jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ludovic Zaza | Bojana Ranković | Philippe Schwaller | Raffaella Buonsanti
Source: Journal of the American Chemical Society (article, 2025-02-07)
Field: Materials Science — Materials Chemistry

A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes The ability to precisely design colloidal nanocrystals (NCs) has far-reaching implications in optoelectronics, catalysis, biomedicine, and beyond. Achieving such control is generally based on a trial-and-error approach. Data-driven synthesis holds promise to advance both discovery and mechanistic knowledge. Herein, we contribute to advancing the current state of the art in the chemical synthesis of colloidal NCs by proposing a machine-learning toolbox that operates in a low-data regime, yet comprehensive of the most typical parameters relevant for colloidal NC synthesis. The developed toolbox predicts the NC shape given the reaction conditions and proposes reaction conditions given a target NC shape using Cu NCs as the model system. By classifying NC shapes on a continuous energy scale, we synthesize an unreported shape, which is the Cu rhombic dodecahedron. This holistic approach integrates data-driven and computational tools with materials chemistry. Such development is promising to greatly accelerate materials discovery and mechanistic understanding, thus advancing the field of tailored materials with atomic-scale precision tunability. Toolbox ; Nanotechnology ; Nanocrystal ; Chemistry ; Biomedicine ; Colloid ; Field (mathematics) ; Scale (ratio) ; Computer science ; Materials science ; Physics Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Brook Wander | Joseph Musielewicz | Raffaele Cheula | John R. Kitchin
Source: The Journal of Physical Chemistry C (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis Access to the potential energy Hessian enables determination of the Gibbs free energy and certain approaches to transition state search and optimization. Here, we demonstrate that off-the-shelf pretrained Open Catalyst Project (OCP) machine learned potentials (MLPs) determine the Hessian with great success (58 cm<sup>-1</sup> mean absolute error (MAE)) for intermediates adsorbed to heterogeneous catalyst surfaces. This enables the use of OCP models for the aforementioned applications. The top performing model, with a simple offset correction, gives good estimations of the vibrational entropy contribution to the Gibbs free energy with an MAE of 0.042 eV at 300 K. The ability to leverage models to capture the translational entropy was also explored. It was determined that 94% of randomly sampled systems had a translational entropy greater than 0.1 eV at 300 K. This underscores the need to go beyond the harmonic approximation to consider the entropy introduced by adsorbate translation, which increases with temperature. Lastly, we used MLP-determined Hessian information for transition state search and found we were able to reduce the number of unconverged systems by 65 to 93% overall convergence, improving on the baseline established by CatTSunami. Artificial neural network ; Catalysis ; Graph ; Computer science ; Chemistry ; Theoretical computer science ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electrocatalysts for Energy Conversion
Authors: Ammar Armghan | Raj Agravat | Shobhit K. Patel | Khaled Aliqab | Naim Ben Ali | Meshari Alsharari
Source: Ain Shams Engineering Journal (article, 2025-07-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plus-shaped ultra-wideband solar absorber based on MXene material for solar thermal heating applications using machine learning Researchers are using MXene material nowadays, because of its superior optical potentials, in the photothermal and photoelectric fields. This paper shows a broadband MXene material-based solar absorber. The proposed MXene-based solar absorber has a 600 × 600 nm 2 structure dimension. Plus-shaped structure covered by cylinder resonator using MXene material. The solar absorber has excellent capability of light capture (97.11 % average absorptance with 0.2 µm to 3 µm), and 96.4 % absorption of energy with the solar radiation at AM 1.5 solar irradiance. This solar absorber got a 99.98 % maximum average absorptance. Considering a test size of 0.25 & an absorptance predictions mean squared error (MSE) of 7.1804 × 10–6, the machine learning (ML) (Local Regression) technique has an adequate prediction accuracy. This research is a good peak for solar heating devices applicable in industry and household usage. Thermal ; Materials science ; Photovoltaic system ; Engineering physics ; Aerospace engineering ; Mechanical engineering ; Optoelectronics ; Engineering ; Physics ; Electrical engineering ; Meteorology Metamaterials and Metasurfaces Applications ; Energy Harvesting in Wireless Networks ; MXene and MAX Phase Materials
Authors: Naoki Matsumura | Yuta Yoshimoto | Tamio Yamazaki | Tomohito Amano | Tomoyuki Noda | Naoki Ebata | Takatoshi Kasano | Yasufumi Sakai
Source: Journal of Chemical Theory and Computation (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Generator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-Scale Simulations Neural network potentials (NNPs) enable large-scale molecular dynamics (MD) simulations of systems containing >10,000 atoms with the accuracy comparable to <i>ab initio</i> methods and play a crucial role in material studies. Although NNPs are valuable for short-duration MD simulations, maintaining the stability of long-duration MD simulations remains challenging due to the uncharted regions of the potential energy surface (PES). Currently, there is no effective methodology to address this issue. To overcome this challenge, we developed an automatic generator of robust and accurate NNPs based on an active learning (AL) framework. This generator provides a fully integrated solution encompassing initial data set creation, NNP training, evaluation, sampling of additional structures, screening, and labeling. Crucially, our approach uses a sampling strategy that focuses on generating unstable structures with short interatomic distances, combined with a screening strategy that efficiently samples these configurations based on interatomic distances and structural features. This approach greatly enhances the MD simulation stability, enabling nanosecond-scale simulations. We evaluated the performance of our NNP generator in terms of its MD simulation stability and physical properties by applying it to liquid propylene glycol (PG) and polyethylene glycol (PEG). The generated NNPs enable stable MD simulations of systems with >10,000 atoms for 20 ns. The predicted physical properties, such as the density and self-diffusion coefficient, show excellent agreement with the experimental values. This work represents a remarkable advance in the generation of robust and accurate NNPs for organic materials, paving the way for long-duration MD simulations of complex systems. Nanosecond ; Molecular dynamics ; Computer science ; Generator (circuit theory) ; Scale (ratio) ; Dynamics (music) ; Artificial neural network ; Artificial intelligence ; Statistical physics ; Biological system ; Physics ; Chemistry ; Computational chemistry ; Laser ; Biology ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Force Microscopy Techniques and Applications
Authors: Disha Mehta | Pragnesh N. Dave | Vished Kumar
Source: RSC Advances (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Toxic crystal violet dye removal by novel, eco-friendly seablite biochar–ferrite composite: adsorption isotherm, kinetics, and artificial neural network Rising water demand has intensified pollution and created an urgent need for efficient treatment methods. Adsorption is a green and low-cost approach, yet conventional adsorbents often face sustainability and regeneration challenges. In this study, a novel adsorbent was developed by pyrolyzing <i>Suaeda monoica</i> leaf powder (LP) into biochar (BC300), followed by base treatment and coprecipitation with NiCuZnFe<sub>2</sub>O<sub>4</sub> spinel to form a ferrite-biochar composite (FCOB). FCOB effectively removed Crystal Violet (CV) dye from aqueous solution. FE-SEM images revealed a layered morphology, while FTIR analysis confirmed multiple adsorption mechanisms, including hydrogen bonding, electrostatic attraction, surface complexation, and pore filling for CV adsorption. Optimization studies showed maximum CV removal at pH 8 with a 30 mg FCOB dose, maintaining >95% removal up to 200 mg L<sup>-1</sup> dye concentration. For higher concentrations, 150, 250 mg L<sup>-1</sup>, the equilibrium time was 120 min. The Langmuir model indicated monolayer adsorption with a maximum capacity (<i>q</i> <sub>max</sub>) of 325.5 mg g<sup>-1</sup> at 30 °C, whereas the Dubinin-Radushkevich (D-R) model (<i>E</i> < 8 kJ mol<sup>-1</sup>) suggested physical adsorption. Kinetic analysis revealed that the pseudo-second-order (PSO) model best described the process, indicating the chemical nature of CV adsorption onto FCOB, while the Elovich model provided a better fit at higher concentrations, reflecting surface heterogeneity. Thermodynamic parameters confirmed that CV adsorption was spontaneous and endothermic (Δ<i>H</i>° = 49.03 kJ mol<sup>-1</sup>). FCOB retained >275 mg g<sup>-1</sup> capacity after five regeneration cycles, demonstrating good reusability. Artificial neural network (ANN) modeling reliably predicted adsorption performance (<i>R</i> <sup>2</sup> > 0.99) using pH, dye concentration, dose, time, and temperature as inputs. These findings highlight FCOB as an economical, eco-friendly, and scalable adsorbent for dye removal from wastewater. Crystal violet ; Adsorption ; Endothermic process ; Aqueous solution ; Fourier transform infrared spectroscopy ; Monolayer ; Langmuir adsorption model ; Coprecipitation ; Spinel Pigment Synthesis and Properties
Authors: Jiaqi Wang | Sijun Huang | Fanxiang Meng | Qiuting Huang | Xue Xia | Weiqing Xu | Yi Guo | Guokun Liu | Weigang Wang | Ping Li | Shuping Xu
Source: Analytical Chemistry (article, 2025-06-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecule-Responsive SERS Sensors for Urine Diagnosis of Kidney Diseases Enhanced by Neural Networks Early diagnosis of kidney disease is crucial for treatment and prognosis. Compared with kidney biopsy, a noninvasive urine-based diagnosis method of kidney disease can be more convenient and less painful for patients. Urine is closely associated with kidney disease, including nephritis, kidney failure, and kidney cancer. Since it contains various biomolecules, including small-molecule metabolites, proteins, and so on, urine is an appropriate sample for diagnosing and monitoring kidney disease progression. Herein, we developed a liquid biopsy method for diagnosing various kidney diseases based on a specific SERS sensing mode combined with neural network models, which allows an integrated response to multiple types of targets with a single probe and facilitates the detection of complex samples from multiple target groups. Compared to label-free SERS, this method relies on changes in the probe molecule, which facilitates the sensitivity of the assay. Two kinds of silver nanoparticle-casting films assist this method with the surface decoration of molecule-responsive Raman reporter molecules: <i>p</i>-mercaptobenzoic acid (MBA) and <i>p</i>-aminothiophenol (PATP). MBA responds to amino components in urine by SERS spectral changes caused by molecular polarizability. PATP illustrates the level of small-molecule metabolites in urine according to SERS changes resulting from the rate of the hot hole-catalyzed reactions. These interactions were evidenced by density functional theory and molecular docking simulations. Through these two SERS sensors, we acquired the SERS data sets of urine samples and established a classifier by incorporating neural network models, enabling the effective discrimination of healthy and kidney disease samples. The method is helpful for clinical validation and shows promise for use in long-term kidney disease monitoring programs. Chemistry ; Urine ; Kidney ; Biomolecule ; Molecule ; Biochemistry ; Internal medicine ; Medicine ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Renal and Vascular Pathologies ; Biosensors and Analytical Detection
Authors: Alex Kutana | Kōji Shimizu | S. Watanabe | Ryoji Asahi
Source: Scientific Reports (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Representing Born effective charges with equivariant graph convolutional neural networks Graph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-informed network must obey certain transformation rules to ensure the independence of the property on the choice of the reference frame. Here we explicitly encode such properties using an equivariant graph convolutional neural network. The network respects rotational symmetries of the crystal throughout by using equivariant weights and descriptors and provides a tensorial output of the target value. Applications to tensors of atomic Born effective charges in diverse materials including perovskite oxides, Li<sub>3</sub>PO<sub>4</sub>, and ZrO<sub>2</sub>, are demonstrated, and good performance and generalization ability is obtained. Equivariant map ; Convolutional neural network ; Computer science ; Graph ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Topic Modeling ; Neural Networks and Applications
Authors: Mingjian Wen | Wuji Huang | Jin Dai | Sadhan K. Adhikari
Source: npj Computational Materials (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Cartesian atomic moment machine learning interatomic potentials Machine learning interatomic potentials (MLIPs) have substantially advanced atomistic simulations in materials science and chemistry by balancing accuracy and computational efficiency. While leading MLIPs rely on representing atomic environments using spherical tensors, Cartesian representations offer potential advantages in simplicity and efficiency. Here, we introduce the Cartesian Atomic Moment Potential (CAMP), an approach to building MLIPs entirely in Cartesian space. CAMP constructs atomic moment tensors from neighboring atoms and employs tensor products to incorporate higher body-order interactions, providing a complete description of local atomic environments. Integrated into a graph neural network (GNN) framework, CAMP enables physically motivated, systematically improvable potentials. The model demonstrates excellent performance across diverse systems, including periodic structures, small organic molecules, and two-dimensional materials, achieving accuracy, efficiency, and stability in molecular dynamics simulations that rival or surpass current leading models. CAMP provides a powerful tool for atomistic simulations to accelerate materials understanding and discovery. Moment (physics) ; Cartesian coordinate system ; Interatomic potential ; Computer science ; Physics ; Classical mechanics ; Quantum mechanics ; Mathematics ; Geometry ; Molecular dynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Seonghwan Kim | Charles M. Schroeder | Nicholas E. Jackson
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Functional monomer design for synthetically accessible polymers Machine learning (ML) has emerged as a powerful tool to navigate polymer structure-property relationships. Despite recent progress, data sparsity is a major obstacle hindering the practical application of ML in polymer science. In this work, we explore functional monomer design by developing the first comprehensive database of monomer-level chemical and physical properties for approximately 12M synthetically accessible polymers. We generated diverse monomer-level properties by integrating quantum chemistry calculations with active learning to efficiently probe a vast chemical space of synthetically feasible polymers. Monomer-level property descriptors are benchmarked against both higher level computational predictions and experimental data to the extent possible, demonstrating their relevance to polymer design. Our results show that many monomer-level properties are weakly correlated, implying a strong freedom for functional design such that multiple physical properties can be simultaneously optimized by monomer selection. Moreover, the synthetically accessible nature of this chemical space allows targeted monomers to be considered by common polymerization mechanisms to facilitate their synthetic realization. Overall, this work opens new avenues for creating synthetically accessible polymers and provides new insights for designing next generation polymeric materials. Monomer ; Polymer ; Chemical space ; Functional polymers ; Space (punctuation) ; Nanotechnology ; Combinatorial chemistry ; Chemistry ; Computer science ; Materials science ; Organic chemistry ; Polymerization ; Biochemistry ; Drug discovery Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Memory and Neural Computing
Authors: Xiaozhi Wang | Hai‐Long Wu | Tong Wang | Yao Chen | Baoshuo Jia | Huan Fang | Xiaoming Yin | Yanping Zhao | Ru‐Qin Yu
Source: Analytical Chemistry (article, 2025-01-17)
Field: Chemistry — Organic Chemistry

NIRFluor: A Deep Learning Platform for Rapid Screening of Small Molecule Near-Infrared Fluorophores with Desired Optical Properties Small molecule near-infrared (NIR) fluorophores play a critical role in disease diagnosis and early detection of various markers in living organisms. To accelerate their development and design, a deep learning platform, NIRFluor, was established to rapidly screen small molecule NIR fluorophores with the desired optical properties. The core component of NIRFluor is a state-of-the-art deep learning model trained on 5179 experimental big data. First, novel hybrid fingerprints including Morgan fingerprints, physicochemical properties, and solvent properties were proposed. Then, a powerful deep learning model, multitask fingerprint-enhanced graph convolutional network (MT-FinGCN), was designed, which combines fingerprint information and molecule graph structure information to achieve accurate prediction of six properties (absorption wavelength, emission wavelength, Stokes shift, extinction coefficient, photoluminescence quantum yield, and lifetime) of different small molecule NIR fluorophores in different solvents. Furthermore, the "black-box" of the GCN model was opened through interpretability studies. Finally, the well-trained models were placed on the web platform NIRFluor for free use (https://nirfluor.aicbsc.com). Chemistry ; Near-infrared spectroscopy ; Interpretability ; Molar absorptivity ; Deep learning ; Photoluminescence ; Fingerprint (computing) ; Molecule ; Small molecule ; Quantum yield ; Infrared ; Artificial intelligence ; Biological system ; Pattern recognition (psychology) ; Optoelectronics ; Computer science ; Fluorescence ; Materials science ; Optics ; Organic chemistry Click Chemistry and Applications ; Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques
Authors: Van Thuy Hoang | O‐Joun Lee
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Pre-Training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck This study aims to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in acquiring labeled molecules, the previous pre-training methods still rely on semantic subgraphs, i.e., functional groups. Only focusing on the functional groups could overlook the graph-level distinctions. The key challenge to build a pre-trained GNN on molecules is how to (1) generate well-distinguished graph-level representations and (2) automatically discover the functional groups without prior knowledge. To solve it, we propose a novel Subgraph-conditioned Graph Information Bottleneck, named S-CGIB, for pre-training GNNs to recognize core subgraphs (graph cores) and significant subgraphs. The main idea is that the graph cores contain compressed and sufficient information that could generate well-distinguished graph-level representations and reconstruct the input graph conditioned on significant subgraphs across molecules under the S-CGIB principle. To discover significant subgraphs without prior knowledge about functional groups, we propose generating a set of functional group candidates, i.e., ego networks, and using an attention-based interaction between the graph core and the candidates. Despite being identified from self-supervised learning, our learned subgraphs match the real-world functional groups. Extensive experiments on molecule datasets across various domains demonstrate the superiority of S-CGIB. Bottleneck ; Graph ; Computer science ; Induced subgraph isomorphism problem ; Artificial neural network ; Information bottleneck method ; Theoretical computer science ; Artificial intelligence ; Voltage graph ; Line graph ; Cluster analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks