jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Circe Hsu | Claire Schlesinger | Karan Ravi Mudaliar | Jordan Leung | Robin Walters | Peter Schindler
Source: Advanced Intelligent Discovery (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

FIRE‐GNN: Force‐Informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties The work function and cleavage energy of a surface are critical properties that determine the viability of materials in electronic emission applications, semiconductor devices, and heterogeneous catalysis. While first‐principles calculations are accurate in predicting these properties, their computational expense, combined with the vast search space of surfaces, makes a comprehensive screening approach with density functional theory (DFT) infeasible. Here, we introduce FIRE‐GNN (Force‐Informed, Relaxed Equivariance Graph Neural Network), which integrates surface‐normal symmetry breaking and machine learning interatomic potential (MLIP)‐derived force information, achieving a twofold reduction in mean absolute error (down to 0.065 eV) over the previous state‐of‐the‐art for work function prediction. We additionally benchmark recent invariant and equivariant architectures, analyze the impact of symmetry breaking, and evaluate out‐of‐distribution generalization, demonstrating that FIRE‐GNN consistently outperforms competing models for work function predictions. This model enables accurate and rapid predictions of the work function and cleavage energy across a vast chemical space and facilitates the discovery of materials with tuned surface properties. Equivariant map ; Invariant (physics) ; Benchmark (surveying) ; Surface (topology) ; Artificial neural network ; Function (biology) ; Curvature ; Work (physics) ; Symmetry (geometry) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Xiaonan Shan | Uttam Gupta | N. Ravi | Jiefu Chen | Lars C. Grabow | Richard Tran
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

The Challenges and Opportunities in Automatic Electrochemical Catalysts Synthesis for High Throughput Catalyst Screening The advancement of human civilization has consistently relied on the discovery of new materials, driving exponential progress in energy, medicine, environmental restoration, food and water security, and transportation. However, traditional material discovery methods have largely been based on slow, resource-intensive trial-and-error approaches, often referred to as Edisonian methods. To accelerate innovation, high-throughput, autonomous materials design, synthesis, and characterization are essential. In this study, we present our efforts to develop an automated high-throughput catalyst synthesis and evaluation system for different electrochemical catalyst screening, including oxygen evolution reaction (OER), hydrogen evolution reaction (HER) and ethanol oxidation reaction (EOR). The system employs robotic arms to precisely control and mix catalyst precursors and to manage electrode bundle movements for catalyst deposition. Following deposition, the performance of the catalysts is systematically evaluated and recorded. We used the system to screen ternary metal alloys, metal-metal-phosphors, and quaternary alloy. The metal alloy system selection is guided by a machine learning model, which paves the way for the fully automated material and catalysts discovery. We will discuss the steps taken to ensure consistent and reliable results using this platform, along with demonstration experiments on metal alloy synthesis for OER applications. Catalysis ; Characterization (materials science) ; Electrochemistry ; Oxygen evolution ; Throughput ; Process (computing) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Nanoporous metals and alloys
Authors: Jian Chen | Fei Zhou | Shumin Han | Xin Chen | Mingqing Liao | Hongli Wang | Yong Cao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning accelerate the design of low cost and high strength Cu-Zn alloys based on the small sample dataset Tin ; Alloy ; Consistency (knowledge bases) ; Electrical resistivity and conductivity ; Sizing ; Aluminium ; Stacking ; Sample (material) Metallurgy and Material Science ; Metallurgical and Alloy Processes ; Electrodeposition and Electroless Coatings
Authors: Tingyang Xing | Panying Mao | Shixian Wang | Hao Ye | Shieh‐Yueh Yang | Weikang Jiang | Xiaozhong Qiu | Yanling Shi | Lijun Wu
Source: Analytical Chemistry (article, 2025-09-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

GSH and Halides Directed Controllable Synthesis of Chiral Gold Nanostars for Sensitive Diagnosis of Nephritis Types Using SERS and Transformer Neural Network Surface-enhanced Raman spectroscopy (SERS) has shown potential for early disease diagnosis via urinary metabolomics, but still faces challenges in achieving stable hot spots and processing complex clinical data. In this study, the preparation of chiral gold nanostars with precisely controllable branch size, number, and sharpness was realized by investigating the effects of l-GSH and CTA<i>X</i> (<i>X</i> indicates halides) on site occupancy, reduction rate, and selective adsorption on crystal facets. Raman spectroscopic characterization using rhodamine 6G (R6G) as a reporter molecule revealed that nanoparticles with fewer branches, larger branch bases, and smoother surfaces exhibited excellent SERS activity, with an analytical enhancement factor (AEF) of 5.49 × 10<sup>6</sup> in the nonresonance region, which was also supported by finite-difference time-domain (FDTD) calculations. The hierarchical self-assembly of gold nanostructures was achieved by introducing a hydrophobic template to control the substrate size, significantly improving the reproducibility of SERS substrates. Combined with a Transformer neural network, this study achieved 99.94% diagnostic accuracy in distinguishing urine samples from healthy individuals and patients with acute interstitial nephritis and nonacute interstitial nephritis, demonstrating the potential of SERS and deep learning in clinical diagnostics. Rhodamine 6G ; Colloidal gold ; Raman spectroscopy ; Molecule ; Clinical diagnosis ; Nanoparticle ; Nanostructure ; Cystatin C Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Heng Ni | Yuling Han | Yijun Zhang | Xiaodong Yang | Yang Shen
Source: Solar Energy Materials and Solar Cells (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Synergistic machine learning and DFT driven high-throughput screening strategy for A2BX6-Type inorganic perovskite photovoltaic materials Photovoltaic system ; Band gap ; Throughput ; Perovskite (structure) ; Stability (learning theory) ; Density functional theory ; Process (computing) ; Energy (signal processing) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Divya Suman (18076284) | Jigyasa Nigam (18076287) | Sandra Saade (12129514) | Paolo Pegolo (18793445) | Hanna Türk (13983245) | Xing Zhang (11943) | Garnet Kin-Lic Chan (1333134) | Michele Ceriotti (1301079)
Source: Figshare (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Exploring the Design Space of Machine Learning Modelsfor Quantum Chemistry with a Fully Differentiable Framework Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities directly from compositions and geometries of atomic configurations. With the emergence of ML approaches to predict the “ingredients” of a QM calculation, such as the ground-state charge density or the effective single-particle Hamiltonian, it has become possible to obtain multiple properties through analytical physics-based operations on these intermediate ML predictions. We present a framework that seamlessly integrates the prediction of an effective electronic Hamiltonian, for both molecular and condensed-phase systems, with PySCFAD, a differentiable QM workflow. This integration facilitates training models indirectly against functions of the Hamiltonian, such as electronic energy levels, dipole moments, polarizability, etc. We then use this framework to explore various possible choices within the design space of hybrid ML/QM models, examining the influence of incorporating multiple targets on model performance and learning a reduced-basis ML Hamiltonian that can reproduce targets computed on a much larger basis. Our benchmarks evaluate the accuracy and transferability of these hybrid models, compare them against predictions of atomic properties from their surrogate models, and provide indications to guide the design of the interface between the ML and QM components of the model. Differentiable function ; Hamiltonian (control theory) ; Dipole ; Quantum ; Quantum chemical ; Space (punctuation) ; Quantum chemistry ; Interface (matter) ; Software Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Manisha Kumari | Alok Shukla
Source: Computational Materials Science (article, 2025-12-13)
Field: Materials Science — Materials Chemistry

Machine learning-driven prognostication of multifunctional properties of (K,Na)NbO3-based lead-free ceramics for optimized materials design Interpretability ; Dielectric ; Ceramic ; Piezoelectricity ; Curie temperature ; Generalization ; Material properties ; Feature (linguistics) Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis
Authors: Sri Vishnuvardhan Reddy Akepati | Nitant Gupta | J. Shah | Stephen Kronenberger | Veena Venkat | Rohan Adhikari Sridhar | Simona Bianco | Dave J. Adams | Arthi Jayaraman
Source: ACS Measurement Science Au (article, 2025-11-26)
Field: Materials Science — Biomaterials

Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged one-dimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D-) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2D-SAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use. Small-angle X-ray scattering ; Scattering ; Nanostructure ; Small-angle scattering ; Anisotropy ; Segmentation Supramolecular Self-Assembly in Materials ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Zheng Cao | Xiaoyuan Wang | Guorui He | Yichong Li | Yang Yang | Yufei Wang | Haiyan Gao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Facilitating creep-resistant Al alloy design through a physics-aware integrated machine learning pipeline Interpretability ; Pipeline (software) ; Probabilistic logic ; Curse of dimensionality ; Stability (learning theory) ; Creep ; Matching (statistics) ; Stress (linguistics) ; Volume fraction Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Nuclear Materials and Properties
Authors: Yuxuan Liao | Ruixi Chen | Ting Xie | Chuanxiu Yu | Jiongyu Wang | Zhimin Zhang | Hongmei Lü
Source: Chemometrics and Intelligent Laboratory Systems (article, 2025-11-20)
Field: Chemistry — Spectroscopy

SigmaCCS2: Collision cross section prediction using molecular topological and geometric information with graph neural network ENCODE ; Topology (electrical circuits) ; Molecular graph ; Generalization ; Graph ; Artificial neural network ; Dual (grammatical number) ; Collision Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Mei Yang | Changxin Wang | Minhui Song | Lu Xie | Xiao‐Xu Wang | Ping Qian | Yanjing Su
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

First-Principles and Machine Learning-Driven Design of Efficient Electrocatalysts for Lithium-Sulfur Batteries: Metal Atom-Doped Tin/Ti3c2br Heterostructures Tin ; Heterojunction ; Doping ; Lithium (medication) ; Materials science ; Atom (system on chip) ; Sulfur ; Metal ; Lithium metal ; Nanotechnology ; Chemical engineering ; Optoelectronics ; Chemistry ; Computer science ; Metallurgy ; Engineering ; Physical chemistry ; Electrode ; Embedded system ; Anode MXene and MAX Phase Materials ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Dmitriy D. Matyushin
Source: Журнал физической химии / Russian Journal of Physical Chemistry (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Applying molecular similarity used for evaluating the accuracy of retention index predictions in gas chromatography using deep learning When predicting retention indices using deep learning, there is usually no way to assess the reliability of the prediction for a particular molecule. In this work, using stationary phases based on polyethylene glycol and the NIST 17 database as an example, it is shown that, on average, the closer the molecule in the training data set is to the compound being predicted, the more accurate the prediction. Tanimoto similarity of “molecular fingerprints” ECFP is the most appropriate molecular similarity calculation algorithm for this problem among the four considered. It is shown that for a number of transformation products of unsymmetrical dimethylhydrazine, whose structure was established using this prediction, it could be very unreliable. Similarity (geometry) ; Pattern recognition (psychology) ; Reliability (semiconductor) ; Data set ; Set (abstract data type) ; Kovats retention index ; Deep learning ; Transformation (genetics) Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Guobing Zhou | Taiping Hu | Bin Jin | Fangjia Fu | Haoyu Wang | Hao Zhang | Zhen Yang | Shenzhen Xu
Source: ChemRxiv (preprint, 2025-07-07)
Field: Materials Science — Materials Chemistry

Atomic Resolution of Solid Electrolyte Interphase Formation via Off-Lattice On-the-Fly Kinetic Monte Carlo Complex reactions occurring at the electrode/electrolyte interface in Li metal batteries lead to the formation of a solid electrolyte interphase (SEI), which plays a vital role in stabilizing batteries’ performance. An in-depth understanding of the SEI formation mechanism thus is crucial for battery optimization. Kinetic Monte Carlo (KMC) simulations can offer detailed information on the interfacial reaction processes over multiple timescales. However, conventional KMC methods employing on-lattice models and pre-defined event lists are inappropriate for modeling SEI formation due to the complex chemical environment at materials interfaces. In this work, we propose an off-lattice on-the-fly KMC (OTF-KMC) method integrated with a machine learning force field (MLFF) to investigate SEI growth at atomic-level. We study a system comprising a Li(100) surface and an electrolyte containing the ethylene carbonate (EC) and Li salts, and identify various decomposition pathways of EC molecules and PF_6^- anions to generate SEI. Our results show that the SEI features a spatial distribution of an inorganic inner layer near the Li electrode with organic products relatively far from the surface, aligning closely with experimental evidences. More importantly, we present a versatile and robust computational framework for modeling interfacial reactions in electrochemical systems. Interphase ; Monte Carlo method ; Kinetic Monte Carlo ; Electrolyte ; Kinetic energy ; On the fly ; Lattice (music) ; Statistical physics ; Materials science ; Dynamic Monte Carlo method ; Chemical physics ; Physics ; Chemistry ; Physical chemistry ; Computer science ; Electrode ; Mathematics ; Classical mechanics ; Statistics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advancements in Solid Oxide Fuel Cells
Authors: Minh Thanh Chung
Source:  (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Reproducibility Report for SC25 Paper TensorMD: Accelerating Molecular Dynamics with a High-Performance Machine Learning Interatomic Potential This reproducibility report provides details about the artifact evaluation done with regard to the Artifact Description and Evaluation appendix of the SC25 paper TensorMD: Accelerating Molecular Dynamics with a High-Performance Machine Learning Interatomic Potential by Rongfen Lin et al. The work was done as part of the Reproducibility Initiative of SC25. The author is a member of the SC25 Reproducibility Committee. Reproducibility ; Artifact (error) ; Molecular dynamics ; Interatomic potential ; Work (physics) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Electron Microscopy Techniques and Applications
Authors: Huo, Songze | Cao, Xiao-Ming
Source: ArXiv.org (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Accelerating High-Throughput Catalyst Screening by Direct Generation of Equilibrium Adsorption Structures The adsorption energy serves as a crucial descriptor for the large-scale screening of catalysts. Nevertheless, the limited distribution of training data for the extensively utilised machine learning interatomic potential (MLIP), predominantly sourced from near-equilibrium structures, results in unreliable adsorption structures and consequent adsorption energy predictions. In this context, we present DBCata, a deep generative model that integrates a periodic Brownian-bridge framework with an equivariant graph neural network to establish a low-dimensional transition manifold between unrelaxed and DFT-relaxed structures, without requiring explicit energy or force information. Upon training, DBCata effectively generates high-fidelity adsorption geometries, achieving an interatomic distance mean absolute error (DMAE) of 0.035 \textÅ on the Catalysis-Hub dataset, which is nearly three times superior to that of the current state-of-the-art machine learning potential models. Moreover, the corresponding DFT accuracy can be improved within 0.1 eV in 94\% of instances by identifying and refining anomalous predictions through a hybrid chemical-heuristic and self-supervised outlier detection approach. We demonstrate that the remarkable performance of DBCata facilitates accelerated high-throughput computational screening for efficient alloy catalysts in the oxygen reduction reaction, highlighting the potential of DBCata as a powerful tool for catalyst design and optimisation. Adsorption ; Interatomic potential ; Refining (metallurgy) ; Catalysis ; Potential energy ; Artificial neural network ; Outlier ; Molecular dynamics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Qipeng Wang | Di Gao | Peng Liu | Qingdong Zhang | Xiaoxing Yin
Source:  (article, 2025-11-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Assisted Two-Line Method for Extracting the Dielectric Constant of Nematic Liquid Crystals This paper presents a machine learning-assisted two-line method (MLA-TLM) for extracting the dielectric constant of nematic liquid crystals (NLCs) in the microwave range. Two inverted microstrip lines (IMSLs) with different physical lengths are designed to measure the phase difference and insertion loss difference caused by the NLC. To address the limitations of conventional closed-form equations in characterizing the multilayer IMSL structure, a multilayer perceptron (MLP) model is developed to directly map the measured differences to the relative permittivity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\varepsilon_{r})$</tex> and loss tangent (tanδ), The IMSL prototypes, fabricated using standard liquid crystal display technology, exhibit reflection coefficients below −10 dB from 9 GHz to 12.5 GHz. Validation against reference data shows maximum errors of 2.2% for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\varepsilon_{r})$</tex> and 14.5% for tanδ, demonstrating the effectiveness of the proposed method in mitigating analytical approximations for complex electromagnetic coupling. Liquid crystal ; Permittivity ; Dielectric ; Dissipation factor ; Relative permittivity ; Microwave ; Microstrip ; Phase (matter) ; Reflection (computer programming) Liquid Crystal Research Advancements ; Molecular spectroscopy and chirality ; Material Dynamics and Properties
Authors: Yuxiang Han | Xuerong Hu | Junyan Lu | Linxiang Wang | Roderick Melnik
Source: Applied Physics A (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Phenomenological modeling for ferromagnetic shape memory alloys with lumped-element formulation and artificial neural network Artificial neural network ; Shape-memory alloy ; Ferromagnetism ; Phenomenological model ; Element (criminal law) ; Materials science ; Computer science ; Biological system ; Artificial intelligence ; Topology (electrical circuits) ; Statistical physics ; Physics ; Condensed matter physics ; Engineering ; Electrical engineering Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials
Authors: Chengxi Zhao | Linjiang Chen
Source: Scientia Sinica Chimica (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

Recent developments in organic molecular crystal structure prediction methods and applications: traditional techniques and machine learning 晶体结构预测(Crystal Structure Prediction, CSP)技术能够仅依赖分子式预测材料的晶体结构，其在识别稳定结构和探索多晶型方面展现出独特的优势，已成为材料科学、药物学等领域不可或缺的工具。自20世纪末以来，CSP方法经历了从初期侧重技术实现的探索，到逐步实现高通量精确计算的阶段，并发展为一种能够全面探索高维势能面、精确排序分子晶体能量的综合性算法。本文综述了有机分子CSP的主要方法及策略，同时介绍了机器学习等新技术在CSP领域的引入和应用情况，并讨论了这些技术展现出的巨大潜力。本文旨在为读者提供一个全面、系统的CSP技术进展回顾，探讨当前的应用现状与挑战，并展望机器学习为该领域带来的新机遇，促进CSP技术在多领域的深入应用和跨学科融合。 Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions