jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunhao Fan | Sheng Zhang | Gia-Wei Chern
Source: arXiv (Cornell University) (preprint, 2026-01-12)
Field: Materials Science — Materials Chemistry

Machine learning nonequilibrium phase transitions in charge-density wave insulators Nonequilibrium electronic forces play a central role in voltage-driven phase transitions but are notoriously expensive to evaluate in dynamical simulations. Here we develop a machine learning framework for adiabatic lattice dynamics coupled to nonequilibrium electrons, and demonstrate it for a gating induced insulator to metal transition out of a charge density wave state in the Holstein model. Although exact electronic forces can be obtained from nonequilibrium Green's function (NEGF) calculations, their high computational cost renders long time dynamical simulations prohibitively expensive. By exploiting the locality of the electronic response, we train a neural network to directly predict instantaneous local electronic forces from the lattice configuration, thereby bypassing repeated NEGF calculations during time evolution. When combined with Brownian dynamics, the resulting machine learning force field quantitatively reproduces domain wall motion and nonequilibrium phase transition dynamics obtained from full NEGF simulations, while achieving orders of magnitude gains in computational efficiency. Our results establish direct force learning as an efficient and accurate approach for simulating nonequilibrium lattice dynamics in driven quantum materials. Non-equilibrium thermodynamics ; Adiabatic process ; Phase transition ; Wave function ; Lattice (music) ; Charge density wave ; Quantum Machine Learning in Materials Science ; Quantum many-body systems ; Organic and Molecular Conductors Research
Authors: Kartik Sau | Ikutaro Hamada | Tamio Ikeshoji | Yiming Lu | Susmita Roy | Shohichi Furukawa | Linda Zhang | Hung Ba Tran | Takahiro Kondo | H. C. Li | Shin-ichi Orimo
Source: ChemRxiv (article, 2026-02-04)
Field: Chemistry — Inorganic Chemistry

Hybrid sampling approach to machine-learning potentials for gas adsorption: Hydrogen adsorption in MOF-303 Porous materials such as metal–organic frameworks (MOFs) are widely studied for applications in catalysis and gas adsorption, including carbon dioxide capture and hydrogen storage for energy and environmental challenges. Accurate simulation of gas adsorption in these materials remains challenging because most approaches rely on classical force fields, which lack first-principles accuracy. Here, we present an approach to generating machine-learning potentials (MLPs) tailored for grand canonical Monte Carlo (GCMC) simulations of gas adsorption. The training dataset combines snapshots from GCMC sampling, evaluated by single-point density functional theory (DFT), with trajectories from DFT-based molecular dynamics. We demonstrate this approach for hydrogen adsorption in MOF-303, achieving accurate energies and forces and robust GCMC performance. The simulation also revealed four primary and four secondary adsorption sites formed near N, O, C, and –OH motifs, highlighting the material’s structural heterogeneity and adsorption behavior. The resulting MLP enables calculation of adsorption isotherms, isosteric heat values, and diffusion coefficients across temperatures. This hybrid-sampling framework bridges DFT-level accuracy and large-scale simulation capability, providing a transferable tool for screening and designing porous materials for gas storage and separation. Adsorption ; Monte Carlo method ; Hydrogen ; Porous medium ; Porosity ; Hydrogen storage ; Diffusion ; Sampling (signal processing) Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Phase Equilibria and Thermodynamics
Authors: P. M. Rybicka | V. M. Ignat'ev | M. Kulik | P. M. Dominiak
Source: Acta Crystallographica Section A Foundations and Advances (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

MATTS2025: machine learning-enhanced expansion of the MATTS data bank for small-molecule electron density modeling Accurate electron density modelling is crucial for determining molecular structures from experimental X-ray and 3D electron diffraction data, as well as for understanding chemical bonding, intermolecular interactions, and calculating properties like electrostatic potential.This is significantly enhanced by using aspherical models, such as the Multipole Model [1], supported by transferable electron density databanks like MATTS [2, 3]. Electron density ; Multipole expansion ; Intermolecular force ; Electron diffraction ; Diffraction ; Electron Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Jiamei Zhao | Siwei Chen | Lijuan He | Wenxi You
Source: Thermal Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Advances in molecular dynamics simulation of phase equilibrium The molecular dynamics simulation method has developed into a more mature simulation tool for calculating the phase equilibria of pure substances and mixtures. This paper offers a detailed account of the fundamentals of the molecular dynamics method and an overview of its development, with an emphasis on the force fields that are closely related to the method. Based on this, the state of development regarding the application of molecular dynamics simulations for the calculation of phase equilibrium data for pure substances and mixtures of matter systems both domestically and internationally is reviewed in a classified manner. In addition, the combination of molecular dynamics simulation with machine learning for phase equilibrium calculations is presented. The limitations inherent in molecular dynamics simulation are objectively highlighted, potential future developments in this field are also envisioned. Molecular dynamics ; Force field (fiction) ; Field (mathematics) ; Phase equilibrium ; Phase (matter) ; Dynamics (music) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Phase Equilibria and Thermodynamics
Authors: Suresh Erannagari | Kuzhaloli.S | Dr. R. Thinesh Kumar | M.R.Nithyaa | Dr. A. M. Arun Mohan | B Veera Jyothi
Source: The Bioscan. (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Intelligent Material Characterization: A ML Approach for Predicting Microstructure of Nanomaterials Nanomaterials are important for many businesses today, such as computer chips and cloud storage devices. A lot of study is being done to make new nanomaterials at the same time, machine learning (ML) is being used more to solve problems in fields like physics, chemical engineering, and manufacturing sector. Because ML is capable of developing in both controlled and unstructured ways, it could help solve many problems in the real world. Using ML techniques for examining at images of nanomaterials is necessary to find out more about them and characterize and analyze their architecture and spectral data, according to the current state of the art. In order to achieve this, researchers presented in this study a ML based approach for analyzing Scanning transmission electron microscopy (STEM) images and spectral data from STEM images of nanomaterials. To analyze STEM images of a nanomaterial, researchers suggested an approach called Machine Learning for STEM Image Analysis (ML-SIA). In order to analyze the spectrum data of a STEM image of a nanomaterial, researchers have introduced an approach called ML for STEM Image spectrum Data Analysis (ML-SISDA). To execute the algorithms into practice and assess the suggested methods, researchers created a prototype ML application. According to experimental findings, ML-based methods are effective for characterizing nanomaterials. Therefore, by spurring more research in the field of material analysis using AI, this research assists in moving this into the future. KeywordsMachine Learning, Nanomaterials, STEM Images, Spectral Data, Chemical Engineering, Manufacturing Sector Image (mathematics) ; Image processing ; Cloud computing ; Field (mathematics) ; Nanomaterials ; Transmission (telecommunications) ; Data processing Machine Learning in Materials Science ; Advanced Technologies and Applied Computing ; Smart Systems and Machine Learning
Authors: Ruifeng Bi | Qibo Zhang | Ruyi Li | Cheng Yang | Haijian Lu | Yirong Deng | Guanyong Su
Source: Analytical Chemistry (article, 2026-03-27)
Field: Chemistry — Organic Chemistry

Database of Chemicals for Organophosphorus (DCOP): An Information Application Platform for Organophosphorus Compounds Organophosphorus compounds (OPCs) are widely applied in diverse industries, yet their continuous release across the full life cycle poses significant risks to human health and ecosystems. Current research on OPCs is largely limited to organophosphate esters (OPEs), with scarce attention paid to structurally diverse OPCs─creating critical gaps in comprehensive environmental screening and risk assessment. To address this gap and facilitate robust OPC analysis, we curated and integrated OPC-related cheminformatics data from academic and public sources, constructing a structured library of OPCs classified by chemical structure. We further prioritized high-production-volume (HPV) OPCs, which are pivotal for targeted environmental research and regulatory focus. A key analytical advancement herein is the development of a lightweight deep learning model based on graph neural networks (GNNs), which extracts molecular structural features to predict tandem mass spectra (MS/MS) data for OPCs. This model directly overcomes the major bottleneck of limited reference MS/MS for most OPCs in existing libraries. These curated resources and predictive capabilities are embedded into DCOP (Database of Chemicals for Organophosphorus)─a high-quality, large-scale, open-access software platform integrating search query, MS/MS prediction, and data download functions. DCOP fills unmet needs in OPC research, providing a valuable tool to advance environmental monitoring, risk assessment, and regulatory decision-making for OPCs globally. Benzene derivatives ; Parts-per notation Organophosphorus compounds synthesis ; History and advancements in chemistry ; Inorganic and Organometallic Chemistry
Authors: A. E. Ushakov | S. V. Dubinskii
Source: COMPOSITES and NANOSTRUCTURES (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

DATA-DRIVEN CONTROLOF MECHANICAL PARAMETERS INCOMPOSITE STRUCTURES The study presents a methodology to assess «material state» in polymer composites based on mathematical statistics and machine learning algorithms. The proposed framework transforms heterogeneous mechanical properties into an integrated digital material model, enabling quantitative characterization of both the reference state and state changes s caused by process induced defects, damage accumulation, or environmental degradation. The approach combines Pearsonian correlation analysis, hierarchical clustering, principal component analysis (PCA), and a multivariate statistical distance criterion suitable for detecting shifts in the material’s structural response. The methodology is validated on two case studies demonstrating how to arrange material properties into a low dimensional PCA-space, reflecting dominant deformation and failure mechanisms (fiber-dominated, matrixdominated, and bearing -dominated modes), along with a procedure for quantifying in-service deterioration. The resulting digital model provides a foundation for adaptive, data-driven material qualification, enabling the development of machine-learning-enhanced normalization and certification frameworks for composite structures. Normalization (sociology) ; Principal component analysis ; Composite number ; Material properties ; Process (computing) ; Digital image correlation ; Deformation (meteorology) ; Statistical analysis Machine Learning in Materials Science ; Mechanical Behavior of Composites ; Composite Material Mechanics
Authors: Victor M. Freixas | Axel Gomez | Yonghao Gu | Amke Nimmrich | Sergei Tretiak | Munira Khalil | Shaul Mukamel | Niranjan Govind
Source: ChemRxiv (article, 2026-03-18)
Field: Chemistry — Physical and Theoretical Chemistry

Time-Resolved X-ray Photoelectron Spectroscopy: A Direct Probe of Bond Order Rearrangements During Ultrafast Excited-State Intramolecular Proton Transfer Excited-state intramolecular proton transfer (ESIPT) provides a minimal yet rich framework for studying bond order rearrangements during chemical reactions.We 1 combine linear-response time-dependent density functional theory, ab initio molecular dynamics, and neural network potentials including nuclear quantum effects to compute time-resolved X-ray photoelectron spectra (tr-XPS) during ESIPT in 10hydroxybenzo[h]quinoline (HBQ) and its deuterated analog (DBQ).By tuning the photoionization probe energy, site-specific sensitivity is achieved through selective ionization of core electrons from either the donor O atom or the acceptor N atom.The tr-XPS signal exhibits a strong dependence on the ESIPT reaction coordinate through changes in photoelectron yield and binding energy.Differences in ESIPT timescales and isotope-dependent oscillatory spectral features between HBQ and DBQ enable direct connections to local bond order rearrangements near the probed X-ray chromophores.These results demonstrate that tr-XPS provides a powerful probe of ESIPT dynamics and highlight its potential for future experimental realization. Intramolecular force ; Photoionization ; Proton ; Ab initio ; Ionization ; Deuterium ; Acceptor ; Density functional theory ; Ab initio quantum chemistry methods Photochemistry and Electron Transfer Studies ; Metal-Catalyzed Oxygenation Mechanisms ; Light effects on plants
Authors: Yeyong Yu | Xilei Bian | Jie Xiong | Xudong Wu | Quan QIAN
Source: npj Computational Materials (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

AIMATDESIGN: knowledge-augmented reinforcement learning for inverse materials design under data scarcity With the growing demand for novel materials, machine learning-driven inverse design methods face significant challenges in reconciling the high-dimensional materials composition space with limited experimental data. Existing approaches suffer from two major limitations: (I) machine learning models often lack reliability in high-dimensional spaces, leading to prediction biases during the design process; (II) these models fail to effectively incorporate domain expert knowledge, limiting their capacity to support knowledge-guided inverse design. To address these challenges, we present AIMATDESIGN, a reinforcement learning framework that augments experimental data via difference-based sampling to build a trusted experience pool. To enhance model reliability, an automated refinement strategy guided by large language models corrects prediction inconsistencies, ensuring better alignment between reward signals and value functions. Additionally, a knowledge-based reward further leverages expert rules to improve stability and efficiency. Experiments show that AIMATDESIGN outperforms conventional machine learning and reinforcement learning baselines in discovery efficiency, convergence speed, and success rates. From numerous candidates, experimental validation of Zr-based alloys yielded a bulk metallic glass with 1.7 GPa yield strength and 10.2% elongation, closely matching predictions. This framework demonstrates reliable performance in capturing property trends and provides a general pathway toward closed-loop, data-efficient materials discovery. Reinforcement learning ; Matching (statistics) ; Stability (learning theory) ; Reliability (semiconductor) ; Function (biology) ; Domain (mathematical analysis) ; Key (lock) ; Convergence (economics) ; Property (philosophy) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: X.P. Liu | Christian Hacker | Shengnian Wang | Yuhua Duan
Source: International Journal of Hydrogen Energy (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

A generative machine learning model for designing metal hydrides applied to hydrogen storage Generative grammar ; Scalability ; Hydrogen storage ; Ranking (information retrieval) ; Key (lock) ; Density functional theory ; Hydride Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction
Authors: Linfeng Zhang | Taoyong Cui | Dongzhan Zhou | Lei Bai | Sufei Zhang | Luca Rossi | Mao Su | Wanli Ouyang | Pheng-Ann Heng
Source: arXiv (Cornell University) (preprint, 2026-03-19)
Field: Materials Science — Materials Chemistry

An SO(3)-equivariant reciprocal-space neural potential for long-range interactions Long-range electrostatic and polarization interactions play a central role in molecular and condensed-phase systems, yet remain fundamentally incompatible with locality-based machine-learning interatomic potentials. Although modern SO(3)-equivariant neural potentials achieve high accuracy for short-range chemistry, they cannot represent the anisotropic, slowly decaying multipolar correlations governing realistic materials, while existing long-range extensions either break SO(3) equivariance or fail to maintain energy-force consistency. Here we introduce EquiEwald, a unified neural interatomic potential that embeds an Ewald-inspired reciprocal-space formulation within an irreducible SO(3)-equivariant framework. By performing equivariant message passing in reciprocal space through learned equivariant k-space filters and an equivariant inverse transform, EquiEwald captures anisotropic, tensorial long-range correlations without sacrificing physical consistency. Across periodic and aperiodic benchmarks, EquiEwald captures long-range electrostatic behavior consistent with ab initio reference data and consistently improves energy and force accuracy, data efficiency, and long-range extrapolation. These results establish EquiEwald as a physically principled paradigm for long-range-capable machine-learning interatomic potentials. Equivariant map ; Polarization (electrochemistry) ; Aperiodic graph ; Reciprocal ; Artificial neural network ; Ab initio ; Inversion (geology) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Quantum many-body systems
Authors: Guiwei Li | Zeyu Yang | Lu Yang | Qiyuan Cao | Aodu Zheng | Wenzheng Wu | Lei Ren | Qin Huang | Luquan Ren
Source: Matter (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Intelligent programming training of multi-dimensional functional transformation biomimetic soft robots Shape-memory alloy ; Robot ; Nickel titanium ; Soft robotics ; Transformation (genetics) ; Smart material ; Adaptation (eye) ; Genetic programming Shape Memory Alloy Transformations ; Advanced Materials and Mechanics ; Soft Robotics and Applications
Source: Molecular Systems Design & Engineering (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Front cover Machine learning highlights chemistry as the key factor in metal- Nucleofection ; Gestational period ; TSG101 ; Liquation ; Dysgeusia ; Diafiltration ; Emperipolesis ; Triacetin ; Durvalumab Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Viviana Dovale-Farelo | Pedram Tavadze | Miguel A. L. Marques | Srinjoy Das | K. K. Choudhary | Alejandro Bautista-Hernández | Aldo H. Romero
Source: npj Computational Materials (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

System-conditioned reparameterization of the SCAN functional for accurate bandgaps: from analytical constraints to machine learning This work investigates how reparametrizing the Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation (XC) functional within density functional theory affects predictions of the electronic bandgap (Eg) for solids. A system dependent functional (SD-SCAN) is proposed by adjusting a subset of SCAN’s internal parameters to improve bandgaps. For most covalent materials, SD-SCAN yields bandgaps closer to experimental values while preserving accurate lattice constants; improvements remain limited for highly ionic systems, reflecting constraints of SCAN’s α-dependence and the absence of long-range nonlocal (Hartree-Fock-like) exchange at the semilocal/meta-GGA level. The modified parameters enhance exchange in regions with covalent character, raising the conduction bands and broadening the charge density, thereby yielding more realistic electronic structures and improved dielectric response. A machine-learning model (ML-SCAN) predicts SCAN parameters from solid-state descriptors, providing a flexible, system-dependent reparametrization strategy competitive with existing semilocal approaches. A simplified variant, SCAN-0.2, offers a fixed-parameter shortcut for improved bandgap calculations. Overall, this study lays the groundwork for ML-driven XC functionals for semiconductors. Density functional theory ; Work (physics) ; Charge (physics) ; Dielectric ; Lattice (music) ; Band gap ; Ionic bonding Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Heusler alloys: electronic and magnetic properties
Authors: Tao Yu | Shengcai Zhu
Source: Matter and Radiation at Extremes (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Hexagonal diamond formation mechanism: A review As a metastable carbon allotrope, hexagonal diamond (HD) exhibits potentially superior mechanical properties to cubic diamond (CD). However, its synthesis faces significant thermodynamic and kinetic challenges. This review summarizes the critical role of computational simulations in the synthesis of HD, covering both static calculations and molecular dynamics (MD) simulations. Static calculations reveal that graphite tends to transform into CD under interface-free conditions, whereas the presence of an interface results in a lower energy barrier for the HD phase transition. With regard to MD simulations, while early shock compression simulations failed to observe HD formation, recent studies based on neural network potentials have confirmed a shock-velocity-dependent transformation pathway and have successfully obtained HD, consistent with in situ experimental results. Hydrostatic pressure simulations emphasize the importance of controlling interlayer sliding, demonstrating that strategies such as quasi-uniaxial compression can promote the preferential formation of HD. In the future, the integration of high-precision simulations with experimental approaches is expected to enable the controllable synthesis of HD, thereby advancing its applications in ultrahard materials, power electronics, aerospace, and other fields. Diamond ; Metastability ; Molecular dynamics ; Hydrostatic equilibrium ; Kinetic energy ; Compression (physics) ; Hexagonal crystal system ; Hydrostatic pressure Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Boron and Carbon Nanomaterials Research