fosters rapid development of new methodologies for studying complex reactive systems. Interfacing ; Exascale computing ; Modular design ; Chemical Dynamics ; Field (mathematics) ; Molecular dynamics ; Python (programming language) ; Interface (matter) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions