jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S. I. Simak | Erna K. Delczeg‐Czirjak | Olle Eriksson
Source: Scientific Reports (article, 2025-05-17)
Field: Materials Science — Materials Chemistry

Machine learning for improved density functional theory thermodynamics The predictive accuracy of density functional theory (DFT) for alloy formation enthalpies is often limited by intrinsic energy resolution errors, particularly in ternary phase stability calculations. In this work, we present a machine learning (ML) approach to systematically correct these errors, improving the reliability of first-principles predictions. A neural network model has been trained to predict the discrepancy between DFT-calculated and experimentally measured enthalpies for binary and ternary alloys and compounds. The model utilizes a structured feature set comprising elemental concentrations, atomic numbers, and interaction terms to capture key chemical and structural effects. By applying supervised learning and rigorous data curation we ensure a robust and physically meaningful correction. The model is implemented as a multi-layer perceptron (MLP) regressor with three hidden layers, optimized through leave-one-out cross-validation (LOOCV) and k-fold cross-validation to prevent overfitting. We illustrate the effectiveness of this method by applying it to the Al-Ni-Pd and Al-Ni-Ti systems, which are of interest for high-temperature applications in aerospace and protective coatings. Computer science ; Thermodynamics ; Density functional theory ; Statistical physics ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Haochen Shi | Yi-Ming Shi | Pengcheng Jiang | Bo Qiao | Zhiqin Liang | Suling Zhao | Dandan Song
Source: Journal of Materials Chemistry C (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Integrating machine learning and generative models for the intelligent design of TADF materials with circularly polarized luminescence We propose a machine learning and generative approach for circularly polarized TADF. A robust model finds key g lum features, and a VAE generates candidates with high g lum and good synthetic accessibility for chiral optoelectronics. Generative grammar ; Luminescence ; Key (lock) ; Generative model ; Generative Design ; Intelligent design Machine Learning in Materials Science ; Solid-state spectroscopy and crystallography
Authors: Zihao Gao | Changsheng Zhu | Yafeng Shu | Canglong Wang | Yupeng Chen | Shaohui Wang
Source: Applied Intelligence (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Pgcnn: an interpretable graph convolutional neural network for predicting the mechanical properties of Ti-6Al-4V alloy Computer science ; Convolutional neural network ; Graph ; Alloy ; Materials science ; Artificial intelligence ; Pattern recognition (psychology) ; Composite material ; Theoretical computer science Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Yi Sheng Ng | Jin‐Cheng Zheng
Source: ACS Applied Energy Materials (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Machine Learning with Interpretable Local Descriptors for Hydrogen Evolution Reaction on Borophene: Prediction and Physical Insights The local environment of an adsorption site is crucial in determining the adsorption energy. Understanding these local structural characteristics is thus key to optimizing adsorption properties, and this can be enhanced through machine learning (ML) models. In this study, we developed a compact set of five local descriptors (A, B, C, α, and β) that efficiently capture the structural features of the nearest neighbors (NNs) of around 18 different adsorption sites across six different borophene phases under varying external in-plane strain. Using an expanded data set of 131 data points and a fine-tuned ML model, we successfully mapped the descriptors to the hydrogen adsorption free energy (ΔGH), achieving a low cross-validation MAE of 0.05 eV and a high R2 of 0.95. Interpretation with SHapley Additive ExPlanations (SHAP) and radar charts revealed that the structure and coordination of the second nearest neighbors (B and β) play a dominant role in determining ΔGH. Intermediate values of B and β are identified as optimal for HER, whereas extreme descriptor values lead to anomalous ΔGH, which appear to be associated with outlier-like behavior in unrelaxed vacancy formation energy and adsorption charge density distortion cost. The insights from this study enhance the understanding of adsorption in borophene and demonstrate the effectiveness of compact local descriptors for adsorption studies. Borophene ; Artificial intelligence ; Machine learning ; Computer science ; Pattern recognition (psychology) ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Ryoji Yamamoto | Yasuhiko Igarashi | Hiroaki Imai | T. Sakata | Shuntaro Miyakawa | SEIJI YOSHIZAKI | Takaya Saito | Yuya Oaki
Source: Batteries & Supercaps (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Performance Prediction Models with Improved Accuracy and Generalizability for Organic Cathode‐Active Materials of Lithium‐Ion Battery Development of organic energy storage requires enhancing performances of active materials. In particular, reaction potential and specific capacity of cathode‐active materials have significant impact on energy density of organic lithium‐ion battery. However, discovery of new compounds for active materials based on professional experience and intuition meets the limitation of huge search space of organic molecules. The performance predictors enable efficient discovery of new potential compounds. Although the predictors of potential, capacity, and energy density (models G1) are prepared in the previous work, these become older and have problems. In the present work, the updated models G2 have been constructed to improve the accuracy, usability, and generalizability. The models G2 are prepared by sparse modeling for small data combining machine learning and chemical insight on the training data set with adding new data. The updated models are validated using a new test data set and data‐scientific methods. The improved predictors contribute to efficient exploration of new cathode‐active materials to realize high‐performance batteries. Generalizability theory ; Lithium-ion battery ; Lithium (medication) ; Cathode ; Battery (electricity) ; Materials science ; Ion ; Computer science ; Electrical engineering ; Chemistry ; Engineering ; Psychology ; Statistics ; Thermodynamics ; Mathematics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Zainab Lafi | Sina M. Matalqah | S. C. Asha | Nisreen Asha | Hala Mhaidat | Sara Yousef Asha
Source: International Journal of Applied Pharmaceutics (article, 2025-09-07)
Field: Materials Science — Materials Chemistry

ADVANCED FABRICATION AND CHARACTERIZATION OF SILVER NANOPARTICLES USING AI TECHNIQUES The integration of machine learning (ML) into nanoscience has transformed the fabrication and characterization of silver nanoparticles (AgNPs), enabling precise control over particle size, shape, and functionalization. This review highlights the application of supervised and unsupervised ML models, such as artificial neural networks (ANNs), support vector machines (SVMs), and decision trees, in optimizing AgNP synthesis parameters, including temperature, pH, and reducing agent concentration. Emphasis is placed on green synthesis methods using plant extracts, where ML predicts eco-friendly conditions with minimal experimental input. Characterization techniques benefit from ML-driven image and spectral data analysis, enhancing speed and accuracy. ML is also pivotal in predicting the toxicity and biocompatibility of AgNPs, reducing reliance on animal testing and enabling safer biomedical applications. ML reduced synthesis optimization time by 30%," and to specify the types of ML techniques applied, like neural networks or support vector machines (SVMs). Furthermore, ML enhances functionalization strategies for drug delivery, biosensing, and environmental remediation. By quantifying performance outcomes and improving reproducibility, ML supports the scalable and sustainable development of AgNPs. This review offers a detailed synthesis of current advances and identifies future opportunities for intelligent, data-driven nanomaterial design. Characterization (materials science) ; Biocompatibility ; Silver nanoparticle ; Support vector machine ; Artificial neural network ; Scalability ; Fabrication Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanotechnology research and applications
Authors: Karsten Duch | Markus Illner | Jens‐Uwe Repke
Source: Organic Process Research & Development (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Development of an Automated High-Throughput Screening Platform for the Dynamic Phase Separation Analysis of Microemulsion Systems with AI Image Recognition Increasing efforts are undertaken to develop new sustainable production processes, and homogeneous catalysis offers many advantages regarding selectivity and energy efficiency in new chemical production routes. A major factor often limiting the application of homogeneous catalysis is the retention of valuable catalysts. One promising option to introduce superior reaction performance and catalyst recovery in organic reactions is the use of water-soluble catalysts in aqueous reaction media with surfactants. However, these surfactant-based microemulsion systems (MES) exhibit a complex phase separation behavior that is dependent on various parameters such as temperature and component concentrations, while its prediction is currently not possible due to the complex thermodynamics. The characterization of the phase behavior hence requires extensive and time-consuming experimental investigation due to a lack of fundamental modeling approaches. To facilitate the acquisition of experimental data, this contribution presents the development of a high-throughput screening platform for dynamic phase separation analysis with an automated experimental procedure, AI analysis of separation images, and automated result data handling. The platform enables a fast characterization of MES separation behavior, which is required for process development and operation. The functionalities of the screening platform are demonstrated in a case study for the hydroformylation of decene. The image detection is performed with a Mask R-CNN model achieving a ±1.5% accuracy in phase height detection with a classification confidence threshold of 96%. The new setup enables a fast evaluation of over 722 measurement runs each with a different combination of separation temperature and mixture composition that only required at total of 38 manual dosing steps. The gathered data is also used to derive a correlation for a soft-sensor with interpretable machine learning, enabling online insights into otherwise inaccessible process variables in an MES plant and enabling its operability. Microemulsion ; Throughput ; Separation (statistics) ; Computer science ; Image (mathematics) ; Phase (matter) ; Artificial intelligence ; Process engineering ; Chromatography ; Nanotechnology ; Pattern recognition (psychology) ; Chemistry ; Materials science ; Machine learning ; Chemical engineering ; Engineering ; Organic chemistry ; Operating system Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Crystallization and Solubility Studies
Authors: Puneethkumar M. Srinivasappa | Tangsheng Zou | Chun Ki Ng | Amol P. Amrute
Source: World Scientific Annual Review of Functional Materials (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

High-Throughput Methods for Accelerated Catalyst Discovery The development of efficient catalysts is a critical need, driven by the ever-growing demand for process efficiency and environmental concerns related to greenhouse gas emissions and waste generation. Discovering a new catalyst and developing it into a deployable form is a slow process that can take more than 10 years, based on the conventional one-at-a-time approach. High-throughput Experimentation (HTE) has evolved as a revolutionary approach to overcome the limitations of conventional methods. The application of HTE in catalysis research has significantly accelerated the discovery of efficient catalysts and optimal reaction conditions in a number of relevant reactions. Earlier review papers extensively described the applications of HTE in various heterogeneously and homogeneously catalyzed transformations. In this review, we further explore the origin and evolution of the HTE approach and show how its early developments paved the way for today’s cutting-edge advances. We outline key advances in miniaturization, parallelization, and automation, enabling technologies behind various HTE modules and systems for catalyst synthesis and reactor design for parallel catalyst screening. To emphasize improvements achieved by applying HTE methods, the review further describes recent applications of batch and continuous-flow HT modes for reaction of high practical relevance, such as C–C bond formation, nitric oxide (NO) and carbon monoxide (CO) oxidation, alkene epoxidation, and carbon dioxide (CO 2 ) conversion. Finally, perspectives are provided suggesting synergizing HTE with data-driven methods, such as machine learning, artificial intelligence, and autonomous workflows, as crucial next steps to further unlock catalyst innovation and integrate catalyst discovery with process engineering. Throughput ; Download ; Computer science ; Library science ; World Wide Web ; Information retrieval ; Operating system Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: R. Shoba | Ajeet Bhagwat Bhane | Uppalapati Srilakshmi | Gulbir Singh | Ayush Gandhi | Rajeev Sharma | Yogesh Kumar
Source: International Journal of Information Technology (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

MLR-ANN: hybrid framework for data driven prediction of properties of aluminium alloys Aluminium ; Ultimate tensile strength ; Artificial neural network ; Data-driven ; Aluminium alloy ; Range (aeronautics) ; Alloy ; Linear regression ; Residual Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Jaesun Kim | J. Q. You | Yutack Park | Yunsung Lim | Yujin Kang | Jisu Kim | Hyejin Jeon | Suyeon Ju | Deli Hong | Seung Yul Lee | Soo-Min Choi | Y. Kim | Jae Wook Lee | Seungwu Han
Source: ArXiv.org (preprint, 2025-10-13)
Field: Materials Science — Materials Chemistry

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials Accurate yet transferable machine-learning interatomic potentials (MLIPs) are essential for accelerating materials and chemical discovery. However, most universal MLIPs overfit to narrow datasets or computational protocols, limiting their reliability across chemical and functional domains. We introduce a transferable multi-domain training strategy that jointly optimizes universal and task-specific parameters through selective regularization, coupled with a domain-bridging set (DBS) that aligns potential-energy surfaces across datasets. Systematic ablation experiments show that small DBS fractions (0.1%) and targeted regularization synergistically enhance out-of-distribution generalization while preserving in-domain fidelity. Trained on fifteen open databases spanning molecules, crystals, and surfaces, our model, SevenNet-Omni, achieves state-of-the-art cross-domain accuracy, including adsorption-energy errors below 0.06 eV on metallic surfaces and 0.1 eV on metal-organic frameworks. Despite containing only 0.5% r$^2$SCAN data, SevenNet-Omni reproduces high-fidelity r$^2$SCAN energetics, demonstrating effective cross-functional transfer from large PBE datasets. This framework offers a scalable route toward universal, transferable MLIPs that bridge quantum-mechanical fidelities and chemical domains. Overfitting ; Limiting ; Generalization ; Scalability ; Chemical space ; Set (abstract data type) ; Regularization (linguistics) ; Training set ; Reliability (semiconductor) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Inorganic Chemistry and Materials
Authors: Wansun Kim | Woo Jeng Kim | Hyerin Lee | Gi‐Ja Lee | Kyung Min Byun | Tai Ha Joo | Yeon-Hee Kim | Sang Woong Moon | Samjin Choi
Source: Sensors and Actuators B Chemical (article, 2025-05-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spontaneous formation of plasmonic nanoparticles by transition metal dichalcogenides for SERS biosensing with deep learning analysis Plasmon ; Biosensor ; Transition metal ; Nanotechnology ; Plasmonic nanoparticles ; Nanoparticle ; Materials science ; Chemistry ; Optoelectronics ; Organic chemistry ; Catalysis Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Advanced Nanomaterials in Catalysis
Authors: Mulusew W. Yaltaye | Yingqi Zhao | Kang Zhan | Éva Bozó | Pei-Lin Xin | Vahid Farrahi | Francesco De Angelis | Jian‐An Huang
Source: The Journal of Physical Chemistry Letters (article, 2025-08-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Single-Molecule SERS Detection of Phosphorylation in Serine and Tyrosine Using Deep Learning-Assisted Plasmonic Nanopore Single-molecule detection of post-translational modifications (PTMs) such as phosphorylation plays a crucial role in early diagnosis of diseases and therapeutics development. Although single-molecule surface-enhanced Raman spectroscopy (SM-SERS) detection of PTMs has been demonstrated, the data analysis and detection accurracies were hindered by interference from citrate signals and lack of reference databases. Previous reports required complete coverage of the nanoparticle surface by analyte molecules to replace citrates, hampering the detection limit. Here, we developed a high-accuracy SM-SERS approach by combining a plasmonic particle-in-pore sensor to collect SM-SERS spectra of phosphorylation at Serine and Tyrosine, k-means-based clustering for citrate signal removal, and a one-dimensional convolutional neural network (1D-CNN) for phosphorylation identification. Significantly, we collected SM-SERS data with submonolayer analyte coverage of the particle surface and discriminated the phosphorylation in Serine and Tyrosine with over 95% and 97% accuracy, respectively. Finally, the 1D-CNN features were interpreted by a one-dimensional gradient feature weight and SM-SERS peak occurrence frequencies. Analyte ; Serine ; Plasmon ; Phosphorylation ; Nanopore ; Surface-enhanced Raman spectroscopy ; Molecule ; Raman spectroscopy ; Materials science ; Tyrosine ; Chemistry ; Nanotechnology ; Optoelectronics ; Raman scattering ; Biochemistry ; Chromatography ; Physics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Y. Alkabakibi | Congwei Xie | Artem R. Oganov
Source: ArXiv.org (preprint, 2025-04-28)
Field: Materials Science — Materials Chemistry

Graph Neural Network Prediction of Nonlinear Optical Properties Nonlinear optical (NLO) materials for generating lasers via second harmonic generation (SHG) are highly sought in today's technology. However, discovering novel materials with considerable SHG is challenging due to the time-consuming and costly nature of both experimental methods and first-principles calculations. In this study, we present a deep learning approach using the Atomistic Line Graph Neural Network (ALIGNN) to predict NLO properties. Sourcing data from the Novel Opto-Electronic Materials Discovery (NOEMD) database and using the Kurtz-Perry (KP) coefficient as the key target, we developed a robust model capable of accurately estimating nonlinear optical responses. Our results demonstrate that the model achieves 82.5% accuracy at a tolerated absolute error up to 1 pm/V and relative error not exceeding 0.5. This work highlights the potential of deep learning in accelerating the discovery and design of advanced optical materials with desired properties. Artificial neural network ; Nonlinear system ; Graph ; Deep learning ; Key (lock) ; Nonlinear optical ; Deep neural networks ; Backpropagation ; Data modeling Machine Learning in Materials Science ; Nonlinear Optical Materials Studies ; Nonlinear Optical Materials Research
Authors: Niek De Jonge | Justin J. J. van der Hooft | Daniel Probst
Source: ArXiv.org (preprint, 2025-02-15)
Field: Chemistry — Spectroscopy

To Bin or not to Bin: Alternative Representations of Mass Spectra Mass spectrometry, especially so-called tandem mass spectrometry, is commonly used to assess the chemical diversity of samples. The resulting mass fragmentation spectra are representations of molecules of which the structure may have not been determined. This poses the challenge of experimentally determining or computationally predicting molecular structures from mass spectra. An alternative option is to predict molecular properties or molecular similarity directly from spectra. Various methodologies have been proposed to embed mass spectra for further use in machine learning tasks. However, these methodologies require preprocessing of the spectra, which often includes binning or sub-sampling peaks with the main reasoning of creating uniform vector sizes and removing noise. Here, we investigate two alternatives to the binning of mass spectra before down-stream machine learning tasks, namely, set-based and graph-based representations. Comparing the two proposed representations to train a set transformer and a graph neural network on a regression task, respectively, we show that they both perform substantially better than a multilayer perceptron trained on binned data. Bin ; Spectral line ; Physics ; Mathematics ; Algorithm ; Astronomy Molecular spectroscopy and chirality
Authors: S.N. Nazrin | Liyana Adilla Burhanuddin | Halimah Badioze Zaman | Faznny Mohd Fudzi | Ibrahim A. Shaaban
Source: The European Physical Journal Special Topics (article, 2025-03-29)
Field: Materials Science — Ceramics and Composites

Integrating machine learning and experimental data in modeling optical behaviors of neodymium oxide nanoparticle-doped glasses Neodymium ; Nanoparticle ; Doping ; Materials science ; Oxide ; Computer science ; Nanotechnology ; Optoelectronics ; Optics ; Physics ; Laser ; Metallurgy Glass properties and applications ; Luminescence Properties of Advanced Materials ; Pigment Synthesis and Properties