jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kansei Kanayama | Kazuaki Toyoura
Source: Journal of Physics Condensed Matter (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

Quantitative evaluation of nuclear quantum effects on the phase transitions in BaTiO<sub>3</sub> using large-scale molecular dynamics simulations based on machine learning potentials The machine learning potential (MLP) based molecular dynamics (MD) method (MLPMD) was applied for constructing the pressure-temperature phase diagram in the barium titanate (BaTiO<sub>3</sub>) crystals. The nuclear quantum effects (NQEs) on the phase transitions were quantitatively evaluated from the difference in the phase transition pressures between the NQEs-incorporated and classical simulations. In this study, the quantum thermal bath (QTB) method was used for incorporating the NQEs. The constructed phase diagrams verified that the NQEs lower the phase transition temperatures and pressures. The NQEs are more significant at lower temperatures but cannot be ignored even at room temperature. The phase diagram in the QTB-based MLPMD is in good agreement with those of the previous studies based on dielectric measurements and path-integral based simulations. The displacement distributions of Ti and O ions in the QTB-MLPMD suggest that the pressure-induced tetragonal-cubic phase transition is the displacive type, in contrast to the order-disorder type reported in the literature. Possible reasons for the discrepancy in the microscopic behavior are the differences in the simulation cell size and restriction for lattice dynamics. In contrast to the relatively small simulation cell (12 × 12 × 12 supercell or smaller) with some restriction to the degrees of freedom (DOFs) for lattice dynamics in the previous studies, the large cell (20 × 20 × 20 supercell) without any DOF restriction was employed in the present study. Phase diagram ; Molecular dynamics ; Phase transition ; Phase (matter) ; Quantum ; Barium titanate ; Quantum phase transition ; Condensed matter physics ; Dielectric ; Thermal ; Materials science ; Chemistry ; Thermodynamics ; Physics ; Quantum mechanics ; Computational chemistry Machine Learning in Materials Science ; High-pressure geophysics and materials ; Quantum, superfluid, helium dynamics
Authors: Costantino Zazza | Nico Sanna | Stefano Borocci | Felice Grandinetti
Source: ChemistryOpen (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

Perspective on Theoretical Modeling of Soft Molecular Machines and Devices: A Fusion of Data‐Driven Approaches with Traditional Computational Chemistry Algorithms The design of complex molecular machines and devices represents one of the most ambitious frontiers in nanotechnology, synthetic chemistry, and molecular engineering. These intricate systems, inspired by biological machines, require precise control over atomic and electronic interactions to achieve desired functionalities. Theoretical modeling plays a crucial role in this process, offering predictive insights into molecular behavior, guiding experimental design, and optimizing performance. Methods such as density functional theory, quantum theory of atoms in molecules coupled with widely adopted and distinctive visualization methods, molecular dynamics simulations, and quantum mechanical/molecular mechanical hybrid approaches provide analytical information into the stability in terms of mutual chemical interactions and conformational shaping of flexible supramolecular aggregates for nanotechnological applications. Theoretical approaches also facilitate interdisciplinary integration, bridging chemistry, physics, and materials science to create conceptually hybrid devices with enhanced performance. Machine learning and artificial intelligence are now being incorporated into theoretical modeling, accelerating the discovery and refinement of novel molecular architectures. This fusion of data-driven approaches with traditional computational chemistry algorithms is expected to revolutionize the design paradigm of soft molecular machines and devices. Perspective (graphical) ; Computer science ; Fusion ; Algorithm ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes ; Mass Spectrometry Techniques and Applications
Authors: Soumya Mondal | Subhanu Halder | D. Basu | Sandeep Kumar | Tarak Karmakar
Source: ArXiv.org (preprint, 2025-07-22)
Field: Materials Science — Materials Chemistry

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Coarse-grained (CG) molecular dynamics (MD) simulations can simulate large molecular complexes over extended timescales by reducing degrees of freedom. A critical step in CG modeling is the selection of the CG mapping algorithm, which directly influences both accuracy and interpretability of the model. Despite progress, the optimal strategy for coarse-graining remains a challenging task, highlighting the necessity for a comprehensive theoretical framework. In this work, we present a graph-based coarsening approach to develop CG models. Coarse-grained sites are obtained through edge contractions, where nodes are merged based on a local variational cost metric while preserving key spectral properties of the original graph. Furthermore, we illustrate how Message Passing Atomic Cluster Expansion (MACE) can be applied to generate ML-CG potentials that are not only highly efficient but also accurate. Our approach provides a bottom-up, theoretically grounded computational method for the development of systematically improvable CG potentials. Interpretability ; Metric (unit) ; Key (lock) ; Molecular dynamics ; Selection (genetic algorithm) ; Enhanced Data Rates for GSM Evolution ; Cluster (spacecraft) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Yu‐Chen Yeh | Janghoon Ock | Chandrasekhar, Achuth | Maheshwari, Shagun | Farimani, Amir Barati
Source: arXiv (Cornell University) (preprint, 2025-01-07)
Field: Materials Science — Materials Chemistry

Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap Prediction We investigate transformer-based language models, including RoBERTa, T5, Llama-3, and MatSciBERT, for predicting the band gaps of semiconductor materials directly from textual descriptions. The inputs encode key material features, such as chemical composition, crystal system, space group, and other structural and electronic properties. Unlike shallow machine learning models, which require extensive feature engineering, or Graph Neural Networks, which rely on graph representations derived from atomic coordinates, pretrained language models can process textual inputs directly, eliminating the need for manual feature preprocessing or structure-based encoding. Material descriptions were constructed in two formats: structured strings with a consistent template and natural language narratives generated via the ChatGPT API. Each model was augmented with a custom regression head and finetuned for band gap prediction task. Language models of different architectures and parameter sizes were all able to predict band gaps from human-readable text with strong accuracy, achieving MAEs in the range of 0.25-0.33 eV, highlighting the success of this approach for scientific regression tasks. Finetuned Llama-3, with 1.2 billion parameters, achieved the highest accuracy (MAE 0.248 eV, R2 0.891). MatSciBERT, pretrained on materials science literature, reached comparable performance (MAE 0.288 eV, R2 0.871) with significantly fewer parameters (110 million), emphasizing the importance of domain-specific pretraining. Attention analysis shows that both models selectively focus on compositional and spin-related features while de-emphasizing geometric features, reflecting the difficulty of capturing spatial information from text. These results establish that pretrained language models can effectively extract complex feature-property relationships from textual material descriptions. Encoder ; Semiconductor ; Band gap ; Computer science ; Artificial intelligence ; Natural language processing ; Optoelectronics ; Psychology ; Materials science Machine Learning in Materials Science
Authors: Solyeman Khajeh | Arian Mayelifartash | Ahmad Ranjbar | Mohammad Ali Abdol | Sadegh Sadeghzadeh | Gianaurelio Cuniberti | Thomas D. Kühne | H. Kaveh
Source: Solid State Communications (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Efficient hybrid machine learning model for inverse design of porous boron nitride with high thermal conductivity Boron nitride ; Thermal conductivity ; Inverse ; Porosity ; Boron ; Materials science ; Composite material ; Thermal ; Chemical engineering ; Chemistry ; Mathematics ; Physics ; Engineering ; Thermodynamics Thermal properties of materials ; Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design
Authors: Bharat Khushalani
Source: International Journal of Materials Science and Applications (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Development of a Machine Learning Model for Predicting the Structural and Optical Properties of Nanomaterials Based on Quantum-Mechanical Simulations The rapid advancement of nanotechnology has enabled the development of materials with unique properties that differ significantly from their bulk counterparts. Understanding and predicting the properties of nanomaterials, such as their electronic, optical, and mechanical characteristics, is crucial for their application in fields like electronics, energy storage, and catalysis. However, the computational methods used to predict these properties, particularly through quantum mechanical simulations such as Density Functional Theory (DFT), are computationally expensive and time-consuming, especially when applied to large datasets of nanomaterials. This paper proposes a novel approach that integrates machine learning (ML) techniques with DFT simulations to predict the structural and optical properties of nanomaterials. By utilizing a dataset derived from DFT calculations, we train and evaluate multiple machine learning models, including Random Forest, Support Vector Machine (SVM), and Deep Neural Networks (DNN), to predict key properties such as band gap, conductivity, and optical absorption. The goal is to develop a model that reduces the computational burden of traditional simulation methods while maintaining high accuracy and generalizability. The models were trained on a synthetic dataset that simulates the composition, size, and crystal structure of nanomaterials, with target properties generated based on these features. We evaluated the performance of the models using standard regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and &amp;lt;i&amp;gt;R&amp;lt;/i&amp;gt;&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;. Results show that the DNN model provides the best predictive accuracy, closely followed by the Random Forest model, while the SVM model demonstrated lower performance in this context. Additionally, feature importance analysis revealed that material composition, particle size, and crystal structure were the most influential factors in determining the predicted properties of the nanomaterials. This research demonstrates the potential of machine learning to accelerate the discovery of new nanomaterials by providing a fast and scalable way to predict their properties. By combining the predictive power of ML with quantum mechanical simulations, this study offers an efficient framework for material discovery that can be applied to a wide range of nanomaterial systems. Materials science ; Nanomaterials ; Quantum ; Nanotechnology ; Quantum dot ; Composite material Machine Learning in Materials Science ; Advanced Materials and Semiconductor Technologies ; Computational Drug Discovery Methods
Authors: Xin Wang | Bingjie Xiao | Bo Zhang | Rongyu Sun | Cui Li | Peter K. Liaw
Source: ACS Materials Letters (review, 2025-07-14)
Field: Materials Science — Materials Chemistry

Deep Learning Applications in the Analysis of Wear Mechanisms in Metallic Materials: A Review The accurate diagnosis and prediction of metal wear mechanisms are critical for ensuring the durability and safety of industrial systems. Conventional tribological methods, although widely used, are limited in processing complex data sets, identifying early stage wear features, and enabling real-time monitoring. Recently, deep learning has shown strong potential to address these challenges through automated feature extraction, better temporal prediction, and more accurate anomaly detection. Particularly, models such as the Convolutional neural network, Long Short-Term Memory network, and Autoencoder have been successfully applied to image-based surface analysis, vibration and acoustic signal interpretation, and wear progression forecasting. Advanced hybrid architectures and cross-domain transfer techniques further extend the applicability of deep learning to multimodal wear analysis under diverse operating conditions. This Review systematically explores the current advancements in deep learning-driven wear analysis, critically assessing model capabilities, data dependencies, and physical interpretability, and outlines future directions for integrating AI systems into tribological research and industrial diagnostics. Materials science ; Nanotechnology Metal Alloys Wear and Properties ; Gear and Bearing Dynamics Analysis ; Lubricants and Their Additives
Authors: Mohammad Anwar Parvez | Ibrahim M. Mehedi
Source: Polymers (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

Data-Driven Polymer Classification Using BiGRU and Hybrid Metaheuristic Optimization Algorithms Polymers characterize a different and important class of materials through various industries, all with unique functional properties and structural attributes. Conventional models of polymer classification depend greatly on labor-intensive methods liable to human error and subjectivity. Hence, a continually growing requirement for new polymers with greater properties is a deep understanding and exploration of the chemical space. Hence, data-driven methods for polymers are developing and able to deal with unique challenges originating from the outstanding physical and chemical range of polymers at smaller and larger scales. Recently, Deep Learning (DL) models have considerably transformed material science by allowing for the automatic study and classification of composite polymers. In this paper, a novel optimization algorithm with a DL-Based Neural Networks for Data-Driven Polymer Classification (OADLNN-DDPC) model is proposed. The main intention of the OADLNN-DDPC model is to improve the classification model for data-driven polymers using state-of-the-art optimization algorithms. The data normalization stage is initially executed via Z-score normalization to convert input data into a beneficial format. In addition, the proposed OADLNN-DDPC model implements the bald eagle search (BES) model for feature selection to detect and retain the most appropriate features. For the polymer classification process, the bidirectional gated recurrent unit (BiGRU) technique is employed. Lastly, the zebra optimizer algorithm (ZOA) is implemented for the tuning process. Extensive experiments conducted on a polymers dataset with 19,500 records and 2048 features demonstrated that OADLNN-DDPC achieves an accuracy of 98.58%, outperforming existing models, such as LSTM (83.37%), PLS-DA (88.18%), and K-NN (98.36%). The simulation process of the OADLNN-DDPC model is performed under the polymer classification dataset. The experimental analysis specified that the OADLNN-DDPC model demonstrated improvement over another existing model. Normalization (sociology) ; Algorithm ; Computer science ; Feature selection ; Polymer ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Davita McTush Camp
Source: ACS Omega (article, 2025-12-18)
Field: Chemistry — Physical and Theoretical Chemistry

Bridging Organic Chemistry Teaching Laboratories and Real-World Research with Modern Computing and Spectroscopic Techniques The Reimagining Organic Chemistry Laboratories in Education Systems (ROLES) initiative at Spelman College was established to provide undergraduate students with synthetic, analytical, computing, and critical-thinking skills necessary for training innovators and problem solvers for future STEM careers. Embedded in the second-semester organic chemistry laboratory, the initiative revamps the course to take on a research-based structure that incorporates purposeful laboratory activities that illustrate the alignment of chemical concepts introduced in teaching laboratories with techniques utilized in the chemical workforce. The ROLES research-based instruction features student-relatable modules of drug discovery, polymer-cosmetic chemistry, and polymer-material science all with real-world applications. Each module is accompanied by computational approaches including machine learning in Artificial Intelligence (AI), electronic Density Functional Theory (DFT) calculations, and docking molecular-modeling studies. The laboratories also utilize instrumentation including NMR, FTIR, GC, and MS spectroscopies. In all, the ROLES initiative provides a transformative blueprint through the chemistry curriculum to actively engage students and deliver connections between chemical theory and everyday life. The intent of this perspective is to describe how real-life applications and the use of modern technology, and instrumentation can be folded into an introductory chemistry lab. This perspective will focus on the logistics of the new curriculum, computational methodologies used, and how spectral analysis is applied throughout the program. Bridging (networking) ; Blueprint ; Curriculum ; Transformative learning ; Instrumentation (computer programming) ; Perspective (graphical) Various Chemistry Research Topics ; Machine Learning in Materials Science ; Chemistry and Chemical Engineering
Authors: Caroline Desgranges | Jérôme Delhommelle
Source: Communications Chemistry (review, 2025-09-26)
Field: Materials Science — Materials Chemistry

Deciphering the complexities of crystalline state(s) with molecular simulations Predicting the outcome of a crystallization process remains a long-standing challenge in solid state chemistry. This stems from the subtle interplay between thermodynamics and kinetics that results in a complex crystal energy landscape, spanned by many polymorphs and other metastable intermediates. Molecular simulations are uniquely positioned to unravel this interplay, as they constitute a framework that can compute free energies (thermodynamics), barriers (kinetics), and visualize the crystallization mechanisms at high resolution. We show here how recent progress in computational methods, and their augmentation with Machine Learning, has advanced our ability to predict crystal structure and simulate crystal nucleation. Metastability ; Crystal structure prediction ; Crystallization ; Crystal (programming language) ; Molecular dynamics ; Process (computing) ; Kinetics Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Churna Bhandari | Gavin Nop | Hari Paudyal | T. A. Lograsso | Durga Paudyal
Source: Physical Review Materials (article, 2025-03-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing stability, magnetic anisotropy, and coercivity of manganese aluminum: Machine learning, <i>ab initio</i>, and micromagnetic modeling The binary manganese aluminum (MnAl) alloy with $\mathrm{L}{1}_{0}$ crystal structure is a promising rare earth (RE) element-free permanent magnetic material because of its exceptional magnetic properties. However, experimentally synthesizing it in a stable bulk form is extremely challenging. Here, an alternative method of stabilizing the material, a pathway for experimental synthesis and validation, is proposed and theoretically verified. This is done by partially substituting Mn and Al sites with Fe and Ni and identifying its enhanced phase stability, saturation magnetization density, magnetic anisotropy, and coercivity from density functional theory (DFT), machine learning (ML) crystal graph convolution neural network (CGCNN), and micro-magnetic modeling. When considering a fixed $50%$ Ni, the magnetic anisotropy increases with the increasing Fe content but decreases the formation energy. The calculated formation energies, elastic constants, and phonon frequencies demonstrate that the binary and quaternary compositions are stable. Most importantly, in $50%$ Fe and Ni-substituted-equiatomic phase, magnetic anisotropy constants and saturation magnetization density increase by 56% and 23% as compared to the MnAl. Further, the coercivity of the equiatomic phase predicted with micro-magnetic modeling is higher by 17% than the parent compound. Materials science ; Coercivity ; Manganese ; Ab initio ; Condensed matter physics ; Anisotropy ; Aluminium ; Magnetic anisotropy ; Stability (learning theory) ; Metallurgy ; Magnetization ; Magnetic field ; Computer science ; Machine learning ; Optics ; Physics Magnetic Properties and Applications ; Magnetic Properties of Alloys ; Microstructure and mechanical properties
Authors: Kaustubh J. Sawant | David Stockwell | Anthony D. DeBellis | L. A. D'Orazio | Philippe Sautet
Source: Angewandte Chemie International Edition (article, 2025-08-21)
Field: Chemistry — Inorganic Chemistry

Ensemble of Active Acid Sites on Amorphous Silica–Alumina Surfaces for Catalytic Cracking Amorphous silica-alumina are critical materials in catalysis, particularly for fluid catalytic cracking (FCC). However, the atomic scale understanding of the active sites has been challenging, because of the nonuniform atomic distribution and the material's amorphous nature. Here, we use density functional theory (DFT), machine learning potentials and sampling methods to investigate the relationship between structure and acidity in silica-modified alumina. Under FCC conditions, we predict an ensemble of acid sites with diverse local structures and a spectrum of acid strengths, including zeolite-like bridging Brønsted acid sites (BAS) and pseudo-bridging silanol BAS. This distribution is influenced by surface structure, Si coverage, and extent of hydroxylation, shaped by synthesis methods and reaction conditions. Experiments using a model Si-stabilized alumina catalyst confirm that Brønsted acidity increases with Si content, peaking at an optimal value before declining. These insights provide a foundation for designing efficient solid acid catalysts for industrial applications. Silanol ; Catalysis ; Amorphous solid ; Zeolite ; Fluid catalytic cracking ; Materials science ; Brønsted–Lowry acid–base theory ; Bridging (networking) ; Cracking ; Amorphous silica ; Chemical engineering ; Acid strength ; Computational chemistry ; Chemistry ; Organic chemistry ; Composite material ; Computer science Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis for Biomass Conversion