jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zongguo Wang | Ziyi Chen | Yuan Yang | Yangang Wang
Source: Inventions (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

CrySPAI: A New Crystal Structure Prediction Software Based on Artificial Intelligence Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most of these approaches are limited to predicting specific systems, which hinders their application to unknown or unexplored domains. In this paper, we present a crystal structure prediction software based on artificial intelligence, named as CrySPAI, to predict energetically stable crystal structures of inorganic materials given their chemical compositions. The software consists of three key modules, an evolutionary optimization algorithm (EOA) that searches for all possible crystal structure configurations, density functional theory (DFT) that provides the accurate energy values for these structures, and a deep neural network (DNN) that learns the relationship between crystal structures and their corresponding energies. To optimize the process across these modules, a distributed framework is implemented to parallelize tasks, and an automated workflow has been integrated into CrySPAI for seamless execution. This paper reports the development and implementation of the AI-based CrySPAI Crystal Prediction Software tool and its unique features. Crystal structure prediction ; Artificial intelligence ; Computer science ; Software ; Machine learning ; Crystal structure ; Crystallography ; Chemistry ; Operating system Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Rutu Patel | Mayankkumar L. Chaudhary | Ram K. Gupta
Source: Coordination Chemistry Reviews (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Data-driven and computational insights into borophene: Integrating machine learning with DFT and AI Borophene ; Density functional theory ; Workflow ; Bridging (networking) ; Molecular machine ; Boron Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Organoboron and organosilicon chemistry
Authors: Hafızur Rahman | Abid Hussain | Muhammad Ilyas | Lei Li | Khalid Waheed | Manzoor Ahmed
Source: Annals of Nuclear Energy (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Development of a Parametric Multi-Material Physics-Informed Neural Network for real-time heat transfer prediction in spherical tokamak divertor under transient heat loads Divertor ; Tokamak ; Transient (computer programming) ; Parametric statistics ; Heat flux ; Artificial neural network ; Heat transfer Fusion materials and technologies ; Magnetic confinement fusion research ; Nuclear reactor physics and engineering
Authors: Sojeong Yang | Se‐Hun Kim | Takuji Oda
Source: Acta Materialia (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Sticking, reflection, and abstraction behavior of hydrogen irradiated on (110) tungsten surfaces at 0.1-100 eV by molecular dynamics simulations using a machine learning potential Materials science ; Tungsten ; Reflection (computer programming) ; Molecular dynamics ; Irradiation ; Abstraction ; Hydrogen ; Dynamics (music) ; Chemical physics ; Metallurgy ; Computational chemistry ; Nuclear physics ; Computer science ; Physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear Physics and Applications
Authors: Yuanming Zhang | Yang Li | Yong Chen | Xiaoming Xu | Zhonghua Li | Tianxi Zhang | Wei Li Ong | Sergey M. Kozlov | Zhigang Zou | Ghim Wei Ho | Zhaosheng Li
Source: Materials Today (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Advances in metastable phase catalysis with thermodynamic-kinetic adaptability Metastability ; Phase diagram ; Phase (matter) ; Adaptability ; Schematic Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Arkaprava Banerjee | Kunal Roy
Source: Journal of Hazardous Materials (article, 2025-07-21)
Field: Chemistry — Analytical Chemistry

A new approach methodology (NAM) for carcinogenicity prediction of organic chemicals using the multiclass ARKA framework and machine-learning-based stacking regression Stacking ; Artificial intelligence ; Machine learning ; Regression ; Computer science ; Organic chemicals ; Regression analysis ; Data mining ; Chemistry ; Mathematics ; Statistics ; Environmental chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Fathya Y. M. Salih | Dinis O. Abranches | Edward J. Maginn | Yamil J. Colón
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Open-source generation of sigma profiles: impact of quantum chemistry and solvation treatment on machine learning performance The open-source generation of Sigma Profiles (SPs) using OpenSPGen was tuned to maximize machine learning performance and minimize computational cost. Open source ; Sigma ; Six Sigma ; Solvation ; Computer science ; Quantum ; Artificial intelligence ; Machine learning ; Quantum chemistry ; Chemistry ; Physics ; Quantum mechanics ; Molecule ; Chromatography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Ionic liquids properties and applications
Authors: Sunjay Sethi | Arnab Dutta | Emil I. Jaffal | Nishant Yadav | Danila Shiryaev | Brian Hoang | Anirudh Machathi | Sangjoon Lee | Karabi Das | Partha P. Jana | Anton O. Oliynyk
Source: Journal of the American Chemical Society (article, 2025-04-18)
Field: Materials Science — Materials Chemistry

Unsupervised Machine Learning Prediction of a Novel 1:3 Intermetallic Phase with the Synthesis of TbIr <sub>3</sub> (PuNi <sub>3</sub> -type) as Experimental Validation Crystal structure classification of binary intermetallic structures with 1:3 stoichiometry was done with machine learning algorithms. The data set included 97 features and a total of 2366 reported compounds adopting six different structure types. An unsupervised learning method based on principal component analysis (PCA) followed by clustering using the K-means method was applied to cluster compounds belonging to different structure types. With the recommendation engine, we predicted the expansion of the clusters and then identified the cluster/structure-type overlap. The PuNi<sub>3</sub>-type was among the clearly segregated structure types according to the unsupervised model, and a novel representative, TbIr<sub>3</sub>, was selected for experimental validation, adopting this structure. The final supervised machine learning predictions were done with partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and XGBoost, confidently predicting that the novel TbIr<sub>3</sub> belongs to the PuNi<sub>3</sub>-type with accuracies of 96.6, 99.8, and 99.9% respectively. Successful crystal structure segregation was attributed to a novel set of descriptors comprising both compositional and structural features. Given that the predicted PuNi<sub>3</sub>-type of the TbIr<sub>3</sub> phase could be controversial due to the extensive study of the Tb-Ir phase diagram and the reports of the TbIr<sub>3</sub> in two different structure types, we conducted two independent experimental structural validations to confirm the existence of TbIr<sub>3</sub> in the PuNi<sub>3</sub>-type structure. Subsequent theoretical validation explained that Ir-Ir contacts are the primary stability factor of TbIr<sub>3</sub> in the PuNi<sub>3</sub>-type structure compared to other structure types. Intermetallic ; Chemistry ; Phase (matter) ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Computer science Machine Learning in Materials Science ; Nuclear Materials and Properties ; Rare-earth and actinide compounds
Authors: Dennis Meier | E. Charles H. Sykes
Source: ACS Energy Letters (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

From Prediction to Performance: A Roadmap for Single-Atom Alloy Catalysts Single-atom alloy (SAA) catalysts bridge the precision of homogeneous catalysis and the practicality of heterogeneous systems. This Perspective highlights how theory-led approaches, particularly density functional theory and machine learning, enable their rational design. By shifting from combinatorial exploration to targeted prediction, researchers have identified alloy combinations with high activity, selectivity, and stability that would have not been predicted based on conventional catalytic wisdom. The unique geometric and electronic structures of SAAs break scaling relationships and enable bifunctional reactivity. Emerging areas such as dual-atom alloys and plasmon-enhanced and electrochemical catalysis are discussed. As the compositional phase space of bimetallic and trimetallic systems grows exponentially, theoretical guidance is essential to navigate this complexity efficiently. With surface science providing crucial mechanistic insights and AI accelerating screening, we outline a roadmap for predictive catalyst design and advocate for tighter integration between computation and experiment to address pressing challenges in energy and environmental catalysis. Bimetallic strip ; Catalysis ; Rational design ; Bifunctional ; Alloy ; Homogeneous ; Electrochemical energy storage ; Density functional theory ; Phase (matter) Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Chao Zhang
Source: Wear (article, 2025-01-28)
Field: Materials Science — Materials Chemistry

Multi-phase and multi-scale engine wear modeling via quantum chemical molecular dynamics and machine learning: A theoretical framework Materials science ; Discrete element method ; Plough ; Wear resistance ; Element (criminal law) ; Estimation ; Forensic engineering ; Resistance (ecology) ; Finite element method ; Composite material ; Metallurgy ; Structural engineering ; Mechanical engineering ; Engineering ; Mechanics ; Archaeology ; Systems engineering Metal Alloys Wear and Properties ; Tribology and Wear Analysis ; Tunneling and Rock Mechanics
Authors: Y. Liu | Milena Jovanović | Krishnanand Mallayya | Wesley J. Maddox | Andrew Gordon Wilson | Sebastian Klemenz | Leslie M. Schoop | Eun-Ah Kim
Source: Communications Materials (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Materials Expert-Artificial Intelligence for materials discovery Advances in materials databases create an opportunity to uncover descriptors that predict emergent properties, yet most studies rely on high-throughput ab initio calculations that can diverge from experiment. Experimentalists instead depend on intuition honed by hands-on work. We present “Materials Expert-Artificial Intelligence” (ME-AI), a machine-learning framework that translates this intuition into quantitative descriptors extracted from curated, measurement-based data. Using a set of 879 square-net compounds described using 12 experimental features, we train a Dirichlet-based Gaussian-process model with a chemistry-aware kernel. ME-AI reproduces established expert rules for spotting topological semimetals (TSMs) and reveals hypervalency as a decisive chemical lever in these systems. Remarkably, a model trained only on square-net TSM data correctly classifies topological insulators in rocksalt structures, demonstrating transferability. Complementing electronic-structure theory, our framework scales with growing databases, embeds expert knowledge, offers interpretable criteria, and guides targeted synthesis, accelerating materials discovery and rapid experimental validation across diverse chemical families. Material databases offer avenues for identifying predictive descriptors, yet often rely on data that diverges from experimental results. Here, machine learning was used to capture expert intuition into quantifiable descriptors, revealing hypervalency as a key predictor for topological semimetals. Intuition ; Topological data analysis ; Interpretability ; Experimental data ; Spotting ; Training set Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Shunsuke Mieda
Source: ACS Omega (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

Comparison of Computational Methods for Simulating Depolymerization Reaction A chemical recycling process that reduces polymers to their raw materials plays a crucial role in circular economy. To contribute to chemical recycling, this study proposes a system that simulates the process of depolymerization from polymer-to-monomer using reactive molecular dynamics (MD). Two MD methods, Reax force field (ReaxFF) and neural network potential (NNP), were employed to simulate the depolymerization of a polystyrene model. We validated the simulation accuracies by comparing monomer yields and decomposition products with experimental results. The results showed that NNP-MD accurately replicated the degradation and redecomposition processes and achieved consistency with the experimental data at various temperatures. ReaxFF-MD, however, was less able to represent the depolymerization process. We conclude that NNP-MD is capable of simulating polymer depolymerization results that are consistent with experimental observations. These results contribute to the development of methods for efficient chemical recycling and the broader realization of a circular economy. Depolymerization ; ReaxFF ; Polymer ; Polystyrene ; Molecular dynamics ; Monomer ; Force field (fiction) ; Materials science ; Biological system ; Process (computing) ; Chemical engineering ; Computational chemistry ; Biochemical engineering ; Process engineering ; Chemistry ; Computer science ; Polymer chemistry ; Engineering ; Composite material ; Artificial intelligence Machine Learning in Materials Science ; Recycling and Waste Management Techniques ; Polymer crystallization and properties
Authors: V. Samuel Raj | Manikantan R. Nair | Nurul Izza Soaid | Mohamed Shuaib Mohamed Saheed
Source: Diamond and Related Materials (article, 2025-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Benchmarking classical machine learning and neural networks for supercapacitor electrodes optimization Artificial neural network ; Random forest ; Support vector machine ; Mean squared error ; Convolutional neural network ; Decision tree ; Supercapacitor ; Extreme learning machine Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Nanoporous metals and alloys
Authors: Riya Singh | Aryan Amit Barsainyan | Rida Irfan | Connor Joseph Amorin | Stewart He | Tony Davis | Arun Thiagarajan | Shiva Sankaran | Seyone Chithrananda | Walid Ahmad | Derek Jones | Kevin McLoughlin | Hyojin Kim | Anoushka Bhutani | Sheela Sathyanarayana | Venkat Viswanathan | Jonathan Allen | Bharath Ramsundar
Source: ChemRxiv (preprint, 2025-08-03)
Field: Materials Science — Materials Chemistry

ChemBERTa-3: An Open Source Training Framework for Chemical Foundation Models The rapid advancement of machine learning in computational chemistry has opened new doors for designing molecules, predicting molecular properties, and discovering novel materials. However, building scalable and robust models for molecular property prediction remains a significant challenge due to the vast size and complexity of chemical space. In this paper, we introduce ChemBERTa-3, an open-source training framework designed to train and fine-tune large-scale chemical foundation models. We explore the potential of multiple model architectures by evaluating their performance across various molecular datasets from the MoleculeNet suite. Our experiments demonstrated that pre-training on the expansive ZINC20 dataset yields models capable of performing well on both classification and regression tasks, providing valuable insights into drug discovery and materials science. For scalability, we leveraged both AWS-based Ray deployments and on-premise high-performance computing clusters to support the processing power required to train on billions of molecules. In support of reproducible and extensible science, we have open-sourced all ChemBERTa3 models. Foundation (evidence) ; Training (meteorology) ; Open source ; Computer science ; Political science ; Geography ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Andrés Solé | Albert Mosella-Montoro | Joan Cardona | Silvia Gómez‐Coca | Daniel Aravena | Eliseo Ruíz | Javier Ruiz‐Hidalgo
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Cartesian encoding graph neural network for crystal structure property prediction: application to thermal ellipsoid estimation CartNet efficiently predicts anisotropic displacement parameters (ADPs) for molecular crystal structures, reducing computational costs and outperforming tested methods while achieving state-of-the-art accuracy across diverse datasets. Ellipsoid ; Cartesian coordinate system ; Property (philosophy) ; Artificial neural network ; Graph ; Computer science ; Encoding (memory) ; Algorithm ; Mathematics ; Artificial intelligence ; Theoretical computer science ; Geometry ; Geology ; Geodesy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Reshma Devi | Avaneesh Balasubramanian | Keith T. Butler | Gopalakrishnan Sai Gautam
Source: Scientific Data (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

A literature-derived dataset of migration barriers for quantifying ionic transport in battery materials The rate performance of any electrode or solid electrolyte material used in a battery is critically dependent on the migration barrier (E<sub>m</sub>) governing the motion of the intercalant ion, which is a difficult-to-estimate quantity both experimentally and computationally. The foundation for constructing and validating accurate machine learning (ML) models that are capable of predicting E<sub>m</sub>, and hence accelerating the discovery of novel electrodes and solid electrolytes, lies in the availability of high-quality dataset(s) containing E<sub>m</sub>. Addressing this critical requirement, we present a comprehensive dataset comprising 621 distinct literature-reported E<sub>m</sub> values calculated using density functional theory based nudged elastic band computations, across 443 compositions and 27 structural groups consisting of various compounds that have been explored as electrodes or solid electrolytes in batteries. Our dataset includes compositions corresponding to fully charged and/or discharged states of electrodes, with intermediate compositions incorporated in select instances. Crucially, for each compound, our dataset provides structural information, including the initial and final positions of the migrating ion, along with its corresponding E<sub>m</sub> in easy-to-use .xlsx and JSON formats. We envision our dataset to be highly useful for the scientific community, facilitating the development of advanced ML models that can predict E<sub>m</sub> precisely and accelerate materials discovery. Battery (electricity) ; Electrolyte ; Electrode ; Ionic bonding ; Battery capacity Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies
Authors: Chenyang Zhao | Yanbo Liu | Shi Tang | Jianmin Zhou | Songgu Wu | Junbo Gong
Source: Angewandte Chemie International Edition (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

CrystalGAT: An AI‐Based Intelligent Flexible Crystal Materials Design Computing Platform Endowing functional crystals with flexibility significantly broadens their potential applications in the field of flexible smart devices. Despite tremendous efforts to understand the foundational structural principles of flexibility, most discoveries of crystals with mechanical flexibility remain accidental. Currently, machine learning has become a transformative research paradigm in materials science. Here, we propose an innovative approach and develop a platform for predicting the mechanical properties of molecular crystals named CrystalGAT. CrystalGAT is a graph neural network model based on attention mechanisms, which constructs a robust optimal model through data augmentation strategies and achieves markedly superior prediction performance compared to established models. A high prediction accuracy of 90% is demonstrated on the validation set, alongside a promising generalization capability that extends to multicomponent systems. Most importantly, we have identified key segments influencing the mechanical properties of molecular crystals using CrystalGAT, successfully achieving a breakthrough in transforming brittle crystals into flexible photoresponsive crystals. Furthermore, it is possible to quickly screen for plastic multicomponent drug crystals, enhancing tableting performance. CrystalGAT provides an efficient method for flexible molecular crystal design, demonstrating its potential applications in material discovery and drug molecular modification. For user convenience, a dedicated website has been established: https://huggingface.co/spaces/ZZZCCCYYY/CrystalGAT. Flexibility (engineering) ; Tableting ; Crystal (programming language) ; Generalization ; Artificial neural network ; Key (lock) ; Brittleness ; Transformative learning Machine Learning in Materials Science ; Crystallography and molecular interactions ; Inorganic Chemistry and Materials
Authors: Cristiano Malica | Nicola Marzari
Source: npj Computational Materials (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Teaching oxidation states to neural networks Abstract While the accurate description of redox reactions remains a challenge for first-principles calculations, it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with strongly localized d or f electrons. Here, we first show that DFT+U+V molecular dynamics is capable of following the adiabatic evolution of oxidation states over time, using representative Li-ion cathode materials. In turn, this allows to develop redox-aware machine-learning potentials. We show that considering atoms with different oxidation states (as accurately predicted by DFT+U+V) as distinct species in the training leads to potentials that are able to identify the correct ground state and pattern of oxidation states for redox elements present. This can be achieved, e.g., through a systematic combinatorial search for the lowest-energy configuration or with stochastic methods. This brings the advantages of machine-learning potentials to key technological applications (e.g., rechargeable batteries), which require an accurate description of the evolution of redox states. Artificial neural network ; Computer science ; Psychology ; Artificial intelligence ; Mathematics education Machine Learning in Materials Science ; Electrochemical Analysis and Applications