jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Colin Zhang | Yang Ha
Source: Journal of Chemical Information and Modeling (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Toward Complete Molecular Structure Prediction from Infrared Spectroscopy Using Deep Learning Infrared (IR) spectroscopy is a broadly used tool to solve the molecular structures of unknown compounds. Though the theory of generating IR spectra from molecules is well established, the inverse problem of solving molecular structures from given spectra is still challenging. Because complex organic compounds often produce ambiguous spectra with overlapping peaks, even human researchers may struggle to interpret them accurately. Prior attempts to automate the interpretation of spectra using machine learning have been limited by the availability of high-quality data and primarily focused on predicting the presence of individual functional groups rather than predicting the structure of entire molecules. In this work, we propose a dual-loss deep learning architecture, inspired by image-captioning models, to predict the complete molecular structure as a Simplified Molecular Input Line Entry System (SMILES) string from only IR spectral absorbances. To train the models, we leveraged the high-performance computing resources of Lawrence Berkeley National Laboratory's Lawrencium cluster to generate a rich data set of over 17,000 unique IR spectra through quantum mechanical density functional theory calculations. Our best-performing model predicts the complete structure correctly on a single attempt for 16.26% of an unseen test set of 1710 spectra, while regenerating individual functional groups with up to 88% accuracy. We show that the dual loss function of our model architecture enables it to learn important chemical properties from both SMILES strings and spectral data. This research suggests the potential of using deep learning to enhance the capabilities of IR spectroscopy in analytical chemistry, medicine, and other fields. Deep learning ; Spectroscopy ; Infrared spectroscopy ; Set (abstract data type) ; Spectral line ; Pattern recognition (psychology) ; Density functional theory ; Function (biology) ; Test set Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Computational Drug Discovery Methods
Authors: Kallum
Hiten Mehta (22787425) | Riko I Made (22787428) | Stefanos Agrotis (3127527) | Ivan P. Parkin (1369014) | Gopinathan Sankar (1611058) | Albertus D. Handoko (3719263)
Source: Figshare (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Selectiveand Sustainable Oxidation of Allyl Alcoholin Water Using a TS‑1 Catalyst in Continuous Flow: A Machine-Learning-DrivenApproach A titanosilicate possessing an MFI structure was synthesized and characterized using a suite of <i>ex situ</i> and <i>in situ</i> techniques to ensure that the produced catalyst has isolated Ti<sup>4+</sup> centers in a tetrahedrally coordinated environment. First, the catalyst performance was evaluated using a batch process for allyl alcohol oxidation in the presence of H<sub>2</sub>O<sub>2</sub>. Subsequently, we developed an automatable flow reactor system coupled with a machine learning optimization algorithm to improve the production of glycidol through this catalytic route. Importantly, the reaction was carried out using water as the sole solvent, demonstrating that high selectivity can be maintained in an aqueous medium without the need for organic solvents typically required to suppress hydrolysis side reactions. An optimized turnover frequency of 4.33 h<sup>–1</sup> was achieved, representing a significant improvement over the batch process (3.08 h<sup>–1</sup>). The favorable mass transfer and catalyst-to-substrate ratio in the flow reactor allowed for efficient use of the active sites, achieving 14% conversion in just 12.6 s, with selectivity control achieved through simply tuning the residence time. Our modified autoencoder model proved to be an effective predictor for the sparse data set and outlier-prone results, outperforming conventional machine learning frameworks through simultaneous dimensional reduction and reaction yield prediction. This dimensional reduction approach also facilitated the identification and interpretation of divergent solutions offered by the optimization algorithm. These findings reinforce the potential of integrating machine learning with flow catalysis to develop more efficient and sustainable chemical processes. Catalysis ; Yield (engineering) ; Selectivity ; Flow chemistry ; Aqueous solution ; Batch reactor ; Allyl alcohol ; Process (computing) ; Reduction (mathematics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis for Biomass Conversion
Authors: Q.Q. Li | Hairong Luo | Ziqi Peng | Delong Cui | Juan He | Xing Deng
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Deep Learning Applications in Chemical Instrument Recognition: A Review Interpretability ; Deep learning ; Bridge (graph theory) ; Key (lock) ; Software deployment ; Function (biology) ; Artificial neural network Various Chemistry Research Topics ; Spectroscopy and Chemometric Analyses ; Advanced Data Processing Techniques
Authors: Masrur Ahmed | Jindal K. Shah
Source: Molecular Systems Design & Engineering (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Machine learning-enabled discovery of ionic liquid–solvent electrolytes exhibiting high ionic conductivity This study explores the development of ionic liquid-based high-conductivity electrolytes utilizing predictive machine learning models. Ionic conductivity ; Electrolyte ; Ionic bonding ; Work (physics) ; Conductivity Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Ionic liquids properties and applications
Authors: Svitlana Ponomarova | Oleksandr Ponomarov | Yurii Koval
Source: ArXiv.org (preprint, 2025-09-22)
Field: Materials Science — Materials Chemistry

Machine Learning Approach to Predict Curie Temperature in Binary Alloys This study presents a machine learning approach to predict the Curie temperature in binary alloys, specifically focusing on the Fe-Pt, Fe-Ni, Fe-Pd, and Co-Pt compounds within a concentration range of 10 to 90 atomic percent. The optimal mathematical algorithm for this task is the Voting Ensemble algorithm, which combines the predictions from multiple individual models to produce a final prediction. The results are validated against classical methods for calculating Curie temperatures. The experimental findings indicate that factors such as external pressure, atomic ordering, and alloy composition have a significant influence on the Curie temperatures in all examined binary systems. These factors can be leveraged to design alloys with specific Curie temperatures. Moreover, the proposed features, feature analysis algorithms, and computational methods pave the way for advancements across various materials, including ternary alloys, bulk materials, and nanomaterials, inspiring innovation in the field. Curie temperature ; Ternary operation ; Binary number ; Curie ; Marie curie ; Range (aeronautics) ; Feature (linguistics) Machine Learning in Materials Science ; Nuclear Physics and Applications ; Chemical Thermodynamics and Molecular Structure
Authors: Svitlana Ponomarova | Oleksandr Ponomarov | Yurii Koval
Source: Journal of Magnetism and Magnetic Materials (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

Machine learning approach to predict Curie temperature in binary alloys This study presents a machine learning approach to predict the Curie temperature in binary alloys, specifically focusing on the Fe-Pt, Fe-Ni, Fe-Pd, and Co-Pt compounds within a concentration range of 10 to 90 atomic percent. The optimal mathematical algorithm for this task is the Voting Ensemble algorithm, which combines the predictions from multiple individual models to produce a final prediction. The results are validated against classical methods for calculating Curie temperatures. The experimental findings indicate that factors such as external pressure, atomic ordering, and alloy composition have a significant influence on the Curie temperatures in all examined binary systems. These factors can be leveraged to design alloys with specific Curie temperatures. Moreover, the proposed features, feature analysis algorithms, and computational methods pave the way for advancements across various materials, including ternary alloys, bulk materials, and nanomaterials, inspiring innovation in the field. Curie temperature ; Ternary operation ; Binary number ; Curie ; Marie curie ; Alloy ; Curie's law Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Colter Ellis | Xinyi Fang | Christopher Balzer | Timothy Quah | M. Scott Shell | Glenn H. Fredrickson | Mengyang Gu
Source: Macromolecules (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Fast Phase Prediction of Charged Polymer Blends by White-Box Machine Learning Surrogates Compatibilized polymer blends are a complex yet versatile and widespread category of materials. When the components of a binary blend are immiscible, they are typically driven toward a macrophase-separated state, but with the introduction of electrostatic interactions, they can be either homogenized or shifted to microphase separation. However, both experimental and simulation approaches face significant challenges in efficiently exploring the vast design space of charge-compatibilized polymer blends, encompassing chemical interactions, architectural properties, and composition. In this work, we introduce a white-box machine learning approach integrated with polymer field theory to predict the phase behavior of these systems, which is significantly more accurate than conventional black-box machine learning approaches. The random phase approximation (RPA) calculation is used as a testbed to determine polymer phases. Instead of directly predicting the polymer phase output of RPA calculations from a large input space by a machine learning model, we build a parallel partial Gaussian process model to predict the most computationally intensive component of the RPA calculation that involves only polymer architecture parameters as inputs. This approach substantially reduces the computational cost of the RPA calculation across a vast input space with nearly 100% accuracy for the out-of-sample prediction, enabling rapid screening of polymer blend charge-compatibilization designs. More broadly, the white-box machine learning strategy offers a promising approach for the dramatic acceleration of polymer field-theoretic methods for mapping out polymer phase behavior. Polymer blend ; Polymer ; Polymer architecture ; Random phase approximation ; Binary number ; Gaussian process ; Phase (matter) ; Component (thermodynamics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Tanumoy Banerjee | Kevin Ji | Weiyi Xia | Gaoyuan Ouyang | Tyler Del Rose | Ihor Z. Hlova | B. G. Ueland | D. D. Johnson | C. H. Wang | Ganesh Balasubramanian | Prashant Singh
Source: ArXiv.org (preprint, 2025-03-06)
Field: Materials Science — Materials Chemistry

Machine learning driven search of hydrogen storage materials The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of ternary alloys (easily extendable to multi-principal-element alloys), such as Ti-Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co-Ni-X (X = Al, Mg, V). Ti-Nb-Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40-50%. We attributed to slow hydrogen kinetics in molybdenum rich alloys, which is validated by our pressure-composition isotherm (PCT) experiments on pure Ti and Ti5Mo95 alloys. Density functional theory (DFT) and molecular simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. To aid material selection, we present two periodic tables illustrating elemental effects on (a) H2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage. Ternary operation ; Hydrogen ; Hydrogen storage ; Leverage (statistics) ; Solubility ; Boosting (machine learning) ; Lattice (music) ; Density functional theory Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: J. Böhm | Aurélie Champagne
Source: ArXiv.org (preprint, 2025-10-10)
Field: Materials Science — Materials Chemistry

Predicting Crystal Structures and Ionic Conductivities in Li$_{3}$YCl$_{6-x}$Br$_{x}$ Halide Solid Electrolytes Using a Fine-Tuned Machine Learning Interatomic Potential Understanding ionic transport in halide solid electrolytes is essential for advancing next-generation solid-state batteries. This work demonstrates the effectiveness of fine-tuning the Crystal Hamiltonian Graph Network (CHGNet) universal machine learning interatomic potential to accurately predict total energies, relaxed geometries, and lithium-ion dynamics in the ternary halide family Li$_{3}$YCl$_{6-x}$Br$_{x}$ (LYCB). Starting from experimentally refined disordered structures of Li$_{3}$YCl$_{6}$ and Li$_{3}$YBr$_{6}$, we present a strategy for generating ordered structural models through systematic enumeration and energy ranking, providing realistic structural models. These serve as initial configurations for an iterative fine-tuning workflow that integrates molecular dynamics simulations and static density functional theory calculations to achieve near-ab initio accuracy at four orders of magnitude lower computational cost. We further reveal the influence of composition (varied x) on the predicted phase stability and ionic conductivity in LYCB, demonstrating the robustness of our approach for modeling transport properties in complex solid electrolytes. Interatomic potential ; Crystal structure prediction ; Ionic conductivity ; Ionic bonding ; Halide ; Molecular dynamics ; Ab initio ; Ternary operation ; Crystal structure Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Lee Hwee Kuan | Sojeong Park | Wei Liu | Simon Julian Lauw | Wooseop Kwak | Chandni Verma
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Scalable Neural Network Driven Molecular Dynamics Simulation Scalability ; Computer science ; Molecular dynamics ; Artificial neural network ; Dynamics (music) ; Distributed computing ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry ; Database Machine Learning in Materials Science
Authors: Zhenhua Zhang | Dandan Ren | Mei Hong | Ruiying Li | Yuwei Zhou | C.R. Li | Jieqiong Ding | Fei Wang | Xingchen Liu | Weixin Huang | Xiaodong Wen
Source: Research Square (preprint, 2025-02-05)
Field: Materials Science — Materials Chemistry

Machine learning assisted interfacial catalysis of metal-oxide nanocatalysts in CO2 hydrogenation Nanomaterial-based catalyst ; Catalysis ; Oxide ; Metal ; Materials science ; Chemical engineering ; Nanotechnology ; Chemistry ; Metallurgy ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysts for Methane Reforming
Authors: Yue Shi | Jiaxin Tian | Haoyuan Li
Source: ChemRxiv (preprint, 2025-02-28)
Field: Materials Science — Materials Chemistry

MlCOFSyn: A Machine Learning Framework to Facilitate the Synthesis of 2D Covalent Organic Frameworks Two-dimensional covalent organic frameworks (2D COFs) have been historically synthesized empirically, often resulting in uncontrolled crystallization and inferior crystal sizes, which limit their performance in various applications. Recently, crystallization models tailored for 2D COFs have been developed, which demonstrate great potential in facilitating their rational synthesis. Nevertheless, effective strategies to leverage these models for 2D COF synthesis remain underdeveloped, and the specialized expertise required, combined with the high computational costs of exploring the vast chemical space, poses additional barriers to their practical application. In this work, we present a machine learning framework, named MlCOFSyn, designed to assist in the synthesis of 2D COFs. This framework explores the application of 2D COF crystallization models by implementing three pivotal functionalities: predicting crystal sizes based on the input monomer addition sequence, reverse-engineering monomer addition sequences to achieve desired crystal sizes, and optimizing monomer addition sequences to produce larger crystals. These functionalities are critical for the controlled synthesis of 2D COFs but have been largely underexplored due to the lack of accessible theoretical tools. The MlCOFSyn framework leverages efficient machine-learning algorithms and features an intuitive graphical interface, enabling its use on consumer-grade computers by non-experts. By addressing these gaps, the MlCOFSyn framework represents a substantial advancement in facilitating 2D COF research and synthesis. Covalent bond ; Computer science ; Knowledge management ; Artificial intelligence ; Process management ; Business ; Human–computer interaction ; Chemistry ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Reactions
Authors: Abhigyan Ojha (21754499) | Ayush Tiwari (1697743) | Sai Aditya Tagore Vambaravelli (21754502) | Kisor Kumar Sahu (9040966) | Sivaiah Bathula (4781340)
Source: Figshare (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

ExploringPhase Stability and Transport Propertiesof Emerging Thermoelectric Materials: Machine Learning and ExperimentalInsights This study investigates the thermoelectric (TE) properties of newly synthesized materials, including Heusler alloys, magnesium silicides, and tetrahedrites, identified through machine learning (ML) approaches and validated experimentally. Critical descriptors for predicting important properties of TE materials, such as the band gap, formation energy, and Seebeck coefficient (<i>S</i>), which strongly influence material stability and TE efficiency, are systematically assessed. Although density functional theory (DFT) remains a foundational approach for predicting TE performance, its computational expense limits its widespread application. To address this challenge, crystal graph convolutional neural networks (CGCNN) are employed to efficiently predict band gap and formation energy from graph-based representations of crystal structures. The experimentally measured band gaps of the synthesized materials show strong agreement with CGCNN predictions. Additionally, experimental measurements of <i>S</i> across different TE classes corroborate computational estimates, demonstrating the robustness of the ML models. For instance, the newly synthesized double half-Heusler compound Ti<sub>2</sub>FeNiSb<sub>2</sub> exhibits an experimental <i>S</i> value of approximately −120 μV/K at 700 K, closely matching the Random Forest model prediction of −143.75 μV/K across the 300–700 K range. The CGCNN model achieves a mean absolute error (MAE) of 0.261 eV for band gap and 0.058 eV/atom for formation energy approximately 1.5 and 2 times lower, respectively, than those reported for earlier pretrained models. Furthermore, for the <i>S</i> prediction, the Random Forest algorithm, optimized using a root-mean-square error loss function, improves accuracy by ∼43.55% compared to previous ML methods. Overall, the integration of ML techniques accelerates TE material discovery, overcoming the limitations of conventional computational approaches. Robustness (evolution) ; Random forest ; Band gap ; Stability (learning theory) ; Artificial neural network ; Thermoelectric effect ; Convolutional neural network ; Mean absolute percentage error Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Wei‐Jiang Zhao | Haiyang Huo | Zhong‐Yuan Lu | Zhao‐Yan Sun
Source: Chinese Journal of Polymer Science (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Analyzing Conformational Transition Pathways in Semi-flexible Polymer Chains with Deep Learning Polymer ; Transition (genetics) ; Symmetrization ; Conformational ensembles ; Transition point ; Conformational change ; Bundle ; Artificial neural network Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Jai Krishna Gautam | Praneel Acharya | Zhong Hu
Source: Recent Progress in Materials (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Deep Learning Approaches for Predicting Strain Energy in Heterogeneous Materials This paper investigates data-driven strain energy prediction for heterogeneous elastic materials using the Mechanical MNIST benchmark dataset (60,000 28 × 28 stiffness maps by precomputed finite-element results). The results by classical regression models (linear regression, random forest, and gradient boosting) using hand-crafted features were compared with the results by the deep learning models (Convolutional neural network (CNN) and residual network (ResNet)) trained end-to-end on images. In held-out tests, CNN and ResNet achieve MSE ≈ 4.21–4.33 with R&lt;sup&gt;2&lt;/sup&gt; ≈ 0.982, substantially outperforming classical methods (MSE ≈ 15.8–50.0; R&lt;sup&gt;2&lt;/sup&gt; ≈ 0.79–0.93). In addition to accuracy, the train/inference cost and model limitations (dataset scope, loading modes, physics fidelity) were discussed. The model interpretability (saliency/occlusion) was analyzed to link learned features to stiffness patterns. These results support the use of images as energy surrogates as fast approximations for design and optimization, while outlining steps for real-material validation (e.g., in coupling with acoustic emission sensing and physics‐informed learning). Interpretability ; Deep learning ; MNIST database ; Artificial neural network ; Benchmark (surveying) ; Energy (signal processing) ; Residual ; Coupling (piping) ; Stiffness Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Microstructure and mechanical properties