jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuji Tone | M. Hanai | Mitsuaki Kawamura | Kenjiro Taura | Toyotaro Suzumura
Source: ArXiv.org (preprint, 2025-02-04)
Field: Materials Science — Materials Chemistry

ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity The discovery of new materials using crystal structure prediction (CSP) based on generative machine learning models has become a significant research topic in recent years. In this paper, we study invariance and continuity in the generative machine learning for CSP. We propose a new model, called ContinuouSP, which effectively handles symmetry and periodicity in crystals. We clearly formulate the invariance and the continuity, and construct a model based on the energy-based model. Our preliminary evaluation demonstrates the effectiveness of this model with the CSP task. Generative grammar ; Mathematics ; Statistical physics ; Physics ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: O. Akanbi | Jack P. Shannon | Jérôme Delhommelle | Caroline Desgranges
Source: The Journal of Physical Chemistry Letters (review, 2025-11-05)
Field: Materials Science — Materials Chemistry

Mini Review: Synergizing Driven Quantum Dynamics, AI, and Quantum Computing for Next-Gen Materials Science The design of next-gen materials has undergone remarkable progress in recent years, as evidenced by the emergence of automated platforms combining artificial intelligence (AI)-driven synthesis planning and robotics for execution. In this Mini-Review, we analyze how synergistic approaches that combine driven quantum dynamics, AI/machine learning, and quantum computing accelerate the discovery and design process of quantum materials with enhanced properties and novel functionalities. Building on the capabilities of each of the three methods, synergistic approaches can provide access to the materials' response to time-dependent fields, enable the rapid exploration of vast design spaces, and identify novel quantum phases and materials with optimal properties. We examine recent successes in next-gen materials science for quantum batteries, colloidal quantum dots solar cells, quantum phototransistors, rare-earth-free materials, and applications in quantum information processing. We conclude with a discussion of recent research efforts in AI-for-quantum computing and quantum machine learning for next-gen materials discovery. Quantum computer ; Quantum ; Quantum machine learning ; Quantum information science ; Quantum information ; Quantum dot ; Process (computing) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Sensor and Energy Harvesting Materials
Authors: N. V. Hoang | Tr. Q. Trieu
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Fe-doped MoS <sub>2</sub> monolayers for CO, H <sub>2</sub> S, and NO <sub>2</sub> detection: towards advanced environmental sensors In this study, density functional theory (DFT) calculations integrated with machine learning <i>via</i> the crystal graph convolutional neural network (CGCNN) were employed to systematically investigate the electronic, magnetic, thermomechanical, and optical properties of Fe-doped MoS<sub>2</sub> (Fe-MoS<sub>2</sub>) monolayers before and after the adsorption of CO, H<sub>2</sub>S, and NO<sub>2</sub> gas molecules. The computational results reveal that gas adsorption, particularly for CO and NO<sub>2</sub>, induces strong interactions with the Fe-MoS<sub>2</sub> surface, leading to pronounced modifications in the electronic band structure, density of states, and charge redistribution. The thermomechanical and optical responses of the system are found to be sensitive to the nature of the adsorbed gas species, with notable variations observed in the elastic moduli and optical absorption coefficients. The Fe-MoS<sub>2</sub> monolayers exhibit strong optical absorption in the ultraviolet to visible spectral range (200-500 nm), alongside tunable electromagnetic characteristics modulated by gas adsorption. These findings highlight the potential of Fe-MoS<sub>2</sub> monolayers as a multifunctional material for applications in gas sensing and optoelectronic devices. Monolayer ; Absorption (acoustics) ; Ultraviolet ; Density functional theory ; Adsorption ; Quartz crystal microbalance ; Absorption spectroscopy ; Range (aeronautics) 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Industrial Gas Emission Control
Authors: Xue Wang | L. Lu | Q. M. Ma | Hongyan Shang | Lanyi Sun
Source: Molecules (article, 2025-11-29)
Field: Materials Science — Materials Chemistry

Prediction of Synthesis Yield of Polymethoxy Dibutyl Ether Under Small Sample Conditions In chemical reaction processes, yield prediction frequently faces challenges, such as multi-variable coupling, significant nonlinearity, and the limited accuracy of traditional mechanistic models. This study develops a datadriven prediction model that integrates the genetic algorithm (GA) with CatBoost to address these challenges. Four variables, including reactant ratio (<i>n</i>-butanol to trioxane), reaction temperature, reaction time, and catalyst concentration, were selected as model inputs based on 88 sets of experimental data. The model outputs focused on the yield of polymethoxy dibutyl ether with a polymerization degree of 1 (BTPOM<sub>1</sub>) and the total yield of polymethoxy dibutyl ether with polymerization degrees of 1 to 8 (BTPOM<sub>1-8</sub>). The model achieved automatic optimization of CatBoost on hyperparameters by combining a hybrid-coding genetic algorithm. The results demonstrated that the GACatBoost model significantly outperformed GAAdaBoost for both datasets: for BTPOM<sub>1</sub>, it reduced the mean squared error (MSE) by 50.1%, mean absolute error (MAE) by 40.6%, and mean absolute percentage error (MAPE) by 17.8% relative to GAAdaBoost. For BTPOM<sub>1-8</sub>, the reductions were more pronounced, with MSE decreasing by 54.0%, MAE by 45.0%, and MAPE by 33.8% compared to GAAdaBoost. Additionally, the GACatBoost model significantly outperformed three classical machine learning algorithms: Support Vector Regression (SVR), Random Forest (RF), and KNearest Neighbor (KNN). Feature importance analysis revealed that reaction time and reaction temperature are the key factors influencing BTPOM<i><sub>n</sub></i> yield. This research provides a feasible approach for accurate synthesis yield prediction and process optimization under small sample conditions. It is particularly valuable for early-stage laboratory research where experimental data is often limited. Yield (engineering) ; Mean absolute percentage error ; Hyperparameter ; Mean squared error ; Approximation error ; Polymerization ; Random forest ; Feature (linguistics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; biodegradable polymer synthesis and properties
Authors: Kusaba, Akira | Kuboyama, Tetsuji | Kawka, Karol | Kempisty, Pawel | Kangawa, Yoshihiro
Source: ArXiv.org (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

PyAPX: Python toolkit for atomic configuration pattern exploration In materials discovery, the integration of first-principles calculations with machine learning techniques has been actively studied for two key tasks: crystal structure prediction, which searches for stable structures given a chemical composition, and elemental substitution, which explores chemical compositions that yield desirable properties in a given crystal structure. However, even when both the crystal structure and chemical composition are fixed, material properties can still vary depending on the atomic arrangements (configurations) at crystallographic sites. To support detailed material design, we present PyAPX, a Python toolkit that performs Bayesian searches of stable atomic configurations. A distinctive feature of this initial release is the introduction of encoding methods suitable for configuration search, and we evaluate their performance using the h-BCN system. As a result, they were confirmed to yield superior convergence compared to commonly used one-hot encoding. PyAPX is broadly applicable to crystalline materials and is expected to further advance materials discovery. Python (programming language) ; Crystal structure ; Crystal (programming language) ; Yield (engineering) ; Feature (linguistics) ; Crystal structure prediction Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Le Zhao | Peng Ou | Jueyi Ye | Ju Rong | Xiaohua Yu | Yutao Niu | Li Ma | Keyuan Chen
Source: JOM (article, 2025-09-15)
Field: Materials Science — Metals and Alloys

High-Speed and Stable Deep-Learning Predictions of Anodic Dissolution of Ti-6Al-4V and Ti-4Al-2V Alloys Dissolution ; Corrosion ; Anode ; Welding ; Current density ; Density functional theory Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics
Authors: Kusaba, Akira | Kuboyama, Tetsuji | Kawka, Karol | Kempisty, Pawel | Kangawa, Yoshihiro
Source: arXiv (Cornell University) (preprint, 2025-11-22)
Field: Materials Science — Materials Chemistry

PyAPX: Python toolkit for atomic configuration pattern exploration In materials discovery, the integration of first-principles calculations with machine learning techniques has been actively studied for two key tasks: crystal structure prediction, which searches for stable structures given a chemical composition, and elemental substitution, which explores chemical compositions that yield desirable properties in a given crystal structure. However, even when both the crystal structure and chemical composition are fixed, material properties can still vary depending on the atomic arrangements (configurations) at crystallographic sites. To support detailed material design, we present PyAPX, a Python toolkit that performs Bayesian searches of stable atomic configurations. A distinctive feature of this initial release is the introduction of encoding methods suitable for configuration search, and we evaluate their performance using the h-BCN system. As a result, they were confirmed to yield superior convergence compared to commonly used one-hot encoding. PyAPX is broadly applicable to crystalline materials and is expected to further advance materials discovery. Python (programming language) ; Crystal structure ; Crystal (programming language) ; Yield (engineering) ; Feature (linguistics) ; Crystal structure prediction Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Itamar Wallwater | Yonatan Dubi | Ari Pakman | Anat Milo
Source: ChemRxiv (preprint, 2025-05-26)
Field: Chemistry — Physical and Theoretical Chemistry

Detecting polymorphism in molecular crystalsfrom single-molecule properties Understanding the crystallization of molecules, and specifically the appearance of polymorphs, is a great challenge to modern chemistry, with both fundamental and practical aspects. Here, motivated by the proven ability of Machine-Learning (ML) algorithms to perform classification tasks, we harness ML-based tools and existing chemical datasets to ask the following question: can the existence of polymorphs of a molecular crystal be predicted based solely on properties of the single molecule. We find that our algorithm can predict the existence of polymorphism with an average accuracy of 65% at best, not enough to generate a reliable “polymorph predicting” engine. Moreover, our results imply that the number of polymorphs is much larger than that reported in the literature. We suggest two possible reasons for the poor performance, both providing important insights. The first is that the data is inherently biased towards mono-morphs, as many polymorphs are reported as mono-morphs, not because they indeed have only one stable crystal structure, but rather because only on crystal form has been observed in experiments. The second reason is even more profound, suggesting that the many-body nature of crystallization limits the possibility of predicting crystal properties based solely on single-molecule characteristics. Polymorphism (computer science) ; Molecule ; Genetics ; Materials science ; Crystallography ; Computational biology ; Biology ; Chemistry ; Gene ; Genotype ; Organic chemistry Various Chemistry Research Topics
Authors: Audrey CampBell | Ligen Wang | Qi‐Jun Hong
Source: Journal of the American Ceramic Society (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Accelerating melting temperature predictions by leveraging LASP machine learning potentials in the SLUSCHI package Abstract The automated computational package SLUSCHI , originally interfaced with the first‐principles package VASP , has demonstrated effectiveness but remains computationally demanding for accurately calculating melting temperatures. This study leverages machine learning potentials via the efficient molecular dynamics simulator LAMMPS , utilizing pre‐trained LASP neural network potentials derived from first‐principles data. Tests on 30 diverse material systems—including simple metals, transition metals, alloys, oxides, and carbides—demonstrate that this approach significantly cuts computational costs, often by more than one order of magnitude compared to conventional DFT simulations. Approximately 60% of the calculated melting temperatures, prior to applying any DFT‐based correction, fall within 200 K of experimental values. Focusing specifically on single‐element systems, where direct comparison with DFT is possible, the percentage of melting temperatures within 200 K of experimental data improves from 53% to 82% following a DFT correction. This substantial improvement in computational efficiency, without sacrificing accuracy, facilitates high‐throughput materials screening and accelerates material design consistent with the materials genome paradigm. Melting temperature ; Artificial neural network ; Molecular dynamics ; R package ; Computational complexity theory ; Computational model ; Computational simulation Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Phase Equilibria and Thermodynamics
Authors: Ka Hung Chan | Xinyue Huang | Nobumichi Tamura | Xian Chen
Source: Journal of Applied Crystallography (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning analysis for nanoscale grain mapping by synchrotron Laue microdiffraction Understanding the grain morphology, orientation distribution and crystal structure of nanocrystals is essential for optimizing the mechanical and physical properties of functional materials. Synchrotron X-ray Laue microdiffraction is a powerful technique for characterizing crystal structures and orientation mapping using focused X-rays. However, when the grain sizes are smaller than the beam size, mixed peaks in the Laue pattern from neighboring grains limit the resolution of grain morphology mapping. We propose a physics-informed machine learning (PIML) approach that combines a convolutional neural network feature extractor with a physics-informed filtering algorithm to overcome the spatial resolution limits of X-rays, achieving nanoscale resolution for grain mapping. Our PIML method successfully resolves the grain size, orientation distribution and morphology of Au nanocrystals through synchrotron microdiffraction scans, showing good agreement with electron backscatter diffraction results. This PIML-assisted synchrotron microdiffraction analysis can be generalized to other diffraction-based probes, enabling the characterization of nanosized structures with micrometre-sized probes. Synchrotron ; Nanoscopic scale ; Electron backscatter diffraction ; Diffraction ; Orientation (vector space) ; Convolutional neural network ; Resolution (logic) ; Grain size ; Image resolution Microstructure and mechanical properties ; Metallurgy and Material Forming ; Advanced Electron Microscopy Techniques and Applications
Authors: Jacob Wafula | Clint Steed | Gerrit Matthys Ter Haar
Source: MATEC Web of Conferences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Evaluating the performance of active learning models on selective laser melting data sampling Data acquisition in additive manufacturing, specifically selective laser melting, is always expensive and worsens when the material under study is also costly. To address this challenge, researchers use available design of experiment (DoE) tools. This marks a shift away from trial-and-error and one-factor-at-a-time approaches, which are ineffective and cause the number of required experiments to grow exponentially as the number of parameters increases. However, the traditional design of experiments struggles in analysing complex, multi-parameter, and noisy systems, inherent characteristics of selective laser melting data. Active machine learning can excel in this limited data and sophisticated field. This study evaluates the performance of active machine learning models based on neural networks and Gaussian process regression (GPR) with a D-optimal design for predicting Ti-5-5-5-3 and Beta 21S samples. This study demonstrates the ability of both active learning methods to reduce data required with improved predictability, with GPR outperforming the others. These results demonstrate the potential of GPR for effective SLM experimentation and emphasize the necessity of improving active learning based on neural networks to increase experimental accuracy with less data. Active learning (machine learning) ; Artificial neural network ; Kriging ; Process (computing) ; Gaussian process ; Sampling (signal processing) ; Design of experiments ; Data acquisition ; Selective laser melting Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Gaussian Processes and Bayesian Inference
Authors: Veerupaksh Singla | Brett M. Savoie
Source: ChemRxiv (preprint, 2025-03-24)
Field: Materials Science — Polymers and Plastics

More Bang for Your Bond: Small Molecule Kinetics as Predictors of Polymer Stability Thermally stable polymers are essential for many high-performance applications, yet the traditional experimental methods for studying stability are material intensive while machine learning models remain limited by the scarcity of reliable degradation data. We hypothesized that computational small-molecule kinetics could bridge this gap owing to the local character of degradation initiation and the ease of collecting data for new molecular systems. To test this hypothesis, we curated computational kinetic data and developed models trained exclusively on small molecules to predict relative thermal stability rankings of polymers containing C, H, O, N, F, and Cl. Experimental degradation data from 41 common polymers and $\sim$7k small molecules was used to benchmark the ability of small-molecule kinetics to predict polymer behavior without additional training data. All of the resulting models show remarkable transferability between the small-molecule results and the polymer degradation metrics. We find that while chemically diverse training data generally improves predictions, the accuracy of the underlying small molecule kinetics is also crucial for maximizing transferability to polymer predictions. Notably, models trained on higher quality alkane data with consistent kinetic parameters perform comparably or better than those trained on more diverse but incomplete datasets for certain polymer predictions. This suggests that capturing fundamental degradation mechanisms accurately in simple systems may be more valuable than incorporating diverse but potentially inconsistent chemical data. These results underscore significant opportunities for training accurate stability models that can be used for early-stage material design. Kinetics ; Polymer ; Stability (learning theory) ; Bond ; Molecule ; Materials science ; Chemical physics ; Chemistry ; Business ; Computer science ; Organic chemistry ; Physics ; Composite material Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Polymer Nanocomposites and Properties
Authors: Lucian Fernando Bellini | Éric Cassiano Drey dos Santos | Yeun Haur Kang | William Haupt | Carlos Amaral Hölbig
Source:  (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

MetaScope: A Model for Microstructural Analysis of Carbon Steels Using Machine Learning Techniques Microstructural characterization in metallography often relies on manual analysis, which introduces bias and reduces reproducibility. To overcome this, we propose MetaScope, a deep learning model for pixel-level segmentation of ferrite and pearlite in SAE 1020 and 1045 steels. Using 2,200 images with masks generated by a Gaussian Mixture Model, the model combines an U-Net architecture with an EfficientNetB0 encoder. This approach demonstrates the potential of using machine learning techniques to support metallographic workflows and future expansion to other materials. Metallography ; Pearlite ; Segmentation ; Ferrite (magnet) ; Workflow ; Deep learning ; Artificial neural network Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Welding Techniques and Residual Stresses
Authors: Veerupaksh Singla | Brett M. Savoie
Source: ChemRxiv (preprint, 2025-03-12)
Field: Materials Science — Polymers and Plastics

More Bang for Your Bond: Small Molecule Kinetics as Predictors of Polymer Stability Thermally stable polymers are essential for many high-performance applications, yet the traditional experimental methods for studying stability are material intensive while machine learning models remain limited by the scarcity of reliable degradation data. We hypothesized that computational small-molecule kinetics could bridge this gap owing to the local character of degradation initiation and the ease of collecting data for new molecular systems. To test this hypothesis, we curated computational kinetic data and developed models trained exclusively on small molecules to predict relative thermal stability rankings of polymers containing C, H, O, N, F, and Cl. Experimental degradation data from 41 common polymers and $\sim$7k small molecules was used to benchmark the ability of small-molecule kinetics to predict polymer behavior without additional training data. All of the resulting models show remarkable transferability between the small-molecule results and the polymer degradation metrics. We find that while chemically diverse training data generally improves predictions, the accuracy of the underlying small molecule kinetics is also crucial for maximizing transferability to polymer predictions. Notably, models trained on higher quality alkane data with consistent kinetic parameters perform comparably or better than those trained on more diverse but incomplete datasets for certain polymer predictions. This suggests that capturing fundamental degradation mechanisms accurately in simple systems may be more valuable than incorporating diverse but potentially inconsistent chemical data. These results underscore significant opportunities for training accurate stability models that can be used for early-stage material design. Kinetics ; Bond ; Stability (learning theory) ; Polymer ; Molecule ; Small molecule ; Materials science ; Chemistry ; Computer science ; Economics ; Physics ; Organic chemistry ; Composite material ; Classical mechanics ; Finance Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Polymer Nanocomposites and Properties