jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xueshuang Deng | Jiaojiao Chen | Qi Luo
Source: Multiscale and Multidisciplinary Modeling Experiments and Design (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Prediction of ABX3 perovskite crystal structure based on random forest algorithm Random forest ; Stability (learning theory) ; Pooling ; Artificial neural network ; Convolutional neural network ; Feature (linguistics) ; Process (computing) ; Oversampling ; Convolution (computer science) Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Perovskite Materials and Applications
Authors: Li, Lucas | Puel, Jean-Baptiste | Carton, Florence | Barrit, Dounya | Giraldo, Jhony H.
Source: ArXiv.org (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Solar-GECO: Perovskite Solar Cell Property Prediction with Geometric-Aware Co-Attention Perovskite solar cells are promising candidates for next-generation photovoltaics. However, their performance as multi-scale devices is determined by complex interactions between their constituent layers. This creates a vast combinatorial space of possible materials and device architectures, making the conventional experimental-based screening process slow and expensive. Machine learning models try to address this problem, but they only focus on individual material properties or neglect the important geometric information of the perovskite crystal. To address this problem, we propose to predict perovskite solar cell power conversion efficiency with a geometric-aware co-attention (Solar-GECO) model. Solar-GECO combines a geometric graph neural network (GNN) - that directly encodes the atomic structure of the perovskite absorber - with language model embeddings that process the textual strings representing the chemical compounds of the transport layers and other device components. Solar-GECO also integrates a co-attention module to capture intra-layer dependencies and inter-layer interactions, while a probabilistic regression head predicts both power conversion efficiency (PCE) and its associated uncertainty. Solar-GECO achieves state-of-the-art performance, significantly outperforming several baselines, reducing the mean absolute error (MAE) for PCE prediction from 3.066 to 2.936 compared to semantic GNN (the previous state-of-the-art model). Solar-GECO demonstrates that integrating geometric and textual information provides a more powerful and accurate framework for PCE prediction. Perovskite (structure) ; Probabilistic logic ; Energy conversion efficiency ; Process (computing) ; Property (philosophy) ; Convolutional neural network ; Focus (optics) ; Perovskite solar cell Machine Learning in Materials Science ; Perovskite Materials and Applications ; Data Visualization and Analytics
Authors: Zhe Zhang | Huixin Zhan | Tinghui Wu | Robert Burns | Jasreet Hundal | Helio A. Costa
Source: Annual Review of Biomedical Data Science (review, 2025-08-11)
Field: Materials Science — Materials Chemistry

The Expanding Landscape of Neural Architectures and Their Impact in Biomedicine Deep learning and artificial intelligence (AI) have seen explosive growth and success in biomedical applications in the last decade, largely due to the rapid development of deep neural networks and their underlying neural network (NN) architectures. Here, we explore biomedical deep learning and AI from the specific perspective of NN architectures. We discuss widely varying design principles of NN architectures, their use in particular biomedical applications, and the assumptions (often hidden) built into them. We explore neural architecture search techniques that automate the design of NN topology to optimize task performance. Advanced neural architectures are being developed for both molecular and healthcare applications, employing elements of graph networks, transformers, and interpretable NNs, and we discuss and summarize the design considerations and unique advantages of each architecture. Future advances will include the employment of multimodal language models and smaller highly focused mechanistic models that build on the success of today's large models. Computer science ; Deep learning ; Artificial intelligence ; Biomedicine ; Artificial neural network ; Architecture ; Deep neural networks ; Transformer ; Machine learning ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Timothy
J. Giese (1311030) | Jinzhe Zeng (9852355) | Darrin M. York (58145)
Source: Figshare (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

Transferability of MACE Graph Neural Network for RangeCorrected Δ‑Machine Learning Potential QM/MM Applications We previously introduced a “range corrected” Δ−machine learning potential (ΔMLP) that used deep neural networks to improve the accuracy of combined quantum mechanical/molecular mechanical (QM/MM) simulations by correcting both the internal QM and QM/MM interaction energies and forces [J. Chem. Theory Comput. 2021, 17, 6993–7009]. The present work extends this approach to include graph neural networks. Specifically, the approach is applied to the MACE message passing neural network architecture, and a series of AM1/d + MACE models are trained to reproduce PBE0/6–31G* QM/MM energies and forces of model phosphoryl transesterification reactions. Several models are designed to test the transferability of AM1/d + MACE by varying the amount of training data and calculating free energy surfaces of reactions that were not included in the parameter refinement. The transferability is compared to AM1/d + DP models that use the DeepPot-SE (DP) deep neural network architecture. The AM1/d + MACE models are found to reproduce the target free energy surfaces even in instances where the AM1/d + DP models exhibit inaccuracies. We train “end-state” models that include data only from the reactant and product states of the 6 reactions. Unlike the uncorrected AM1/d profiles, the AM1/d + MACE method correctly reproduces a stable pentacoordinated phosphorus intermediate even though the training did not include structures with a similar bonding pattern. Furthermore, the message passing mechanism hyperparameters defining the MACE network are varied to explore their effect on the model’s accuracy and performance. The AM1/d + MACE simulations are 28% slower than AM1/d QM/MM when the ΔMLP correction is performed on a graphics processing unit. Our results suggest that the MACE architecture may lead to ΔMLP models with improved transferability. Mace ; Artificial neural network ; Transferability ; Graph ; Range (aeronautics) ; Training set Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Xiaolu Chen | Yidong Zhao | Zihe Cao | Youqian Sun | Wenming Zhang | Yi Yang | Bing Zhang | Ji Liu | Zhenxi Zhang | Qiang Lu
Source: Organic Electronics (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Machine learning-driven prediction of energy and band gap in FAPbI3 perovskite using diverse structural descriptors Random forest ; Robustness (evolution) ; Stability (learning theory) ; Band gap ; Perovskite (structure) ; Artificial neural network ; Generalization ; Energy (signal processing) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Adam Imel
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Chemistry — Organic Chemistry

<i>(Invited)</i> The Role of Microemulsions in Advanced Energy Storage Microemulsions represent a transformative approach to developing non-traditional electrolytes for electrochemical devices, particularly redox flow batteries (RFBs). These advanced electrolytes uniquely decouple energy density from solution conductivity by leveraging nanophase separation of oil and water domains, all while producing a macroscopic homogeneous, thermodynamic stable solution. Microemulsions offer a safer alternative to conventional non-aqueous electrolytes, such as those based on acetonitrile, by reducing flammability and incorporating environmentally friendly, less hazardous solvents. This dual benefit of enhanced safety and sustainability makes microemulsions a compelling solution for next-generation energy storage technologies. Our research employs experimental and computational methods to investigate the fundamental chemical properties, structural organization, and electrochemical performance of microemulsions. By understanding and optimizing these parameters, we aim to advance the design, functionality, and scalability of these systems, paving the way for their widespread adoption in energy storage applications. This presentation will explore our studies on electrochemical transport, diffusion, and machine learning that provide insights into electrolyte equilibria and structural organization, revealing their impact on electrochemical accessibility and performance. These findings demonstrate the potential of microemulsion electrolytes to integrate into energy storage technologies, addressing both performance limitations and environmental concerns. Microemulsion ; Energy storage ; Nanotechnology ; Materials science ; Process engineering ; Chemistry ; Computer science ; Environmental science ; Chemical engineering ; Physics ; Engineering ; Thermodynamics ; Pulmonary surfactant Surfactants and Colloidal Systems ; Fluid Dynamics and Thin Films
Authors: YURII KOVALOV | Віктор Аулін | Serhii Kovalov | Oleksandr Kuzyk | Andriy Hrynkiv
Source: Bulletin of Kharkov National Automobile and Highway University (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

DIGITALIZATION OF MATERIALS SCIENCE AND DIRECTIONS FOR THE APPLICATION OF ARTIFICIAL INTELLIGENCE Problem. The growing complexity of materials design, the expansion of high-dimensional compositional spaces, and the demand for sustainable innovation have exposed the limitations of traditional experimental approaches in materials science. These methods are often time-consuming, resource-intensive, and insufficiently scalable to meet the challenges of modern research and development. Goal. The aim of this study is to identify current directions in the development of materials science under conditions of digital transformation, with a particular focus on the application of artificial intelligence (AI) methods to enhance scientific approaches, model material properties, and support sustainability principles. Methodology. The research involves a com- parative analysis of leading digital platforms– Materials Project, NOMAD Repository, and Citrine Informatics–that integrate machine learning algo- rithms, simulation tools, and data management sys- tems. Case studies from recent publications (2021– 2025) are used to illustrate practical applications of AI in energy materials, polymers, and alloys. Results. Key areas of AI application in materials science have been identified, including virtual screening, active learning, interpretable modeling, and automated composition optimization. The study evaluates the prospects of digital transformation in the field, particularly in the context of Ukraine’s international integration and alignment with sustainable develop- ment goals. Originality. The paper substantiates the strategic role of materials informatics as a new research paradigm that combines AI, computational methods, and open science. It proposes approaches for integrating Ukrainian materials data into global platforms and outlines a roadmap for national infrastructure development. Practical Value. The findings can inform the creation of interdisciplinary educational programs, the development of a national materials informatics ecosystem, and the implementation of AI-driven innovation in critical sectors such as energy, medicine, defense, and environmental technologies. Context (archaeology) ; Sustainability ; Digital transformation ; Big data ; Grand Challenges ; Informatics ; Sustainable development ; Key (lock) Machine Learning in Materials Science ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Material Properties and Failure Mechanisms
Authors: Yinghao Li (2850890) | Jing Lyu (2795911) | Wenxin Wang (214807)
Source: Figshare (article, 2025-09-11)
Field: Materials Science — Biomaterials

Cyclizationin Linear Step-Growth Polymerizations Intramolecular cyclization is a pervasive yet often ignored factor in step-growth polymerizations (SGPs), particularly under dilute conditions. While experimental studies have confirmed the significant impact of concentration on cyclization, the lack of deep theoretical understanding has limited the ability to guide reaction design and predict the polymer structure. In this work, we adopt a reverse-engineering strategy to extract cyclization-related equations from experimental data using a classical A2 + B2 step-growth polymerization system. By combining analytical derivations with symbolic regression, a machine learning technique for generating closed-form expressions, we obtain explicit formulas for cyclization probability, degree of cyclization, degree of linear polymerization, and molecular weights as functions of monomer conversion and reaction concentration. These expressions capture the dynamic nature of cyclization and demonstrate excellent agreement with experimental results across a broad concentration range. Our work provides a new quantitative framework to incorporate cyclization into SGP theory and offers practical tools for predicting molecular structures and properties under real-world conditions. Monomer ; Polymerization ; Work (physics) ; Polymer ; Degree (music) ; Set (abstract data type) biodegradable polymer synthesis and properties ; Organometallic Complex Synthesis and Catalysis ; Advanced Polymer Synthesis and Characterization
Authors: Rishabh D. Guha | Santiago Vargas | Evan Walter Clark Spotte‐Smith | Alexander R. Epstein | Maxwell C. Venetos | Ryan Kingsbury | Mingjian Wen | Samuel M. Blau | Kristin A. Persson
Source: Journal of Chemical Information and Modeling (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water's remarkable versatility as a solvent. However, accurately predicting the feasibility of hydrolysis through computational techniques is a difficult task, as subtle changes in reactant structure like heteroatom substitutions or neighboring functional groups can influence the reaction outcome. Furthermore, hydrolysis is sensitive to the pH of the aqueous medium, and the same reaction can have different reaction properties at different pH conditions. In this work, we have combined reaction templates and high-throughput ab initio calculations to construct a diverse data set of hydrolysis free energies. The developed framework automatically identifies reaction centers, generates hydrolysis products, and utilizes a trained graph neural network (GNN) model to predict Δ<i>G</i> values for all potential hydrolysis reactions in a given molecule. The long-term goal of the work is to develop a data-driven, computational tool for high-throughput screening of pH-specific hydrolytic stability and the rapid prediction of reaction products, which can then be applied in a wide array of applications including chemical recycling of polymers and ion-conducting membranes for clean energy generation and storage. Hydrolysis ; Graph ; Artificial neural network ; Chemistry ; Computer science ; Biological system ; Artificial intelligence ; Organic chemistry ; Theoretical computer science ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Yinghao Li | Jing Lyu | Wenxin Wang
Source: Macromolecules (article, 2025-09-11)
Field: Chemistry — Organic Chemistry

Cyclization in Linear Step-Growth Polymerizations Intramolecular cyclization is a pervasive yet often ignored factor in step-growth polymerizations (SGPs), particularly under dilute conditions. While experimental studies have confirmed the significant impact of concentration on cyclization, the lack of deep theoretical understanding has limited the ability to guide reaction design and predict the polymer structure. In this work, we adopt a reverse-engineering strategy to extract cyclization-related equations from experimental data using a classical A2 + B2 step-growth polymerization system. By combining analytical derivations with symbolic regression, a machine learning technique for generating closed-form expressions, we obtain explicit formulas for cyclization probability, degree of cyclization, degree of linear polymerization, and molecular weights as functions of monomer conversion and reaction concentration. These expressions capture the dynamic nature of cyclization and demonstrate excellent agreement with experimental results across a broad concentration range. Our work provides a new quantitative framework to incorporate cyclization into SGP theory and offers practical tools for predicting molecular structures and properties under real-world conditions. Monomer ; Polymerization ; Work (physics) ; Polymer ; Intramolecular force ; Degree (music) ; Reaction mechanism Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Polymer crystallization and properties
Authors: Seong-Cheol Ryu | Kyung-Young Jhang
Source: Journal of the Korean Society for Nondestructive Testing (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Estimation of Aluminum Alloy Strength using Nondestructive Testing Data: Introducing σ-Range-Based Data Augmentation Nondestructive testing ; Alloy ; Aluminium ; Materials testing ; Destructive testing Machine Learning in Materials Science ; Ultrasonics and Acoustic Wave Propagation ; Structural Health Monitoring Techniques
Authors: Mona Amrihesari | Blair Brettmann
Source: Journal of Chemical Education (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

Introducing Data-Driven Materials Informatics into Undergraduate Courses through a Polymer Science Workshop With the rapid growth of artificial intelligence and machine learning across scientific disciplines from materials discovery to data-driven problem solving, there is increasing opportunity to integrate these tools into a broad range of applications. Successful adoption of these approaches in research can be enhanced by foundational exposure during undergraduate education. The objective of this study is to introduce fundamental machine learning concepts to undergraduate students through a hands-on, application-focused workshop during a polymer science and engineering course. Students were guided through key steps of the machine learning workflow, including data cleaning, model training, performance evaluation, and result interpretation, using a polymer solubility data set generated via visual inspection. The effectiveness of the workshop was assessed through pre- and postworkshop student surveys, which indicated a measurable improvement in students' understanding and confidence in applying machine learning techniques. The integration of this workshop into a materials course introduces the students to the new concepts while extending the application of the course material. Informatics ; Engineering physics ; Mathematics education ; Data science ; Computer science ; Engineering ethics ; Engineering ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Hani M. Hashim (22440318) | Jeremy N. Harvey (1273875)
Source: Figshare (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

ConformationalAnalysis of Macrocyclic Compounds Usinga Machine-Learned Interatomic Potential Macrocyclic compounds play a vital role in many chemical and biological systems, yet their conformational analysis remains a significant challenge. In this work, we investigate the conformational landscape of macrocyclic compounds using a machine-learned interatomic potential (MLIP) based on a Nequip-like graph neural network. This MLIP is trained on the energy differences between ωB97XD3 and GFN1-xTB. The model not only reproduces the DFT relative conformer energies of the macrocycles with high fidelity but also yields optimized structures that are practically identical to those obtained via density functional theory. Furthermore, when integrated into a metadynamics-based conformational sampling framework (CREST), we recover structures that very closely match the structure obtained after gas-phase optimization with DFT starting from the crystal structure. These results underscore the potential of machine learning to overcome longstanding challenges in the conformational analysis of complex macrocyclic systems. Conformational isomerism ; Conformational ensembles ; Molecular conformation ; Molecular dynamics ; Density functional theory ; Crystal structure ; Energy minimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Shuaike Shen | Ke Liu | Muzhi Zhu | Hao Chen
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

A Denoising Pre-training Framework for Accelerating Novel Material Discovery Crystal materials play an important role in the development of society. The discovery of new materials is critical to achieving sustainable development goals (SDGs), such as climate change mitigation, affordable and clean energy, and fostering innovation in industry and infrastructure. Recent advances in deep learning for crystal property prediction have accelerated material discovery, but these methods typically rely on labeled data, which is often limited and varies across different properties. This limitation hinders the full utilization of the vast amount of unlabeled data in materials science. To overcome this challenge, we introduce an unsupervised Denoising Pre-training Framework (DPF) tailored for crystal structures. DPF trains a model to reconstruct the original crystal structure by recovering the masked atom types, perturbed atom positions, and perturbed crystal lattices. Through pre-training, models learn the intrinsic features of crystal structures and capture the key features influencing crystal properties. We pre-train models on a dataset of 380,743 unlabeled crystal structures and fine-tune them on downstream property prediction tasks. Extensive experiments demonstrate the effectiveness of our framework, showing its potential to significantly advance material science and contribute to the development of society by accelerating the discovery of materials crucial for sustainable technologies. Training (meteorology) ; Noise reduction ; Computer science ; Artificial intelligence ; Geography ; Meteorology Machine Learning in Materials Science ; Geochemistry and Geologic Mapping