jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rohit Modee | Sheena Agarwal | Ashwini Verma | Kavita Joshi | U. Deva Priyakumar
Source: ChemRxiv (preprint, 2021-05-26)
Field: Materials Science — Materials Chemistry

DART: Deep Learning Enabled Topological Interaction Model for Energy Prediction of Metal Clusters and its Application in Identifying Unique Low Energy Isomers Recently, Machine Learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple Topological Atomic Descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART, Deep Learning Enabled Topological Interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case Gallium clusters with size ranging from 31 to 70 atoms. DART model is designed based on the principle that energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31-70, which comprises structures and DFT optimized energies of Gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the identification of ground-state structures without geometry optimization. Albeit using topological descriptor, DART achieves MAE of 3.59 kcal/mol (0.15 eV) on testset. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of DART model by predicting energies for about 6k unseen configurations picked up from Molecular Dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data. Cluster (spacecraft) ; Atom (system on chip) ; Topology (electrical circuits) ; Energy (signal processing) ; Computer science ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Hadi Abroshan | Anand Chandrasekaran | Paul Winget | Yuling An | Shaun Kwak | Christopher T. Brown | Mathew D. Halls
Source:  (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Accelerated design and optimization of novel OLED materials via active learning To date, the development of organic light-emitting diode (OLED) materials has been primarily based on a combination of chemical intuition and trial-and-error experimentation. The approach is often expensive and time-consuming, let alone in most instances fails to offer new materials leading to higher efficiencies. Data-driven approaches have emerged as a powerful tool to accelerate the design and discovery of novel materials with multifunctional properties for next generation OLED technologies. Virtual high-throughput methods assisted by machine learning (ML) enable a broad screening of chemical space to predict material properties and suggest new candidates for OLEDs. In order to build reliable predictive ML models for OLED materials, it is required to create and manage a high volume of data which not only maintain high accuracy but also properly assess the complexity of materials chemistry in the OLED space. Active learning (AL) is among several strategies developed to face the challenge in both materials science and life science applications, where the data management in large-scale becomes a main bottleneck. Here, we present a workflow that efficiently combines AL with atomic-scale simulations to reliably predict optoelectronic properties of OLED materials. This study provides a robust and validated framework to account for multiple parameters that simultaneously influence OLED performance. Results of this work pave the way for a fundamental understanding of optoelectronic performance of emergent layers from a molecular perspective, and further screen candidate materials with superior efficiencies before laborious simulations, synthesis, and device fabrication. OLED ; Workflow ; Computer science ; Chemical space ; Bottleneck ; Robustness (evolution) ; Nanotechnology ; Materials science ; Embedded system ; Bioinformatics ; Database Machine Learning in Materials Science ; Organic Light-Emitting Diodes Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Dominikus Brian | Xiang Sun
Source: ChemRxiv (preprint, 2021-11-08)
Field: Materials Science — Materials Chemistry

Charge Transfer Landscape Manifesting Structure-Rate Relationship in the Condensed Phase via Machine Learning In this work, we develop a machine learning (ML) strategy to map molecular structure to condensed-phase charge transfer (CT) properties including CT rate constants, energy levels, electronic couplings, energy gaps, reorganization energies, and reaction free energies, which are called CT fingerprints. The CT fingerprints of selected landmark structures covering the conformation space of an organic photovoltaic molecule dissolved in explicit solvent are computed and used to train ML models using kernel ridge regression. The ML models show high predictive power with R2>0.97, and both mean absolute error and root mean square error within chemical accuracy. The CT landscape for millions of molecular dynamics sampled structures is thus constructed, which allows for instant prediction of CT rate properties given any molecular structure. The unprecedented CT landscape will shed light on real-time CT dynamics in nanoscale and mesoscale condensed-phase systems, and the optimal fabrication design for homogeneous and heterogeneous optoelectronic devices. Energy landscape ; Phase (matter) ; Molecular dynamics ; Charge (physics) ; Kernel (algebra) ; Chemical physics ; Mean squared error ; Nanoscopic scale ; Materials science ; Statistical physics ; Physics ; Molecular physics ; Chemistry ; Nanotechnology ; Computational chemistry ; Thermodynamics ; Mathematics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Doyl Dickel | Mashroor S. Nitol | Christopher D. Barrett
Source: Computational Materials Science (preprint, 2021-05-04)
Field: Materials Science — Materials Chemistry

LAMMPS implementation of rapid artificial neural network derived interatomic potentials Rotation formalisms in three dimensions ; Artificial neural network ; Computer science ; Molecular dynamics ; Algorithm ; Artificial intelligence ; Interatomic potential ; Computational science ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; X-ray Diffraction in Crystallography
Authors: Ryan‐Rhys Griffiths | Philippe Schwaller | Alpha A. Lee
Source: arXiv (Cornell University) (preprint, 2021-05-06)
Field: Materials Science — Materials Chemistry

Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning. In this paper, we identify three trends within the fields of chemical reaction prediction and synthesis design that require a change in direction. First, the manner in which reaction datasets are split into reactants and reagents encourages testing models in an unrealistically generous manner. Second, we highlight the prevalence of mislabelled data, and suggest that the focus should be on outlier removal rather than data fitting only. Lastly, we discuss the problem of reagent prediction, in addition to reactant prediction, in order to solve the full synthesis design problem, highlighting the mismatch between what machine learning solves and what a lab chemist would need. Our critiques are also relevant to the burgeoning field of using machine learning to accelerate progress in experimental Natural Sciences, where datasets are often split in a biased way, are highly noisy, and contextual variables that are not evident from the data strongly influence the outcome of experiments. Outlier ; Computer science ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Natural (archaeology) ; Focus (optics) ; Chemist ; Data science ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Christopher M. Collins | Luke M. Daniels | Quinn Gibson | Michael W. Gaultois | Michael D. Moran | Richard Feetham | Michael J. Pitcher | Matthew S. Dyer | C. Delacotte | Marco Zanella | Claire A. Murray | Györgyi Glodán | Olivier Pérez | D. Pelloquin | Troy D. Manning | Jonathan Alaria | George R. Darling | John B. Claridge | Matthew J. Rosseinsky
Source: Angewandte Chemie (article, 2021-05-06)
Field: Materials Science — Materials Chemistry

Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning Abstract We report the aperiodic titanate Ba 10 Y 6 Ti 4 O 27 with a room‐temperature thermal conductivity that equals the lowest reported for an oxide. The structure is characterised by discontinuous occupancy modulation of each of the sites and can be considered as a quasicrystal. The resulting localisation of lattice vibrations suppresses phonon transport of heat. This new lead material for low‐thermal‐conductivity oxides is metastable and located within a quaternary phase field that has been previously explored. Its isolation thus requires a precisely defined synthetic protocol. The necessary narrowing of the search space for experimental investigation was achieved by evaluation of titanate crystal chemistry, prediction of unexplored structural motifs that would favour synthetically accessible new compositions, and assessment of their properties with machine‐learning models. Thermal conductivity ; Metastability ; Ferroelectricity ; Materials science ; Aperiodic graph ; Lattice (music) ; Phonon ; Oxide ; Crystal structure ; Anharmonicity ; Chemical physics ; Condensed matter physics ; Chemistry ; Crystallography ; Physics ; Optoelectronics ; Composite material ; Dielectric Advanced Thermoelectric Materials and Devices ; Advanced Condensed Matter Physics ; Machine Learning in Materials Science
Authors: Marco Eckhoff | Jörg Behler
Source: npj Computational Materials (preprint, 2021-10-15)
Field: Materials Science — Materials Chemistry

High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions Abstract Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first-principles quality simulations. Still, most machine learning potentials cannot distinguish different electronic spin arrangements and thus are not applicable to materials in different magnetic states. Here we propose spin-dependent atom-centered symmetry functions as a type of descriptor taking the atomic spin degrees of freedom into account. When used as an input for a high-dimensional neural network potential (HDNNP), accurate potential energy surfaces of multicomponent systems can be constructed, describing multiple collinear magnetic states. We demonstrate the performance of these magnetic HDNNPs for the case of manganese oxide, MnO. The method predicts the magnetically distorted rhombohedral structure in excellent agreement with density functional theory and experiment. Its efficiency allows to determine the Néel temperature considering structural fluctuations, entropic effects, and defects. The method is general and is expected to be useful also for other types of systems such as oligonuclear transition metal complexes. Artificial neural network ; Symmetry (geometry) ; Atom (system on chip) ; Manganese oxide ; Spin (aerodynamics) ; Condensed matter physics ; Degrees of freedom (physics and chemistry) ; Physics ; Manganese ; Statistical physics ; Materials science ; Computer science ; Quantum mechanics ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Daniel Probst | Philippe Schwaller | Jean‐Louis Reymond
Source: ChemRxiv (preprint, 2021-07-06)
Field: Materials Science — Materials Chemistry

Reaction Classification and Yield Prediction using the Differential Reaction Fingerprint DRFP Predicting the nature and outcome of reactions using computational methods is a crucial tool to accelerate chemical research. The recent application of deep learning-based learned fingerprints to reaction classification and reaction yield prediction has shown an impressive increase in performance compared to previous methods such as DFT- and structure-based fingerprints. However, learned fingerprints require large training data sets, are inherently biased, and are based on complex deep learning architectures. Here we present the differential reaction fingerprint \textit{DRFP}. The \textit{DRFP} algorithm takes a reaction SMILES as an input and creates a binary fingerprint based on the symmetric difference of two sets containing the circular molecular n-grams generated from the molecules listed left and right from the reaction arrow, respectively, without the need for distinguishing between reactants and reagents. We show that \textit{DRFP} outperforms DFT-based fingerprints in reaction yield prediction and other structure-based fingerprints in reaction classification, reaching the performance of state-of-the-art learned fingerprints in both tasks while being data-independent. Fingerprint (computing) ; Yield (engineering) ; Computer science ; Binary number ; Artificial intelligence ; Differential (mechanical device) ; Pattern recognition (psychology) ; Binary classification ; Algorithm ; Mathematics ; Physics ; Support vector machine ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Marko Melander | Tongwei Wu | Karoliina Honkala
Source: ChemRxiv (preprint, 2021-09-21)
Field: Materials Science — Materials Chemistry

Constant inner potential DFT for modelling electrochemical systems under constant potential and bias Electrochemical interfaces and reactions play a decisive role in e.g. clean energy conversion but understanding their complex chemistry remains an outstanding challenge. Constant potential or grand canonical ensemble (GCE) simulations are indispensable for unraveling the properties of electrochemical processes as a function of the electrode potential. Currently, constant electrode potential calculations at the density functional theory (DFT) level are carried out by fixing the Fermi level of the simulation cell. However, the Fermi level from DFT calculations does does not always reflect the experimentally controlled electrode potential or describe the thermodynamic independent variable in GCE-DFT i.e the electrochemical potential of an electron reservoir. Here we develop and implement the constant inner potential (CIP) method as a more robust and general approach to GCE-DFT simulations of electrochemical systems under constant potential or bias conditions. The CIP is shown to directly control the reservoir electron electrochemical potential making the method widely applicable in simulating electrochemical interfaces. We demonstrate that the CIP and Fermi level GCE-DFT approaches are equivalent for metallic electrodes and inner sphere reactions. The CIP method is shown to be applicable in simulating also semiconductor electrodes, outer sphere reactions, and a biased two-electrode cell for which the Fermi level approach does not reflect the experimental electrode potential. Unlike the Fermi level method, CIP does not require any electronic structure information as only the inner potential is needed, which makes the approach more compatible with classical force field or machine learning potentials. The CIP approach emerges as a general GCE DFT method to simulate (photo)electrochemical interfaces from first principles. Electrode potential ; Electrochemistry ; Electrode ; Density functional theory ; Standard electrode potential ; Electrochemical potential ; Fermi level ; Fermi energy ; Chemistry ; Constant (computer programming) ; Chemical physics ; Materials science ; Atomic physics ; Electron ; Physical chemistry ; Computational chemistry ; Physics ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Yunfei Peng | Zhihang Ma | Xiao Wang | Junru Li | Xinlin Li | Chuanwei Zhang | Tian He | Guoqiang Li | Pengfei Zhang
Source: Fractals (review, 2021-11-11)
Field: Materials Science — Surfaces, Coatings and Films

FRACTAL SURFACE RECOVERY AND SELF-HEALING CONTRIBUTED TO SUSTAINABLE SUPERHYDROPHOBICITY: A REVIEW The concept of superhydrophobicity has been widely used after years of theoretical and experimental exploration. Researchers have obtained materials with excellent surface superhydrophobicity for numerous areas (e.g. textiles, paints, and coatings industries), through different design and synthesis methods using low surface energy components (LSECs) and micro/nanohierarchy composite structures. However, the durability of superhydrophobic material surfaces has gradually become a prominent obstacle to restricting applications. In this paper, advances in improving the durability of superhydrophobic surfaces in the past decade are reviewed, covering different schemes for recovering fractal surfaces based on the LSECs and micro/nanocomposite structures. It also presents a balanced review on wet chemical methods, chemical vapor deposition (CVD), layer-by-layer (LbL), microarc oxidation (MAO), and self-healing of the fractal surfaces, with the aim at developing sustainable superhydrophobicity. In addition, it innovatively summarized the research on the synthesis of self-healing superhydrophobic materials by machine learning methods. By the end it discusses perspectives on future development in this emerging research area. Materials science ; Durability ; Nanotechnology ; Self-healing ; Fractal ; Composite number ; Contact angle ; Chemical vapor deposition ; Composite material Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Aerogels and thermal insulation
Authors: Cian, Lorenzo | Lancioni, Giuseppe | Zhang, Lei | Ianese, Mirco | Novelli, Nicolas | Serra, Giuseppe | Maresca, Francesco
Source: arXiv (Cornell University) (preprint, 2021-09-28)
Field: Materials Science — Materials Chemistry

Atomistic Graph Neural Networks for metals: Application to bcc iron The prediction of the atomistic structure and properties of crystals including defects based on ab-initio accurate simulations is essential for unraveling the nano-scale mechanisms that control the micromechanical and macroscopic behaviour of metals. Density functional theory (DFT) can enable the quantum-accurate prediction of some of these properties, however at high computational costs and thus limited to systems of ~1,000 atoms. In order to predict with quantum-accuracy the mechanical behaviour of nanoscale structures involving from thousands to several millions of atoms, machine learning interatomic potentials have been recently developed. Here, we explore the performance of a new class of interatomic potentials based on Graph Neural Networks (GNNs), a recent field of research in Deep Learning. Two state-of-the-art GNN models are considered, SchNet and DimeNet, and trained on an extensive DFT database of ferromagnetic bcc iron. We find that the DimeNet GNN Fe potential including three-body terms can reproduce with DFT accuracy the equation of state and the Bain path, as well as defected configurations (vacancy and surfaces). To the best of our knowledge, this is the first demonstration of the capability of GNN of reproducing the energetics of defects in bcc iron. We provide an open-source implementation of DimeNet that can be used to train other metallic systems for further exploration of the GNN capabilities. Density functional theory ; Quantum ; Ab initio ; Computer science ; Interatomic potential ; Materials science ; Vacancy defect ; Statistical physics ; Artificial neural network ; Graph ; Molecular dynamics ; Theoretical computer science ; Artificial intelligence ; Physics ; Computational chemistry ; Condensed matter physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Vipul Mann | Venkat Venkatasubramanian
Source:  (preprint, 2021-08-26)
Field: Materials Science — Materials Chemistry

Retrosynthesis Prediction using Grammar-based Neural Machine Translation: An Information-Theoretic Approach Retrosynthetic prediction is one of the main challenges in chemical synthesis because it requires a search over the space of plausible chemical reactions that often results in complex, multi-step, branched synthesis trees for even moderately complex organic reactions. Here, we propose an approach that performs single-step retrosynthesis prediction using SMILES grammar-based representations in a neural machine translation framework. Information-theoretic analyses of such grammar-representations reveal that they are superior to SMILES representations and are better-suited for machine learning tasks due to their underlying redundancy and high information capacity. We report the top-1 prediction accuracy of 43.8% (syntactic validity 95.6%) and maximal fragment (MaxFrag) accuracy of 50.4%. Comparing our model’s performance with previous work that used character-based SMILES representations demonstrate significant reduction in grammatically invalid predictions and improved prediction accuracy. Fewer invalid predictions for both known and unknown reaction class scenarios demonstrate the model’s ability to learn the underlying SMILES grammar efficiently. Retrosynthetic analysis ; Computer science ; Artificial intelligence ; Grammar ; Machine translation ; Natural language processing ; Redundancy (engineering) ; Machine learning ; Rule-based machine translation ; Synchronous context-free grammar ; Translation (biology) ; Example-based machine translation ; Linguistics Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Asymmetric Hydrogenation and Catalysis