jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hang Xiao | Rong Li | Xiaoyang Shi | Yan Chen | Liangliang Zhu | Lei Wang | Xi Chen
Source: ChemRxiv (preprint, 2023-05-22)
Field: Materials Science — Materials Chemistry

An invertible, invariant crystallographic representation for inverse design of solid-state materials using generative deep learning The past decade has witnessed rapid progress in deep learning for molecular design, owing to the availability of invertible and invariant representations for molecules such as simplified molecular-input line-entry system (SMILES), which has powered cheminformatics since the late 1980s. However, the design of elemental components and their structural arrangement in solid-state materials to achieve certain desired properties is still a long-standing challenge in physics, chemistry and biology. This is primarily due to, unlike molecular inverse design, the lack of an invertible crystallographic representation that satisfies symmetry invariances. To address this issue, we have developed a simplified line-input crystal-encoding system (SLICES), which is a string-based crystallographic representation that satisfies both invertibility and symmetry invariances. SLICES successfully reconstructed 94.95% of over 40,000 structurally and chemically diverse crystal structures, showcasing an unprecedented invertibility. Furthermore, by only encoding compositional and topological data, SLICES guarantees symmetry invariances. We demonstrate the application of SLICES in the inverse design of direct narrow-gap semiconductors for optoelectronic applications. As a string-based, invertible, invariant and efficient crystallographic representation, SLICES has the potential to become a standard tool for in-silico materials discovery. Invertible matrix ; Invariant (physics) ; Inverse ; Crystallographic point group ; Representation (politics) ; Symmetry (geometry) ; String (physics) ; Crystallography ; Mathematics ; Pure mathematics ; Computer science ; Crystal structure ; Chemistry ; Theoretical physics ; Physics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Di Luo | David D. Dai | Liang Fu
Source: arXiv (Cornell University) (preprint, 2023-11-03)
Field: Materials Science — Materials Chemistry

Pairing-based graph neural network for simulating quantum materials We develop a pairing-based graph neural network for simulating quantum many-body systems. Our architecture augments a BCS-type geminal wavefunction with a generalized pair amplitude parameterized by a graph neural network. Variational Monte Carlo with our neural network simultaneously provides an accurate, flexible, and scalable method for simulating many-electron systems. We apply this method to two-dimensional semiconductor electron-hole bilayers and obtain accurate results on a variety of interaction-induced phases, including the exciton Bose-Einstein condensate, electron-hole superconductor, and bilayer Wigner crystal. Our study demonstrates the potential of physically-motivated neural network wavefunctions for quantum materials simulations. Pairing ; Wave function ; Artificial neural network ; Quantum ; Graph ; Parameterized complexity ; Physics ; Quantum Monte Carlo ; Geminal ; Statistical physics ; Computer science ; Quantum mechanics ; Topology (electrical circuits) ; Theoretical computer science ; Monte Carlo method ; Mathematics ; Artificial intelligence ; Superconductivity ; Algorithm ; Chemistry Machine Learning in Materials Science ; Quantum many-body systems ; Quantum and electron transport phenomena
Authors: Md Habibur Rahman | Prince Gollapalli | Panayotis Manganaris | Satyesh Kumar Yadav | Ghanshyam Pilania | Brian DeCost | Kamal Choudhary | Arun Mannodi‐Kanakkithodi
Source: arXiv (Cornell University) (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of Group IV, III-V, and II-VI zinc blende (ZB) semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely Crystal Graph Convolutional Neural Network (CGCNN), Materials Graph Network (MEGNET), and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98 % given the range of values within the dataset, improving significantly on the state-of-the-art. Models are tested for different defect types as well as for defect charge transition levels. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially-optimized defective structures, helping identify electronically active defects in technologically-important semiconductors. Artificial neural network ; Graph ; Crystal structure prediction ; Semiconductor ; Impurity ; Density functional theory ; Computer science ; Convolutional neural network ; Materials science ; Algorithm ; Computational chemistry ; Artificial intelligence ; Crystal structure ; Theoretical computer science ; Chemistry ; Crystallography ; Physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; CO2 Reduction Techniques and Catalysts
Authors: Ka Lok Chan | Long Thanh Ta | Yong Huang | Haibin Su | Zhenyang Lin
Source: Molecules (article, 2023-06-13)
Field: Materials Science — Materials Chemistry

Incorporating Domain Knowledge and Structure-Based Descriptors for Machine Learning: A Case Study of Pd-Catalyzed Sonogashira Reactions Machine learning has revolutionized information processing for large datasets across various fields. However, its limited interpretability poses a significant challenge when applied to chemistry. In this study, we developed a set of simple molecular representations to capture the structural information of ligands in palladium-catalyzed Sonogashira coupling reactions of aryl bromides. Drawing inspiration from human understanding of catalytic cycles, we used a graph neural network to extract structural details of the phosphine ligand, a major contributor to the overall activation energy. We combined these simple molecular representations with an electronic descriptor of aryl bromide as inputs for a fully connected neural network unit. The results allowed us to predict rate constants and gain mechanistic insights into the rate-limiting oxidative addition process using a relatively small dataset. This study highlights the importance of incorporating domain knowledge in machine learning and presents an alternative approach to data analysis. Interpretability ; Sonogashira coupling ; Computer science ; Artificial neural network ; Machine learning ; Artificial intelligence ; Chemistry ; Combinatorial chemistry ; Catalysis ; Palladium ; Organic chemistry Machine Learning in Materials Science ; Data Quality and Management ; Computational Drug Discovery Methods
Authors: Ziqiao Meng | Peilin Zhao | Yu Yang | Irwin King
Source: arXiv (Cornell University) (preprint, 2023-06-28)
Field: Materials Science — Materials Chemistry

A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry that have recently garnered attention from both the machine learning and drug discovery communities. Various deep learning approaches have been proposed to tackle these problems, and some have achieved initial success. In this survey, we conduct a comprehensive investigation of advanced deep learning-based models for reaction and retrosynthesis prediction. We summarize the design mechanisms, strengths, and weaknesses of state-of-the-art approaches. Then, we discuss the limitations of current solutions and open challenges in the problem itself. Finally, we present promising directions to facilitate future research. To our knowledge, this paper is the first comprehensive and systematic survey that seeks to provide a unified understanding of reaction and retrosynthesis prediction. Retrosynthetic analysis ; Strengths and weaknesses ; Artificial intelligence ; Computer science ; Machine learning ; Deep learning ; Management science ; Data science ; Engineering ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Yu Zheng | Nicholas E. Jackson
Source: ChemRxiv (preprint, 2023-03-13)
Field: Materials Science — Materials Chemistry

Machine learning quantum-chemical bond scission in thermosets under extreme deformation Despite growing interest in polymers under extreme conditions, most atomistic molecular dynamics simulations cannot describe the bond scission events underlying failure modes in polymer networks undergoing large strains. In this work, we propose a physics-based machine learning approach that can detect and perform bond breaking with near quantum-chemical accuracy on-the-fly in atomistic simulations. Particularly, we demonstrate that by coarse-graining highly correlated neighboring bonds, the prediction accuracy can be dramatically improved. Compared to existing quantum mechanics/molecular mechanics (QM/MM) methods, our approach is approximately two orders of magnitude more efficient and exhibits improved sensitivity towards rare bond breaking events at low strain. The proposed bond breaking molecular dynamics scheme enables fast and accurate modeling of strain hardening and material failure in polymer networks, and can accelerate the design of polymeric materials under extreme conditions. Bond cleavage ; Molecular dynamics ; Quantum ; Materials science ; Polymer ; Chemical bond ; Bond length ; Chemical physics ; Statistical physics ; Nanotechnology ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Composite material ; Molecule Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Polymer crystallization and properties
Authors: Logan Williams | Arpan Mukherjee | Ruhil Dongol | Krishna Rajan
Source: The Journal of Physical Chemistry C (article, 2023-06-12)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided Property Prediction of Hybrid Organic–Inorganic Perovskites Using Hirshfeld Surface Representations of Crystal Structures This paper describes a lightweight neural network (NN) to predict thermodynamic, electric, and electronic properties of hybrid organic–inorganic perovskites (HOIPs) using Hirshfeld surfaces as novel material representation for HOIPs. The neural network utilizes only a few Hirshfeld surface features (e.g., volume, surface area, globularity, and effective radius), along with qualitative and quantitative (mixed) variables, to predict the properties of HOIPs in a highly accelerated manner. Our use of Hirshfeld surface-based descriptors of HOIP crystals leads to a new metric for measuring the effective radius of an organic molecule within a given structure, which are proven to be highly effective features for efficient machine learning of crystalline materials’ properties. A detailed comparison between the crystal graph convolutional neural network (CGCNN) and the Hirshfeld surface-based neural network analysis via UMAP and HDBSCAN clustering is provided to assess the efficacy of these methods for different compound chemistries. It is shown that a combination of lower-order feature representation and a shallow lightweight neural network is capable of predicting material properties for HOIPs. Benchmarking against well-established denser crystal property prediction techniques such as the CGCNN and deeper graph attention layer graph neural network (deeper GATGNN) shows that our approach provides comparable and, in some cases, even superior predictive performance of properties such as formation energy, band gap, and electronic dielectric constant but all at much lower computational cost. Artificial neural network ; Artificial intelligence ; Crystal (programming language) ; Convolutional neural network ; Materials science ; Crystal structure ; Computer science ; Machine learning ; Algorithm ; Biological system ; Topology (electrical circuits) ; Mathematics ; Chemistry ; Crystallography Machine Learning in Materials Science ; Perovskite Materials and Applications ; Conducting polymers and applications
Authors: Christopher M. Collins | Hasan M. Sayeed | George R. Darling | John B. Claridge | Taylor D. Sparks | Matthew J. Rosseinsky
Source: ChemRxiv (preprint, 2023-12-19)
Field: Materials Science — Materials Chemistry

Integration of generative machine learning with the heuristic crystal structure prediction code FUSE The prediction of new compounds via crystal structure prediction may transform how the materials chemistry community discovers new compounds. In the prediction of inorganic crystal structures there are two distinct classes of prediction; Performing crystal structure prediction via heuristic algorithms, using a range of established crystal structure prediction codes, and an emerging community using generative machine learning models to predict crystal structures directly. In this work, we demonstrate the use of a generative machine learning model to produce the starting population of crystal structures for a heuristic algorithm and discuss the benefits, demonstrating the method on eight known compounds with reported crystal structures and three hypothetical compounds. We show that the integration of machine learning structure generation to heuristic structure prediction results in both faster compute times per structure and leads to lower energies. This work provides to the community a set of eleven compounds with varying chemistry and complexity which can be used as a benchmark for new crystal structure prediction methods as they emerge. Benchmark (surveying) ; Heuristic ; Machine learning ; Generative grammar ; Computer science ; Artificial intelligence ; Crystal structure ; Crystal (programming language) ; Crystal structure prediction ; Algorithm ; Generative model ; Chemistry ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Shokirbek Shermukhamedov | Dilorom Mamurjonova | Thana Maihom | Michael Probst
Source: ChemRxiv (preprint, 2023-11-29)
Field: Materials Science — Materials Chemistry

Structure to Property: Machine Learning Methods for Predicting Electronic Properties of Crystals We present a general-purpose machine learning model for predicting properties of crystals. Specifically, energy of formation, Fermi level energy, band gap, partial charges, and bulk modulus as well as spectral properties, including electronic and phonon densities of states are targeted. Thus, our model can be used to screen materials for specific properties. The model is based on atomic representations which enables it to effectively capture complex information about each atom and its surrounding environment in a crystal. The accuracy achieved for band gap values exceeds results previously published. By design, our model is not restricted to electronic properties discussed here but can be extended to fit diverse chemical databases. Band gap ; Property (philosophy) ; Crystal (programming language) ; Phonon ; Atom (system on chip) ; Electronic structure ; Energy (signal processing) ; Materials science ; Crystal structure ; Statistical physics ; Computer science ; Condensed matter physics ; Physics ; Chemistry ; Crystallography ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Tian 田 Wang 王 | Jiahui Chen | Jing 婧 Teng 滕 | Jingang 金钢 Shi 史 | Xinhua 新华 Zeng 曾 | Hichem Snoussi
Source: Chinese Physics B (article, 2023-06-28)
Field: Materials Science — Materials Chemistry

Crysformer: An attention-based graph neural network for properties prediction of crystals We present a novel approach for the prediction of crystal material properties that is distinct from the computationally complex and expensive density functional theory (DFT)-based calculations. Instead, we utilize an attention-based graph neural network that yields high-accuracy predictions. Our approach employs two attention mechanisms that allow for message passing on the crystal graphs, which in turn enable the model to selectively attend to pertinent atoms and their local environments, thereby improving performance. We conduct comprehensive experiments to validate our approach, which demonstrates that our method surpasses existing methods in terms of predictive accuracy. Our results suggest that deep learning, particularly attention-based networks, holds significant promise for predicting crystal material properties, with implications for material discovery and the refined intelligent systems. Computer science ; Artificial neural network ; Graph ; Crystal structure prediction ; Crystal (programming language) ; Artificial intelligence ; Deep neural networks ; Theoretical computer science ; Machine learning ; Complex system ; Crystal structure Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Sudarshan Vijay | Maxwell C. Venetos | Evan Walter Clark Spotte‐Smith | Aaron D. Kaplan | Mingjian Wen | Kristin A. Persson
Source: ChemRxiv (preprint, 2023-08-21)
Field: Materials Science — Materials Chemistry

CoeffNet: Predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network Activation barriers of elementary reactions are essential to predict molecular reaction mechanisms and kinetics. However, computing these energy barriers by identifying transition states with electronic structure methods (e.g., density functional theory) can be time-consuming and computationally expensive. In this work, we introduce CoeffNet, an equivariant graph neural network that predicts activation barriers using coeﬀicients of a frontier molecular orbital (such as the highest occupied molecular orbital) of reactant and product complexes as graph node features. We show that using coeﬀicients as features offer several advantages, such as chemical interpretability and physical constraints on the network’s behaviour and numerical range. Model outputs are either activation barriers or coeﬀicients of the chosen molecular orbital of the transition state; the latter quantity allows us to interpret the results of the neural network through chemical intuition. We test CoeffNet on a dataset of SN2 reactions as a proof-of-concept and show that the activation barriers are predicted with a mean absolute error of less than 0.025 eV. The highest occupied molecular orbital of the transition state is visualized and the distribution of the orbital densities of the transition states is described for a few prototype SN2 reactions. Interpretability ; Transition state ; Molecular orbital ; Artificial neural network ; SN2 reaction ; Computer science ; HOMO/LUMO ; Computational chemistry ; Statistical physics ; Chemistry ; Biological system ; Materials science ; Molecule ; Physics ; Artificial intelligence ; Quantum mechanics ; Catalysis ; Stereochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Shijie Sun | Akash Singh | Yumeng Li
Source:  (article, 2023-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Atomistic Simulations for 2D Materials With Defects Abstract 2D materials generally show very different physical and chemical properties from 3D materials, which provide them promising applications in cutting-edge technology areas like aerospace, energy storage and electronics. To better understand and illustrate their unique properties, current research heavily relies on atomistic simulations, while successful simulations require the high reliability of interatomic interaction potentials that empirical potentials cannot provide. The ab initio calculations, for example density functional theory (DFT), are able to conduct high-fidelity simulations in essence, but with a high computational cost and largely limited simulation size. Recently machine learning potentials become a trend to interpolate the potential energy surface based on artificial neural networks (ANNs) with reference datasets from first principle calculations. Although machine learning potentials have been developed for many material systems including 2D materials, there is limited work published regarding structural defects. Using graphene as a model material, we demonstrate a new machine learning potential for 2D materials with defects. The reference energies are generated by DFT calculations. ANN with two hidden layers is used for the training, which has been demonstrated to be suitable for developing machine learning potential in our previous work. For ANN input, the atomic structures are coded using perturbation-invariant representations. After training, the weight and bias parameters are exported as our potential and then imported into LAMMPS software to conduct MD simulations. It is expected that our work provides fundamental support on investigating defective graphene and understanding defect effects to develop structure-property relationship. This will also promote the development of machine learning based simulation tools for the study and design of complex materials. Computer science ; Materials science ; Artificial intelligence Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques