jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xijun Wang | Xiaoliang Wang | Xiaoyi Zhang | Zhao Li | Jiayang Liu | Faramarz Joodaki | Kaihang Shi | Filip Formalik | Omar K. Farha | Daniela Kohen | Randall Q. Snurr
Source: The Journal of Physical Chemistry C (article, 2026-02-09)
Field: Chemistry — Inorganic Chemistry

Machine Learning Interatomic Potentials for Modeling Framework Flexibility and Water Uptake in NbOFFIVE-1-Ni Metal–Organic Framework Metal–organic frameworks (MOFs), with their distinctive porous structures and tunable chemical properties, have shown immense promise in the separation and storage of gases. Currently, the accurate simulation of their adsorptive properties remains challenging, especially for systems where the molecules fit very tightly into the pores. Traditional simulation methods often approximate the frameworks as rigid and do not account for the framework flexibility seen in materials such as NbOFFIVE-1-Ni. First-principles molecular dynamics (FPMD) simulations offer the desired accuracy in modeling this flexibility but are limited by their extensive computational demands, rendering them impractical for long simulations. Conversely, classical force field-based simulations offer computational efficiency but lack the necessary accuracy. To break this accuracy-efficiency trade-off, we have developed machine learning interatomic potentials trained on energies and forces from FPMD to model the framework flexibility of NbOFFIVE-1-Ni in the presence of water over nanosecond time scales. Furthermore, by integrating MLIP-driven molecular dynamics (MLIP-MD) with grand canonical Monte Carlo (GCMC) simulations, we further incorporated framework flexibility into adsorption predictions, yielding water adsorption isotherms that better align with experimental data compared to those of conventional GCMC simulations. These advances offer new opportunities for the design and optimization of MOFs in gas storage and separation applications. Flexibility (engineering) ; Molecular dynamics ; Monte Carlo method ; Rendering (computer graphics) ; Water model ; Interatomic potential Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics ; Carbon Dioxide Capture Technologies
Authors: Seiki Saito | Keisuke TAKEUCHI | Hiroaki Nakamura | Yasuhiro Oda | Kazuo Hoshino | Yuki Homma | S. Yamoto | Yuki Uchida
Source: arXiv (Cornell University) (preprint, 2026-04-07)
Field: Materials Science — Materials Chemistry

Development of a 3D-CNN-based Prediction Model for Migration Barriers in Plasma-Wall Interactions Understanding the long-term transport of hydrogen isotopes in plasma-facing materials, such as tungsten, is critical for the steady-state operation of magnetic confinement fusion reactors. However, dynamically updating the transition parameters for kinetic Monte Carlo (kMC) simulations as the atomic structure evolves under continuous plasma irradiation remains a severe computational bottleneck. Conventionally, calculating these migration barriers requires the iterative and computationally expensive Nudged Elastic Band (NEB) method. To overcome this limitation, this article presents a highly efficient surrogate model for predicting migration barriers using a three-dimensional Convolutional Neural Network (3D-CNN), establishing the final component necessary to realize on-the-fly molecular dynamics (MD) and kMC hybrid simulations. The proposed deep learning model takes a two-channel volumetric input, the local three-dimensional potential energy distribution and the voxelized spatial coordinates of the initial and final trapping sites, to directly output the migration barrier as a scalar value. Trained on a comprehensive dataset of tungsten-hydrogen configurations evaluated using the Embedded Atom Method (EAM) potential, the model demonstrated robust predictive accuracy, achieving a Mean Absolute Error (MAE) of 0.124 eV and a high coefficient of determination of 0.890. Furthermore, utilizing GPU acceleration, the inference time is reduced to approximately 2.7 milliseconds per barrier, achieving a speed-up ratio of over 23,000 compared to conventional NEB calculations. This extraordinary acceleration effectively resolves the computational barrier of transition rate evaluations, paving the way for large-scale, dynamic modeling of plasma-wall interactions. Convolutional neural network ; Scalar (mathematics) ; Inference ; Monte Carlo method ; Acceleration ; Molecular dynamics ; Artificial neural network ; Fusion Fusion materials and technologies ; Magnetic confinement fusion research ; Laser-Plasma Interactions and Diagnostics
Authors: Zincia Sherin | Omar Bashir | Imdadul Hoque Mondal | Prashant Anil Pawase
Source: Trends in Food Science & Technology (article, 2026-01-11)
Field: Chemistry — Analytical Chemistry

Advances in Hyperspectral Imaging for Spoilage Detection in Meat and Poultry: A Non-Invasive Approach Hyperspectral imaging ; Partial least squares regression ; Food spoilage ; Principal component analysis ; Pattern recognition (psychology) ; Calibration ; Convolutional neural network Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Advanced Chemical Sensor Technologies
Authors: Mohamed RJILI
Source: Open MIND (article, 2026-02-27)
Field: Materials Science — Biomaterials

Supramolecular architectures on a torus: geometrically modulated chiral phases and the impact of elastic anisotropy The control of supramolecular self-assembly on curved manifolds represents a challenge in soft matter. This work investigates chiral nematic phases confined to a toroidal geometry, where intrinsic helical twisting power competes with spatially varying curvature. We employ Physics-Informed Neural Networks (PINNs) to simulate self-assembly of chiral host-guest systems, mapping interactions between molecular chirality (q0), saddle-splay elasticity (K24), and bulk elastic anisotropy (K11, K22, K33). Our simulation results unveil a phase diagram governed by a ”geometric clamp” mechanism. Specifically, the saddle-splay potential – arising from coupling between the director field and negative Gaussian curvature – locally suppresses chiral induction. This yields spatially heterogeneous ground states where curvature-pinned achiral domains coexist with frustrated helical textures. We demonstrate that tuning the material’s elastic anisotropy offers a chemical route to select the orientation of these supramolecular domains, providing design principles for curvature-directed functional materials. Anisotropy ; Supramolecular chemistry ; Gaussian curvature ; Liquid crystal ; Curvature ; Elasticity (physics) ; Chirality (physics) ; Coupling (piping) ; Phase diagram Supramolecular Self-Assembly in Materials ; Surface Chemistry and Catalysis ; Liquid Crystal Research Advancements
Authors: Christoforos-Nikitas Kasimatis | Christos Lekarakos | Nikolaos Katsenios | Evangelos Psomakelis | Panagiotis Sparangis | Gerassimos G. Peteinatos | Dimitrios Apostolou | Aspasia Efthimiadou
Source: Smart Agricultural Technology (article, 2026-01-12)
Field: Chemistry — Analytical Chemistry

A predictive system for the classification of cereal species and varieties • Developed a method using Convolutional Neural Networks to automate the identification of cereal grains. • Collected 13.854 high-resolution grain images across four cereal species and six commercial varieties for model training and evaluation. • Evaluated three CNN architectures (VGG16, ResNet50, and Xception) to benchmark classification performance. • ResNet50 and Xception models achieved 97% test accuracy on variety level. Cereals are fundamental to global food supply, and their seeds are the raw materials for many food industries. Grain industry testing and certification workflows include classification of cereal species. Traditional identification relies on expert’s knowledge and is conducted through manual visual assessment, a time-consuming process which can often be subjective. This study introduces a deep learning-based method utilizing Convolutional Neural Networks (CNNs), specifically VGG16, ResNet50, and Xception, to automate cereal classification. Using a digital camera, we captured cereal grain images from four different species (soft wheat, durum wheat, barley, oat) and six varieties (Soft Wheat: Sellas, Durum Wheat: Levante, Barley: Triptolemos and Dimitra, Oat: Flega and Pigasos) resulting in 13.854 image samples for model training and evaluation. The CNN models achieved test accuracies of 65%, 97% and 97%, for VGG16, ResNet50 and Xception respectively. Our findings demonstrate that CNN-based classification offers a reliable and efficient alternative to manual grain inspection, accurately distinguishing between different species and varieties. The outcomes of the proposed approach are both accurate and dependable, encouraging its practical implementation. Convolutional neural network ; Identification (biology) ; Workflow ; Pattern recognition (psychology) ; Benchmark (surveying) ; Cereal grain ; Artificial neural network ; Process (computing) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Biosensors and Analytical Detection
Authors: Li Cheng | Yuehui Xian | Yumei Zhou | Xiangdong Ding | Jun Sun | Dezhen Xue
Source: Advanced Materials (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

Generative Design for Alloys: Harnessing Generative Models for Faster Discovery Generative models are redefining alloy design by moving beyond property prediction toward the autonomous creation of new compositions, processing parameters, microstructures, and architectures. Unlike conventional machine learning methods that map material descriptors to properties, generative frameworks learn the underlying probability distributions across composition, processing, and microstructure. This capability enables efficient exploration of vast design spaces while reducing the risk of local optimization. This review establishes a unified framework linking metallurgical objectives with generative modeling tasks, encompassing property optimization, inverse design for target properties, and microstructure or architecture generation. We detail how generative models address these tasks by outlining methodological foundations, highlighting representative case studies, and assessing both their strengths and limitations. Key challenges, including data scarcity, experimental uncertainty, and limited interpretability, are discussed alongside emerging opportunities in optimization-driven workflows, active learning, and automated experimentation. Together, these advances position generative modeling as a cornerstone of accelerated and autonomous alloy discovery. Generative grammar ; Generative Design ; Generative model ; Property (philosophy) ; Key (lock) ; Architecture ; Cornerstone Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Additive Manufacturing Materials and Processes
Authors: Anonymous | Shin Kiyohara | Yu Kumagai
Source: Physical Review X (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Physics-Based Factorized Machine Learning for Predicting Ionic Dielectric Tensors Considerable effort continues to be devoted to the exploration of next-generation high- <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mrow> <a:mi>κ</a:mi> </a:mrow> </a:math> materials that combine a high dielectric constant with a wide band gap. However, machine learning (ML)-based virtual screening has remained challenging, primarily due to the low accuracy in predicting the ionic contribution to the dielectric tensor, which dominates the dielectric performance of high- <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:mi>κ</c:mi> </c:math> materials. We propose a joint ML model that predicts Born effective charges using an equivariant graph neural network, and phonon properties using a highly accurate pretrained ML potential. The ionic dielectric tensor is then computed analytically from these quantities. This approach significantly improves the accuracy of ionic contribution. Using the proposed model, we successfully identified 31 novel high- <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"> <e:mi>κ</e:mi> </e:math> oxides from a screening pool of over 8000 candidates. Dielectric ; Artificial neural network ; Support vector machine ; Ionic bonding ; Computational learning theory Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Xincheng Miao | Roland Mitrić
Source: Open MIND (preprint, 2026-02-12)
Field: Materials Science — Materials Chemistry

A Hardware-Native Realisation of Semi-Empirical Electronic Structure Theory on Field-Programmable Gate Arrays High-throughput quantum-chemical calculations underpin modern molecular modelling, materials discovery, and machine-learning workflows, yet even semi-empirical methods become restrictive when many molecules must be evaluated. Here we report the first hardware-native realisation of semi-empirical electronic structure theory on a field-programmable gate array (FPGA), implementing as a proof of principle Extended Hückel Theory (EHT) and non-self-consistent Density Functional Tight Binding (DFTB0). Our design performs Hamiltonian construction and diagonalisation on the FPGA device through a streaming dataflow, enabling deterministic execution without host intervention. On a mid-range Artix-7 FPGA, the DFTB0 Hamiltonian generator delivers a throughput over fourfold higher than that of a contemporary server-class CPU. Improvements in eigensolver design, memory capacity, and extensions to nuclear gradients and excited states could further expand capability. Combined with the inherent energy efficiency of FPGA dataflow, this work opens a pathway towards sustainable, hardware-native acceleration of electronic-structure simulation and direct hardware implementations of a broad class of methods. Realisation ; Field-programmable gate array ; Hamiltonian (control theory) ; Class (philosophy) ; Topology (electrical circuits) ; Generator (circuit theory) ; Electronics ; Density functional theory Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum-Dot Cellular Automata
Authors: Federico Grasselli | Kevin Rossi | Stefano de Gironcoli | Andrea Grisafi
Source: ArXiv.org (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Long-range electrostatics in atomistic machine learning: a physical perspective The inclusion of long-range electrostatics in atomistic machine learning (ML) is receiving increasing attention for achieving quantum-mechanical accuracy in predicting a wide range of molecular and material properties. However, there is still no general prescription on how long-range physical effects should be incorporated into the model while preserving well-established locality principles underlying most transferable ML representations. Here, we provide a physical perspective on the problem, by discussing how distinct contributions to the system's electrostatics can be captured through the adoption of different learning paradigms. Specifically, we discern between local charge models, which rely either on explicit charge-density decompositions or implicit auxiliary variables, and models where a notion of nonlocality is deliberately introduced, either via self-consistent procedures or by using nonlocal descriptors and learning architectures. We further address the related aspect of incorporating finite-field effects through the coupling with the system's polarization, relevant for the application of an external electric bias. We conclude by discussing the implications for the simulation of electrochemical interfaces, where long-range electrostatics are essential to capture the interplay between charge redistribution, interfacial dynamics, and ionic screening, and for ionic transport phenomena, which, although less explored, appear far less sensitive to their inclusion. Electrostatics ; Quantum nonlocality ; Perspective (graphical) ; Charge (physics) ; Range (aeronautics) ; Static electricity ; Coupling (piping) ; Locality Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Quantum many-body systems
Authors: lichao
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

MPCT-τ: A Chemical Stability Scalar Model Based on Electronic Configuration This paper presents MPCT-τ (Multi-parameter Chemical Tau), a novel scalar model for predicting chemical element stability based solely on electronic configuration. The model translates quantum-mechanical principles into a single stability parameter τ ranging from 0.0 to 1.0, where higher values indicate greater chemical stability. Through systematic development and validation, MPCT-τ achieves 100% accuracy on a comprehensive test set of 25 representative elements spanning alkali metals, transition metals, halogens, and noble gases. The model successfully explains chemical anomalies such as oxygen's lower stability compared to nitrogen (attributed to electron-electron repulsion) and validates diagonal relationships in the periodic table. MPCT-τ serves as an effective educational tool, research aid, and potential interface for machine learning applications in materials discovery Diagonal ; Diagonal ; Stability (learning theory) ; Stability (learning theory) ; Scalar (mathematics) ; Scalar (mathematics) ; Chemical stability ; Chemical stability ; Scalar potential ; Scalar potential Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: En Yang | Zhihao Mu | Peizhi Li | Menglong Liu | Wei Ma
Source: Agricultural Products Processing and Storage (article, 2026-01-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical probe based multiplex monitoring platforms for agricultural products: detection strategies from fabrication to spectroscopic applications The advancement of multiplex detection platforms represents a critical frontier in precision diagnostics and environmental monitoring, driven by nanoscale fabrication and spectroscopic innovations. However, traditional single-analyte methods remain inadequate for complex real-world matrices where multiple analytes coexist. This review systematically deconstructs recent progress in integrated sensing systems through 3 core technical dimensions: 1) Fabrication and heterostructure assembly, where controlled synthesis (templating, kinetic regulation, phase engineering), directed assembly (interfacial effects, biological programming), and heterostructure design (plasmonic-catalytic-porous synergies) enable electromagnetic hotspot generation, signal amplification, and selective enrichment; 2) Performance enhancement, leveraging engineered interfaces for specificity, nanostructures for ultrasensitive amplification, deep learning-aided spectral decoding, and matrix interference suppression; and 3) Cross-platform applications, spanning surface-enhanced Raman spectroscopy (SERS) (flexible substrates, composites), fluorescence, and colorimetric sensors (nanozymes, portable assays) for multi-target detection in fields like agricultural monitoring. Despite attomolar sensitivity and field-deployable advances, persistent challenges include the “trilemma” of functionality-robustness-scalability trade-offs, material stability, reproducible fabrication, and real-sample validation. By critically evaluating these mechanisms and limitations, this work provides a theoretical blueprint for next-generation intelligent detection platforms and prioritizes future research toward modular designs, multimodal synergy, and scalable manufacturing. Multiplex ; Fabrication ; Multiplexing ; Software portability ; Modular design ; Scalability Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection
Authors: Anton Morgunov | Yu Shee | Alexander V. Soudackov | Vânia Batista
Source: ChemRxiv (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

The Syntax of Matter: Synthesis Planning as the Foundation of Generative Chemistry Recent advances in deep learning have improved benchmark performance for chemical property prediction, yet reliable transfer to new chemical domains remains limited. A contributing factor is that many models treat molecules primarily as static graphs, ignoring the causal logic of how they are constructed. This review surveys multistep synthesis planning (2020-2026) and argues that the field is undergoing a fundamental transition: from an Era of Navigability (2018-2023), focused on the computational feasibility of finding any route through combinatorial search space, to an Era of Validity (2024-Present), focused on the chemical correctness of those routes. We organize the literature around two dominant paradigms, search-based planning and direct sequence generation, and analyze how their design choices relate to different notions of validity. To resolve the ambiguity of current "solvability" metric, which frequently exceeds 99% by measuring only topological connectivity, we introduce a formalized Hierarchy of Chemical Validity (Solv-N). This framework distinguishes between syntactic (Solv-0) and topological (Solv-1) success, which are largely solved, and the higher-order constraints of selectivity (Solv-2) and executability (Solv-3), which remain open challenges. We critically examine how legacy benchmarks and inflated virtual inventories obscure this distinction, and we conclude with a roadmap for synthesis-aware foundation models evaluated under explicit Tier 2-3 constraints. Ambiguity ; Correctness ; Generative grammar ; Hierarchy ; Salient ; Foundation (evidence) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Galymzhan Moldagulov | Kisung Lee | Sanzhar Nurgaliyev | Assanali Salem | Anatolii I. Kuznietsov | Bartosz A. Grzybowski
Source: Angewandte Chemie International Edition (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Hybrid Computational Strategy for Predicting Complex Ligand–Metal Architectures Understanding how metals coordinate to organic ligands is a precondition for the rational design of metal complexes and catalysts. Whereas certain types of ligands are capable of just one easy-to-predict coordination modality, others may present tens and sometimes even hundreds of coordination options (mono-, bi-, or polydentate), and predicting the correct one may be a challenge even to seasoned chemists. The current paper describes a "hybrid" computational approach in which a Machine Learning, ML, algorithm learns to predict complex coordination patterns using knowledge-based "rules" derived from the Cambridge Structural Database, CSD. This model is applicable to a broad scope of ligands (including hemilabile and haptic ones as well as those with denticity > 6) and different metals at different oxidation states. The algorithm's code is disclosed and can be readily deployed in RDKit via our RDMetallics python-wrapper. It is also deployed as a publicly accessible web portal for demonstration and use. Scope (computer science) ; Denticity ; Precondition ; Code (set theory) ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis