jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryo Tamura | Koji Tsuda | Shôichi Matsuda
Source: arXiv (Cornell University) (preprint, 2023-04-27)
Field: Materials Science — Materials Chemistry

NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science NIMS-OS (NIMS Orchestration System) is a Python library created to realize a closed loop of robotic experiments and artificial intelligence (AI) without human intervention for automated materials exploration. It uses various combinations of modules to operate autonomously. Each module acts as an AI for materials exploration or a controller for a robotic experiments. As AI techniques, Bayesian optimization (PHYSBO), boundless objective-free exploration (BLOX), phase diagram construction (PDC), and random exploration (RE) methods can be used. Moreover, a system called NIMS automated robotic electrochemical experiments (NAREE) is available as a set of robotic experimental equipment. Visualization tools for the results are also included, which allows users to check the optimization results in real time. Newly created modules for AI and robotic experiments can be added easily to extend the functionality of the system. In addition, we developed a GUI application to control NIMS-OS.To demonstrate the operation of NIMS-OS, we consider an automated exploration for new electrolytes. NIMS-OS is available at https://github.com/nimsos-dev/nimsos. Python (programming language) ; Computer science ; Automation ; Orchestration ; Software ; Visualization ; Artificial intelligence ; Robotics ; Human-in-the-loop ; Controller (irrigation) ; Embedded system ; Robot ; Programming language ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Fuel Cells and Related Materials
Authors: Bojana Ranković | Ryan‐Rhys Griffiths | Henry B. Moss | Philippe Schwaller
Source: ChemRxiv (preprint, 2023-05-30)
Field: Materials Science — Materials Chemistry

Bayesian optimisation for additive screening and yieldimprovements in chemical reactions – beyond one-hot encoding Reaction additives play a significant role in controlling the reactivity and outcomes of chemical reactions. For example, a recent high-throughput additive screening identified a phthalimide ligand additive for Ni-catalysed photoredox decarboxylative arylations. This discovery enabled a 4-fold yield improvement by stabilising oxidative addition complexes and breaking up deactivated catalyst aggregates. Despite the promise of such large-scale screenings, they remain inaccessible to most research groups due to their cost and complexity. In this work, we demonstrate how to uncover similar results under much lower experimental budgets using Bayesian optimisation empowered methodology. We consider a unique reaction screening setting with 720 additives, forcing us to go beyond simple one-hot encoding of the reaction components. By investigating a range of molecular and reaction representations, we demonstrate convincing improvements over random search-inspired baselines. Importantly, our approach is generalisable and not limited to Ni-catalysed reactions, but can be applied to achieve yield improvements in diverse cross-couplings or other reactions, potentially unlocking access to new chemical spaces that are of interest to the chemical and pharmaceutical industries. The code is available at: https://github.com/schwallergroup/chaos. Yield (engineering) ; Bayesian probability ; Encoding (memory) ; Computer science ; Combinatorial chemistry ; Catalysis ; Throughput ; Biochemical engineering ; Chemistry ; Materials science ; Organic chemistry ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Radical Photochemical Reactions ; Advanced Photocatalysis Techniques
Authors: David Cohen‐Tanugi | Myles Stapelberg | Michael P. Short | S. Ferry | D.G. Whyte | Zachary Hartwig | Tonio Buonassisi
Source: arXiv (Cornell University) (preprint, 2023-11-20)
Field: Materials Science — Materials Chemistry

Long-Term Research &amp; Design Strategies for Fusion Energy Materials Fusion energy is at an important inflection point in its development: multiple government agencies and private companies are now planning fusion pilot plants to deliver electricity to the grid in the next decade. However, realizing fusion as a technically and economically viable energy source depends on developing and qualifying materials that can withstand the extreme environment inside a fusion power plant. This Perspective seeks to engage the broader materials science community in this long-term effort. We first outline the principal materials challenges and research opportunities for fusion. Next, we argue that fusion is distinct from other energy applications with respect to materials, not just in the magnitude and complexity of the technical challenges but also in the present level of uncertainty in materials design requirements. To address this, we finally propose a research framework based on an iterative co-evolution of materials science and fusion power plant design requirements. Systems engineering ; Key (lock) ; Computer science ; Fusion power ; Electricity ; Principal (computer security) ; Fusion ; Government (linguistics) ; Risk analysis (engineering) ; Architectural engineering ; Engineering ; Business ; Computer security ; Electrical engineering Fusion materials and technologies
Authors: Hannes Kneiding | Ainara Nova | David Balcells
Source: ChemRxiv (preprint, 2023-06-21)
Field: Chemistry — Inorganic Chemistry

Evolutionary Multiobjective Optimization of Multiligand Metal Complexes in Diverse and Vast Chemical Spaces Transition metal complexes (TMCs) play a key role in several areas of high interest, including medicinal chemistry, renewable energies, and nanoporous materials. The development of new TMCs enabling these technologies remains challenged by the need of optimizing multiple properties within large chemical spaces, in which the thirty transition metals of the periodic table can be combined with a virtually infinite number of ligands. In this work, we provide an open dataset, tmQMg-L, including a collection of 30K TMC ligands from the Cambridge Structural Database. tmQMg-L combines size, diversity, and synthesizability at an unprecedented scale. Each ligand is characterized by geometric information and a rich fingerprint including electronic and steric features. The ligand charge and metal-coordination mode were also assigned with a robust algorithm based on graphs and natural bond orbital theory. The tmQMg-L dataset was leveraged in the automated generation of 1.37M TMCs resulting from all possible combinations between a square planar palladium(II) scaffold and a pool of 50 different ligands. This TMC space was explored with a multiobjective genetic algorithm (MOGA) that optimized two properties over a Pareto front; namely the polarizability (alpha) and the HOMO-LUMO gap (epsilon). After exploring only 1% of the whole space (i.e. 13k TMCs), the MOGA yielded 130 diverse hits with maximal alpha and epsilon values. Despite the size of the space explored, the evolution of the hits was easily rationalized by analyzing how the MOGA picked ligands of different natures. Instead of the traditional mutation and crossover of fragments within a single ligand, the MOGA of this work implemented full-ligand genetic operations acting on all coordination sites, enforcing chemical diversity across all populations, including the last one containing the hits. We believe that the combined use of the tmQMg-L dataset with this MOGA algorithm will enable the discovery of TMCs with optimal properties within diverse and vast chemical spaces. Polarizability ; Ligand (biochemistry) ; Computer science ; Nanoporous ; Transition metal ; Periodic table ; Chemistry ; Materials science ; Molecule ; Nanotechnology ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Daniel Wigh | Joe Arrowsmith | Alexander Pomberger | Kobi Felton | Alexei A. Lapkin
Source: ChemRxiv (preprint, 2023-08-30)
Field: Materials Science — Materials Chemistry

ORDerly: Datasets and benchmarks for chemical reaction data Machine learning has the potential to provide tremendous value to the life sciences by providing models that aid in the discovery of new molecules and reduce the time for new products to come to market. Chemical reactions play a significant role in these fields, but there is a lack of high-quality open-source chemical reaction datasets for training ML models. Herein, we present ORDerly, an open-source Python package for customizable and reproducible preparation of reaction data stored in accordance with the increasingly popular Open Reaction Database (ORD) schema. We use ORDerly to clean US patent data stored in ORD and generate datasets for forward prediction, retrosynthesis, as well as the first benchmark for reaction condition prediction. We train neural networks on datasets generated with ORDerly for condition prediction and show that datasets missing key cleaning steps can lead to silently overinflated performance metrics. Additionally, we train transformers for forward and retrosynthesis prediction and demonstrate how non-patent data can be used to evaluate model generalisation. By providing a customizable open-source solution for cleaning and preparing large chemical reaction data, ORDerly is poised to push forward the boundaries of machine learning applications in chemistry. Retrosynthetic analysis ; Computer science ; Python (programming language) ; Merge (version control) ; Open source ; Cheminformatics ; Machine learning ; Benchmark (surveying) ; Data mining ; Artificial intelligence ; Software ; Information retrieval ; Chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Lung-Yi Chen | Yi‐Pei Li
Source: ChemRxiv (preprint, 2023-06-16)
Field: Materials Science — Materials Chemistry

Recommending Multiple Reaction Conditions UsingTwo-Stage Deep Neural Networks The temperatures, prices of reagent and solvent and the yields influence the feasibility of the synthetic pathway. Therefore, predicting reaction condition is an important topic when designing a profitable synthetic pathway. For a single-step reaction, there are sometimes more than one suitable reaction contexts. Different reaction conditions result in different reaction rate and selectivity, and the design should comply with the requirement of the chemists. Providing diverse alternatives could help design the more economic synthesis pathway. However, recent literature has only tried to predict one best reaction condition. To improve this situation, we construct a twostage listwise ranking model to recommend multiple reaction conditions, and the ranking metrics are based on the yield level. The model is trained on the dataset consisting of ten representative types of reaction exported from Reaxys, and it recommends the reaction conditions with the top- 20 mean average precision (MAP) equal to 0.2723. The MAE of the temperature prediction is 11.1 °C. Besides, we used t-SNE to reduce the dimensionality of the embeddings and found that the model implicitly learns the pattern of reaction classification when predicting the reaction conditions. Ranking (information retrieval) ; Computer science ; Artificial neural network ; Yield (engineering) ; Reaction conditions ; Curse of dimensionality ; Construct (python library) ; Artificial intelligence ; Chemistry ; Catalysis ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Geoffrey A. Ozin
Source: Matter (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Homo deus: I am not a robot AI materials chemist Chemist ; Experiential learning ; Complement (music) ; Engineering ethics ; Artificial intelligence ; Computer science ; Engineering ; Psychology ; Chemistry ; Mathematics education Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: N. Ian Rinehart | Rakesh K. Saunthwal | Joël Wellauer | Andrew F. Zahrt | Lukas Schlemper | Alexander S. Shved | Raphael Bigler | Serena Fantasia | Scott E. Denmark
Source: ChemRxiv (preprint, 2023-02-13)
Field: Chemistry — Organic Chemistry

Development and Validation of a Chemoinformatic Workflow for Predicting Reaction Yield for Pd-Catalyzed C-N Couplings with Substrate Generalizability A machine learning-based tool that provides conditions and predicted yields for Buchwald-Hartwig couplings from a ChemDraw™ structure input is described. The tool is built on an in-house generated experimental dataset that explores a diverse network of reactant pairings. To minimize the number of experiments necessary to produce models and maximize data value, a workflow based on unsupervised machine leaning tools was created. The workflow enables the construction of models which can successfully generalize—making predictions for reactants which are not represented in the dataset. Workflow ; Generalizability theory ; Yield (engineering) ; Computer science ; Substrate (aquarium) ; Value (mathematics) ; Artificial intelligence ; Machine learning ; Data mining ; Database ; Mathematics ; Thermodynamics ; Physics ; Biology Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Miruna T. Cretu | Alessandra Toniato | Amol Thakkar | Amin Debabeche | Teodoro Laino | Alain C. Vaucher
Source: ChemRxiv (preprint, 2023-03-09)
Field: Materials Science — Materials Chemistry

Standardizing chemical compounds with language models With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds accurately and consistently. Harmonized representations facilitate the extraction of insightful information from datasets, and are advantageous for machine learning applications. To achieve consistent representations throughout datasets, one relies on molecule standardization, which is typically accomplished using rule-based algorithms that modify descriptions of functional groups. Here, we present the first deep-learning model for molecular standardization. We enable custom standardization schemes based solely on data, which, as additional benefit, support standardization options that are difficult to encode into rules. Our model achieves over 98% accuracy in learning two popular rule-based standardization protocols. We then follow a transfer learning approach to standardize metal-organic compounds (for which there is currently no automated standardization practice), based on a human-curated dataset of 1512 compounds. This model predicts the expected standardized molecular format with a test accuracy of 75.6%. As standardization can be considered, more broadly, a transformation from undesired to desired representations of compounds, the same data-driven architecture can be applied to other tasks. For instance, we demonstrate the application to compound canonicalization and to the determination of major tautomers in solution, based on computed and experimental data. Standardization ; Computer science ; Artificial intelligence ; ENCODE ; Machine learning ; Data mining ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Mani Valleti | Rama k. Vasudevan | Maxim Ziatdinov | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2023-03-25)
Field: Materials Science — Materials Chemistry

Deep Kernel Methods Learn Better: From Cards to Process Optimization The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards data set and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The jupyter notebooks that encapsulate the complete analysis accompany the article. Autoencoder ; Artificial intelligence ; Computer science ; Deep learning ; Kernel (algebra) ; Representation (politics) ; Feature learning ; Space (punctuation) ; Domain (mathematical analysis) ; Machine learning ; Process (computing) ; Simple (philosophy) ; Set (abstract data type) ; Optimization problem ; Manifold (fluid mechanics) ; Algorithm ; Mathematics ; Engineering Machine Learning in Materials Science ; Neural Networks and Applications ; Model Reduction and Neural Networks
Authors: Sarah I. Allec | Eric S. Muckley | Nathan S. Johnson | Borg, Christopher K. H. | James E. Saal | Martin, Joshua | Pant, Rohit | Takeuchi, Ichiro | Lee, Andrew S. | Saal, James E. | Ward, Logan | Mehta, Apurva
Source: arXiv (Cornell University) (preprint, 2023-11-16)
Field: Materials Science — Materials Chemistry

A case study of multi-modal, multi-institutional data management for the combinatorial materials science community Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multi-modal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing i) synthesis and processing conditions, ii) characterization results, and iii) property and performance measurements. Here, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure. Workflow ; Computer science ; Data science ; Data management ; Metadata ; Timeline ; Collaboratory ; Materials informatics ; Automation ; Standardization ; Data mining ; Database ; Engineering ; World Wide Web Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Vinod G. Landge | Ankita Mishra | Waruna Thotamune | Audrey L. Bonds | Indunil Alahakoon | Ajith Karunarathne | Michael C. Young
Source: ChemRxiv (preprint, 2023-07-24)
Field: Chemistry — Organic Chemistry

Selective C–H Activation of Unprotected Allylamines by Control of Catalyst Speciation An outstanding challenge in the Pd-catalyzed functionalization of allylamines is the control of stereochemistry. Terminal alkenes preferentially undergo Heck-type reactions, while internal alkenes may undergo a mixture of Heck and C–H activation reactions that give mixtures of stereochemical products. In the case of unprotected allylamines, the challenge in achieving C–H activation is that facile in situ formation of Pd nanoparticles leads to preferential formation of trans rather than cis-substituted products. In this study we have demonstrated the feasibility of using mono-protected amino acid (MPAA) ligands as metal protecting groups to prevent aggregation and reduction, allowing the selective synthesis of free cis-arylated allylamines. This method compliments Heck-selective methods, allowing complete stereochemical control over the synthesis of cinnamylamines, an important class of amine that can serve as therapeutics directly or as advanced intermediates. To highlight the utility of the methodology, we have demonstrated rapid access to mu opioid receptor ligands. Chemistry ; Catalysis ; Heck reaction ; Amine gas treating ; Combinatorial chemistry ; Palladium ; Stereochemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Chemical Synthesis and Analysis ; Catalytic Cross-Coupling Reactions
Authors: Riccardo Zuliani | Efe C. Balta | John Lygeros
Source: arXiv (Cornell University) (preprint, 2023-12-24)
Field: Materials Science — Materials Chemistry

BP-MPC: Optimizing the Closed-Loop Performance of MPC using BackPropagation Model predictive control (MPC) is pervasive in research and industry. However, designing the cost function and the constraints of the MPC to maximize closed-loop performance remains an open problem. To achieve optimal tuning, we propose a backpropagation scheme that solves a policy optimization problem with nonlinear system dynamics and MPC policies. We enforce the system dynamics using linearization and allow the MPC problem to contain elements that depend on the current system state and on past MPC solutions. Moreover, we propose a simple extension that can deal with losses of feasibility. Our approach, unlike other methods in the literature, enjoys convergence guarantees. Backpropagation ; Model predictive control ; Closed loop ; Control theory (sociology) ; Loop (graph theory) ; Computer science ; Control engineering ; Artificial neural network ; Artificial intelligence ; Control (management) ; Engineering ; Mathematics Catalytic Processes in Materials Science ; Advanced Control Systems Optimization