jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ji Qi | Runze Liu | Shyue Ping Ong
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Are Pre-Trained Universal Machine Learning Interatomic Potentials Ready for Solid Electrolyte Discovery? Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond those accessible to conventional first-principles methods. 1 Specifically, for solid electrolyte materials, MLIPs have bridged the gap between ionic conductivities simulated by ab initio molecular dynamics (AIMD) at room temperature and experimental measurements. 2 MLIPs also extend the accuracy of AIMD to systems too computationally demanding for AIMD alone, including grain boundaries, amorphous structures, interfaces, and structures with varying short-range order. 3 Recently, the emergence of universal MLIPs has opened opportunities for discovering new materials at unprecedented scales. 4 In this talk, I will introduce MatPES, a fundamental dataset constructed to enhance the stability and accuracy of universal MLIPs in capturing both thermodynamic and kinetic properties. 5 With benchmark results, I will also attempt to answer the question posed in the title of my talk. Unke, O. T. et al. Machine Learning Force Fields. Chem. Rev. 121 , 10142–10186 (2021). Qi, J. et al. Bridging the gap between simulated and experimental ionic conductivities in lithium superionic conductors. Mater. Today Phys. 21 , 100463 (2021). Lee, T. et al. Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolyte Li0.375Sr0.4375Ta0.75Zr0.25O3. Nat. Commun. 14 , 1940 (2023). Jacobs, R. et al. A practical guide to machine learning interatomic potentials – Status and future. Curr. Opin. Solid State Mater. Sci. 35 , 101214 (2025). Kaplan, A. D. et al. A Foundational Potential Energy Surface Dataset for Materials. Preprint at https://doi.org/10.48550/arXiv.2503.04070 (2025). Bridging (networking) ; Interatomic potential ; Ionic bonding ; Fast ion conductor ; Ab initio ; Electrolyte ; Amorphous solid ; Molecular dynamics ; Ionic conductivity Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Thermal Expansion and Ionic Conductivity
Authors: Arun Sharma
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Machine Learning for Asphaltene Polarizability: Evaluating Molecular Descriptors Polarizability ; Asphaltene ; Artificial intelligence ; Machine learning ; Materials science ; Computer science ; Chemistry ; Organic chemistry ; Molecule Petroleum Processing and Analysis ; Computational Drug Discovery Methods
Authors: Chen Hua | Jing Liu
Source: Journal of Materials Science (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Tree model machine learning to identify liquid metal-based alloy superconductor Materials science ; Superconductivity ; Alloy ; Solid mechanics ; Metal ; Tree (set theory) ; Metallurgy ; Condensed matter physics ; Composite material ; Combinatorics ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Marek Pakosta | Petr Doležel | Roman Svoboda
Source: Logic Journal of IGPL (article, 2025-05-03)
Field: Materials Science — Materials Chemistry

Exploring multi-scale convolutional neural network for modeling amorphous materials behavior: a comparative approach Abstract Glass transitions are a crucial phenomena in amorphous materials with potential for various applications. The Tool–Narayanaswamy–Moynihan model is a widely used empirical model that describes the enthalpy relaxation behavior of these materials. However, determining the appropriate values for its parameters can be challenging. To address the issue, this research explores the use of deep learning to describe the kinetics of enthalpy relaxation and glass transition. More specifically, a multi-scale convolutional neural network (MCNN) model is proposed, which outperforms traditional methods in extracting kinetic information from differential scanning calorimetry data. The MCNN model’s robust architecture captures both low-frequency and high-frequency signal components, making it highly effective for complex pattern recognition in thermal analysis. The study’s findings have significant implications for the utilization of amorphous materials in high-tech and classical engineering applications. Amorphous solid ; Convolutional neural network ; Enthalpy ; Differential scanning calorimetry ; Relaxation (psychology) ; Artificial neural network ; Pattern recognition (psychology) ; Deep learning Material Dynamics and Properties ; Thermal and Kinetic Analysis ; Machine Learning in Materials Science
Authors: J Ão | Wei Wang | ziyang Wang | Qianhong qiu | Changming Du
Source: Research Square (preprint, 2025-10-27)
Field: Materials Science — Materials Chemistry

A method for optimizing catalyst preparation conditions based on machine learning and genetic algorithm Calcination ; Key (lock) ; Boosting (machine learning) ; Decision tree ; Genetic algorithm ; Gradient boosting ; Random forest ; Experimental data Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Cloud Computing and Resource Management
Authors: Hongxing Li | Xiaohua Tian | Wenbin Zhao | Jie Yang | Xiaochuan Wang | Kun Zhang | Jian Li | Changlong Tan
Source: Applied Materials Today (article, 2025-09-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring novel Ni-Mn-X based magnetocaloric materials via machine learning with physical descriptors Magnetic refrigeration ; Entropy (arrow of time) ; Magnetic field ; Magnetic moment ; Energy (signal processing) ; Principle of maximum entropy ; Moment (physics) Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science ; Shape Memory Alloy Transformations
Authors: Pouya Fahimi | Dmitry Vysochinskiy | Mikhail Khadyko
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A neural network-based hardening model for plastic anisotropy evolution in a face centered cubic material Hardening (computing) ; Artificial neural network ; Anisotropy ; Plasticity ; Strain hardening exponent ; Work hardening ; Crystallite Machine Learning in Materials Science ; Elasticity and Material Modeling ; Model Reduction and Neural Networks
Authors: Isaiah
O. Betinol (11949779) | Aleksandra Demchenko (20964685) | Jolene P. Reid (6235811)
Source: Figshare (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Evaluating PredictiveAccuracy in Asymmetric Catalysis:A Machine Learning Perspective on Local Reaction Space Machine learning (ML) models are increasingly being employed in asymmetric catalysis to predict reaction outcomes and optimize enantioselective processes. Despite the trend of expanding data set sizes to improve model performance, asymmetric catalysis presents unique challenges, including the difficulty of acquiring high-quality experimental data and the often-limited availability of structurally diverse examples. Consequently, rational data set design requires the practitioner to choose whether to collect data that maximizes diversity in the training set or data that maximizes representation around a target prediction. A key challenge in these studies is understanding the role of local reaction spacespecifically, how much predictive accuracy is driven by nearest neighbors (structurally and electronically similar data points) and the next-nearest neighbors? This study investigates the predictive power of ML models trained with varying levels of local representation in the reaction space. We provide a framework, a radius-based random forest (RaRF) algorithm, to systematically probe the effects of including diverse reactions dissimilar to a target prediction. We show that when the training set is representative of the target reaction, the gains from increasing data set diversity are modesttypically less than 0.1 kcal/mol in predictive errorand increasing to only 0.5 kcal/mol for extrapolative tests, highlighting the need for targeted data set design. Furthermore, these findings hold even for complex architectures and features. Finally, we demonstrate that a targeted, neighborhood-oriented strategy greatly accelerates the identification of predictive models compared to diversity-driven approaches. Predictive power ; Representation (politics) ; Set (abstract data type) ; Data set ; Identification (biology) ; Training set ; Perspective (graphical) ; Data-driven ; Predictive modelling Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions
Authors: Ming‐Liang Qu | Zhao‐Bin Ding | Dingyue Zhang | Sajjad Foroughi | Huanhao Chen | Zi‐Tao Yu | Jiyizhe Zhang | Lifeng Xiao | Martin J. Blunt | Xiaolei Fan | Qingyang Lin
Source: Advanced Science (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

A Machine Learning‐Driven Pore‐Scale Network Model Coupling Reaction Kinetics and Interparticle Transport for Catalytic Process Design A pore-scale dual-network model is presented with kinetics (DNMK), enhanced by machine learning (ML), for efficient multiscale modeling of reaction-transport coupled catalytic processes in porous systems. In such systems, apparent catalytic performance arises from the intricate interplay between intrinsic microkinetics and inter-particle transport phenomena. By explicitly resolving these coupled effects, DNMK provides mechanistic insight into how spatial particle arrangements and transport limitations govern overall reactor performance. A key innovation of this work is the integration of ML-based surrogates to accelerate the microkinetic module, effectively bridging the large spatial and temporal scale mismatches between transport and catalytic reactions. This hybrid approach achieves up to a 750-fold computational speed-up while preserving full physical and chemical fidelity. The framework is demonstrated for sorption-enhanced CO<sub>2</sub> hydrogenation to methanol, where DNMK identifies optimal catalyst-sorbent configurations that maximize apparent activity and reactor-scale performance. More broadly, DNMK establishes a high-resolution, ML-driven platform for digital catalytic experimentation, enabling predictive, in silico optimization of catalyst scaling, utilization, and process intensification. By allowing rapid, physically consistent evaluation of complex catalytic systems, DNMK reduces reliance on costly experimental trials and opens new pathways for data-driven reactor and process design across diverse chemical engineering applications. Catalysis ; Process (computing) ; Multiscale modeling ; Work (physics) ; Process design ; Bridging (networking) ; Process integration ; Chemical process ; Coupling (piping) Machine Learning in Materials Science ; Catalysts for Methane Reforming ; CO2 Reduction Techniques and Catalysts
Authors: Caden Ginter | Kamal Choudhary | Subhasish Mandal
Source: ArXiv.org (preprint, 2025-10-09)
Field: Materials Science — Materials Chemistry

Accelerated prediction of dielectric functions in solar cell materials with graph neural networks We present an atomistic line graph neural network (ALIGNN) model for predicting dielectric functions directly from crystal structures. Trained on $\sim$7000 dielectric functions from the JARVIS-DFT database computed with a meta-GGA exchange-correlation functional, the model accurately reproduces spectral features, including peak intensities and overall line shapes, while enabling efficient high-throughput screening. Applied to the recently developed Alexandria materials database, containing over four hundred thousand insulating materials, we uncover a clear elemental trend, with vanadium emerging as a strong indicator of materials with high-spectroscopic limited maximum efficiency (SLME). In particular, vanadium-based perovskite materials show a substantially higher fraction of high-SLME compounds compared to the database average, underscoring their promise for optoelectronic applications. Artificial neural network ; Dielectric ; Graph ; Vanadium ; Perovskite (structure) ; Spectral line ; Line (geometry) ; Spectral properties Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chemical and Physical Properties of Materials
Authors: Nicholas Lubbers | Adrián E. Roitberg | Kipton Barros | Andrew E. Sifain | Justin S. Smith | Olexandr Isayev | Benjamin Nebgen | Sergei Tretiak | Andrey Y. Lokhov
Source: UNC Libraries (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks The ability to accurately and efficiently compute quantum-mechanical partial atomistic charges has many practical applications, such as calculations of IR spectra, analysis of chemical bonding, and classical force field parametrization. Machine learning (ML) techniques provide a possible avenue for the efficient prediction of atomic partial charges. Modern ML advances in the prediction of molecular energies [i.e., the hierarchical interacting particle neural network (HIP-NN)] has provided the necessary model framework and architecture to predict transferable, extensible, and conformationally dynamic atomic partial charges based on reference density functional theory (DFT) simulations. Utilizing HIP-NN, we show that ML charge prediction can be highly accurate over a wide range of molecules (both small and large) across a variety of charge partitioning schemes such as the Hirshfeld, CM5, MSK, and NBO methods. To demonstrate transferability and size extensibility, we compare ML results with reference DFT calculations on the COMP6 benchmark, achieving errors of 0.004e<sup>-</sup> (elementary charge). This is remarkable since this benchmark contains two proteins that are multiple times larger than the largest molecules in the training set. An application of our atomic charge predictions on nonequilibrium geometries is the generation of IR spectra for organic molecules from dynamical trajectories on a variety of organic molecules, which show good agreement with calculated IR spectra with reference method. Critically, HIP-NN charge predictions are many orders of magnitude faster than direct DFT calculations. These combined results provide further evidence that ML (specifically HIP-NN) provides a pathway to greatly increase the range of feasible simulations while retaining quantum-level accuracy. Partial charge ; Charge (physics) ; Benchmark (surveying) ; Artificial neural network ; Range (aeronautics) ; Molecular dynamics ; Molecule ; Atomic charge ; Non-equilibrium thermodynamics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Kushal Samanta (1396207) | Arun Mannodi-Kanakkithodi (1611505) | Dibyajyoti Ghosh (1664176)
Source: Figshare (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

InterpretableGraph Neural Network for PredictingTransient Electronic Structures of Semiconductor Quantum Dots Understanding atomic-scale fluctuations in semiconductor quantum dots (QDs) is crucial for optoelectronic material design. We combine <i>ab initio</i> methods and Atomistic Line Graph Neural Networks (ALIGNN) to predict femtosecond time-resolved electronic properties in technologically relevant Cd<sub>28</sub>Se<sub>17</sub>X<sub>22</sub> QDs (X = Cl, OH). These models reveal weaker vibronic coupling in Cl-passivated QDs, highlighting ligand-dependent electron–phonon interactions. ALIGNN models with ensemble learning trained on only ∼10–17% of available data accurately predict bandgap and gap above the conduction band edge (ΔE<sub>gap</sub>) (MAE < 2.8 meV) across long MD trajectories. Transfer learning extends accurate electronic structure predictions to new trajectory segments with minimal retraining. The Feature Nullification Analysis framework uniquely links transient electronic properties, especially trap state formation, to atomic environments. While bandgap dynamics depend on localized atomic sites, ΔE<sub>gap</sub> stems from distributed ones. Such a scalable, atom-resolved methodology efficiently probes long-time-scale quantum dynamics, offering atom-resolved insights for designing optoelectronic nanomaterials. Quantum dot ; Semiconductor ; Band gap ; Transient (computer programming) ; Artificial neural network ; Coupling (piping) ; Electronic structure ; Graph ; Quantum Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Semiconductor Quantum Structures and Devices
Authors: Anyang Wang | Zeyuan Li | Shubo Ren | Xuebin Ke | Xuhao Wan | Ran Han | Xianglian Yan | Wen Wang | Yu Zheng | Yuzheng Guo | Jun Wang
Source: Communications Chemistry (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Probing the thermal decomposition mechanism of CF3SO2F by deep learning molecular dynamics The urgent need to phase out SF<sub>6</sub>, an extremely potent greenhouse gas prevalent in electrical grids, drives the search for eco-friendly insulation alternatives. Trifluoromethanesulfonyl fluoride (CF<sub>3</sub>SO<sub>2</sub>F) emerges as a promising candidate due to its excellent properties. However, understanding its thermal decomposition pathways and products under operationally relevant conditions is critical for evaluating its environmental feasibility and mitigating potential risks upon accidental release or during fault events. This study investigates the thermal decomposition mechanisms of CF<sub>3</sub>SO<sub>2</sub>F using a deep learning potential that combines ab initio accuracy with empirical MD efficiency. By leveraging machine learning driven molecular dynamics, we systematically analyze the yields and components of decomposition products versus temperatures, gas mixing ratios, and buffer gas. The results reveal that the bond-breaking pathways are temperature-dependent, with both elevated temperatures and higher buffer gas mixing ratios promoting its decomposition. Elevated gas pressure enhances the decomposition process by increasing the collision frequency among reactant species. Additionally, N<sub>2</sub> exhibits an inhibitory effect on decomposition under high pressure compared to CO<sub>2</sub>. Experimental validation via a thermal decomposition platform confirms characteristic decomposition products. These findings are pivotal for guiding the rational design and safe deployment of CF<sub>3</sub>SO<sub>2</sub>F to achieve substantial greenhouse gas mitigation in the power industry. Decomposition ; Thermal decomposition ; Process (computing) ; Mixing (physics) ; Molecular dynamics ; Thermal ; Greenhouse gas High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Energetic Materials and Combustion
Authors: Fathya Y. M. Salih | Dinis O. Abranches | Edward J. Maginn | Yamil J. Colón
Source: ChemRxiv (preprint, 2025-02-25)
Field: Chemistry — Physical and Theoretical Chemistry

Open-Source Generation of Sigma Profiles: Impact of Quantum Chemistry and Solvation Treatment on Machine Learning Performance The combination of machine learning (ML) models with chemistry-related tasks requires the description of molecular structures in a machine-readable way. The nature of these so-called molecular descriptors has a direct and major impact on the performance of ML models and remains an open problem in the field. Structural descriptors like SMILES strings or molecular graphs lack size-independence and can be memory intensive. Machine-learned descriptors can be of low dimensionality and constant size but lack physical significance and human interpretability. Sigma profiles, which are unnormalized histograms of the surface charge distributions of solvated molecules, combine physical significance with low dimensionality and size-independence, making them a suitable candidate for a universal molecular descriptor. However, their widespread adoption in ML applications requires open access to sigma profile generation, which is currently not available. This work details the development of an open-source software for generating sigma profiles. Also presented are studies on the effect of different settings on the efficacy of the generated sigma profiles at predicting thermophysical material properties when used as inputs to a Gaussian Process as a simple surrogate ML model. We find that a higher level of theory does not translate to more accurate results. We also provide further recommendations for sigma profile calculation and use in ML models. Sigma ; Open source ; Solvation ; Six Sigma ; Quantum ; Quantum chemistry ; Chemistry ; Computer science ; Artificial intelligence ; Nanotechnology ; Machine learning ; Physics ; Materials science ; Quantum mechanics ; Molecule ; Biochemistry ; Chromatography ; Catalysis ; Reaction mechanism Various Chemistry Research Topics ; Machine Learning in Materials Science
Authors: Xiaohan Ma | Jian Fu | Longlong Fan | Jie Wu | He Qi | Shi Liu | Jun Chen
Source: npj Computational Materials (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Demystifying local polarization configuration evolution for high-piezoelectricity chemical design via deep learning Development of piezoelectric materials through chemical design meets the requirement of the next-generation electronic devices, yet the sensitive piezoelectricity to both chemical components and operational environment call for the trial and error method during material preparation. In order to give an atomic-level understanding about functional unit and assist the chemical design, deep learning was applied to train a novel model based on the most popular BaTiO3 system, as a case study in this work. Through training the atomic force field of calcium and stannum doped solid-solution with Deep Potential method, 3D structure of chemical distribution and corresponding polarization configuration can be constructed for different compositions under different temperatures, which exhibits a high degree of consistency with the local structure quantitatively analyzed from HAADF STEM and reverse Monte Carlo refinement of neutron total scattering data, especially for the critical composition with ultrahigh piezoelectricity of d33 ~ 860 pC/N. Systemic analysis reveals that variations in chemical bond length among various elements with oxygen elements are the primary factors influencing ferroelectric activity and leading to structural evolution. The results and methodology can facilitate the discovery of new ferroelectrics and the design of high-performance piezo/ferroelectrics with atomic-level insights. Piezoelectricity ; Ferroelectricity ; Polarization (electrochemistry) ; Consistency (knowledge bases) ; Reverse Monte Carlo ; Monte Carlo method ; Neutron scattering ; Deep learning Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Chengcheng Liu | Jianfa Feng | Hang Su
Source: Journal of Magnetism and Magnetic Materials (article, 2025-10-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The machine learning-based establishment of a predictive model for the soft magnetic properties of FeSiAl alloy Relative permeability ; Permeability (electromagnetism) ; Coercivity ; Alloy ; Electrical resistivity and conductivity ; Autoencoder ; Magnetic alloy Magnetic Properties and Applications ; Metallurgical Processes and Thermodynamics ; Microstructure and Mechanical Properties of Steels