Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties.

High-throughput computational materials design is an emerging area of materials science. By combining advanced thermodynamic and electronic-structure methods with intelligent data mining and database construction, and exploiting the power of current supercomputer architectures, scientists generate, manage and analyse enormous data repositories for the discovery of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and opportunities that lie ahead.

A method that combines thin film deposition and physical masking techniques has been used for the parallel synthesis of spatially addressable libraries of solid-state materials. Arrays containing different combinations, stoichiometries, and deposition sequences of BaCO(3), Bi(2)O(3), CaO, CuO, PbO, SrCO(3), and Y(2)O(3) were generated with a series of binary masks. The arrays were sintered and BiSrCaCuO and YBaCuO superconducting films were identified. Samples as small as 200 micrometers by 200 micrometers in size were generated, corresponding to library densities of 10,000 sites per square inch. The ability to generate and screen combinatorial libraries of solid-state compounds, when coupled with theory and empirical observations, may significantly increase the rate at which novel electronic, magnetic, and optical materials are discovered and theoretical predictions tested.

Combinatorial methods offer an efficient approach for the development of new materials. Methods for generating combinatorial samples of materials, and methods for characterizing local composition and structure by electron microprobe analysis and electron-backscatter diffraction are relatively well developed. But a key component for combinatorial studies of materials is high-spatial-resolution measurements of the property of interest, for example, the magnetic, optical, electrical, mechanical or thermal properties of each phase, composition or processing condition. Advances in the experimental methods used for mapping these properties will have a significant impact on materials science and engineering. Here we show how time-domain thermoreflectance can be used to image the thermal conductivity of the cross-section of a Nb-Ti-Cr-Si diffusion multiple, and thereby demonstrate rapid and quantitative measurements of thermal transport properties for combinatorial studies of materials. The lateral spatial resolution of the technique is 3.4 microm, and the time required to measure a 100 x 100 pixel image is approximately 1 h. The thermal conductivity of TiCr(2) decreases by a factor of two in crossing from the near-stoichiometric side of the phase to the Ti-rich side; and the conductivity of (Ti,Nb)(3)Si shows a strong dependence on crystalline orientation.

Materials genome engineering (MGE) is a frontier technology in the field of material science and engineering, which is well capable to revolutionize the research and development (R&D) mode of new materials, greatly improve the R&D efficiency, shorten the R&D time, and reduce the cost. This paper reviews the progress of MGE in China from the aspects of the fundamental theory and methods, key technology and equipment, the R&D of new materials and related engineering application, talents training, formation and promotion of new concept of material genetic engineering. The paper also looks forward to the future development of MGE in China.

材料基因工程是材料领域的颠覆性前沿技术，将对材料研发模式产生革命性的变革，全面加速材料从设计到工程化应用的进程，大幅度提升新材料的研发效率，缩短研发周期，降低研发成本，促进工程化应用。本文从基础理论与方法、关键技术与装备、新材料研发与工程化应用、人才培养以及材料基因工程新理念的形成和推广等方面，综述了中国材料基因工程的研究进展，并提出了未来发展方向建议。

The rapid advancement of big data and artificial intelligence has resulted in new data-driven materials research and development (R&D), which has achieved substantial progress. This fourth paradigm is believed to improve materials design efficiency and industrialized application and stimulate the discovery of new materials. The focus of this work is on the emerging field of machine learning-assisted material R&D, with an emphasis on machine learning predictions and optimization design. Following a brief description of feature construction and selection, recent developments in material predictions on phases/structures, processing-structure-property relationships, microstructure, and material performance are reviewed. This paper also summarizes the research progress on optimization algorithms with machine learning models, which is expected to overcome the bottlenecks such as the small size and high noise level of material data samples and huge space for exploration. The challenges and future opportunities for machine learning applications in materials R&D are discussed and prospected.

大数据和人工智能技术的快速发展推动数据驱动的材料研发快速发展成为变革传统试错法的新模式，即所谓的材料研发第四范式。新模式将大幅度提升材料研发效率和工程化应用水平，推动新材料快速发展。本文聚焦机器学习辅助材料研发这一新兴领域，以材料预测和优化设计为主线，在简述材料特征构建与筛选的基础上，综述了机器学习在材料相结构、显微组织、成分-工艺-性能、服役行为预测等方面的研究进展；针对材料数据样本量少、噪音高、质量差，以及新材料探索空间巨大的特点，综述了机器学习模型与优化算法和策略融合，在新材料优化设计中的研究进展和典型应用。最后，讨论了机器学习在材料领域的发展机遇和挑战，展望了发展前景。

There is growing interest in applying machine learning techniques in the field of materials science. However, the interpretation and knowledge extracted from machine learning models is a major concern, particularly as formulating an explicit model that provides insight into physics is the goal of learning. In the present study, we propose a framework that utilizes the filtering ability of feature engineering, in conjunction with symbolic regression to extract explicit, quantitative expressions for the band gap energy from materials data. We propose enhancements to genetic programming with dimensional consistency and artificial constraints to improve the search efficiency of symbolic regression. We show how two descriptors attributed to volumetric and electronic factors, from 32 possible candidates, explicitly express the band gap energy of NaCl-type compounds. Our approach provides a basis to capture underlying physical relationships between materials descriptors and target properties.

For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. We propose a principle for the identification of nontriviality. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the "alphabet" used to describe those systems.

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, CsLaMnCoO, CsLaNiO, SrNiCoO, and SrBaNiO, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.

The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on factors from kinetics, to advances in synthesis techniques, to the availability of precursors. This complexity makes the development of a general theory or first-principles approach to synthesizability currently impractical. Here we show how an alternative pathway to predicting synthesizability emerges from the dynamics of the materials stability network: a scale-free network constructed by combining the convex free-energy surface of inorganic materials computed by high-throughput density functional theory and their experimental discovery timelines extracted from citations. The time-evolution of the underlying network properties allows us to use machine-learning to predict the likelihood that hypothetical, computer-generated materials will be amenable to successful experimental synthesis.

Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to black box models, rather than creating models that are in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward - it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.

We introduce physics-informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. In this work, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct types of algorithms, namely continuous time and discrete time models. The first type of models forms a new family of data-efficient spatio-temporal function approximators, while the latter type allows the use of arbitrarily accurate implicit Runge-Kutta time stepping schemes with unlimited number of stages. The effectiveness of the proposed framework is demonstrated through a collection of classical problems in fluids, quantum mechanics, reaction-diffusion systems, and the propagation of nonlinear shallow-water waves. (C) 2018 Elsevier Inc.

The rapidly developing field of physics-informed learning integrates data and mathematical models seamlessly, enabling accurate inference of realistic and high-dimensional multiphysics problems. This Review discusses the methodology and provides diverse examples and an outlook for further developments.

The chemical space for designing materials is practically infinite. This makes disruptive progress by traditional physics-based modeling alone challenging. Yet, training data for identifying composition-structure-property relations by artificial intelligence are sparse. We discuss opportunities to discover new chemically complex materials by hybrid methods where physics laws are combined with artificial intelligence.© 2023. Springer Nature America, Inc.

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a "window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.

Classification and feature selection of genomics or transcriptomics data is often hampered by the large number of features as compared with the small number of samples available. Moreover, features represented by probes that either have similar molecular functions (gene expression analysis) or genomic locations (DNA copy number analysis) are highly correlated. Classical model selection methods such as penalized logistic regression or random forest become unstable in the presence of high feature correlations. Sophisticated penalties such as group Lasso or fused Lasso can force the models to assign similar weights to correlated features and thus improve model stability and interpretability. In this article, we show that the measures of feature relevance corresponding to the above-mentioned methods are biased such that the weights of the features belonging to groups of correlated features decrease as the sizes of the groups increase, which leads to incorrect model interpretation and misleading feature ranking.With simulation experiments, we demonstrate that Lasso logistic regression, fused support vector machine, group Lasso and random forest models suffer from correlation bias. Using simulations, we show that two related methods for group selection based on feature clustering can be used for correcting the correlation bias. These techniques also improve the stability and the accuracy of the baseline models. We apply all methods investigated to a breast cancer and a bladder cancer arrayCGH dataset and in order to identify copy number aberrations predictive of tumor phenotype.R code can be found at: http://www.mpi-inf.mpg.de/~laura/Clustering.r.

Local feature attribution methods are increasingly used to explain complex machine learning models. However, current methods are limited because they are extremely expensive to compute or are not capable of explaining a distributed series of models where each model is owned by a separate institution. The latter is particularly important because it often arises in finance where explanations are mandated. Here, we present Generalized DeepSHAP (G-DeepSHAP), a tractable method to propagate local feature attributions through complex series of models based on a connection to the Shapley value. We evaluate G-DeepSHAP across biological, health, and financial datasets to show that it provides equally salient explanations an order of magnitude faster than existing model-agnostic attribution techniques and demonstrate its use in an important distributed series of models setting.© 2022. The Author(s).

Development of deep-learning models for intermolecular noncovalent (NC) interactions between proteins and ligands has great potential in the chemical and pharmaceutical tasks, including structure-activity relationship and drug design. It still remains an open question how to convert the three-dimensional, structural information of a protein-ligand complex into a graph representation in the graph neural networks (GNNs). It is also difficult to know whether a trained GNN model learns the NC interactions properly. Herein, we propose a GNN architecture that learns two distinct graphs-one for the intramolecular covalent bonds in a protein and a ligand, and the other for the intermolecular NC interactions between the protein and the ligand-separately by the corresponding covalent and NC convolutional layers. The graph separation has some advantages, such as independent evaluation on the contribution of each convolutional step to the prediction of dissociation constants, and facile analysis of graph-building strategies for the NC interactions. In addition to its prediction performance that is comparable to that of a state-of-the art model, the analysis with an explainability strategy of layer-wise relevance propagation shows that our model successfully predicts the important characteristics of the NC interactions, especially in the aspect of hydrogen bonding, in the chemical interpretation of protein-ligand binding.

The extensive use of carbon nanomaterials such as carbon nanotubes/nanofibers (CNTs/CNFs) in industrial settings has raised concerns over the potential health risks associated with occupational exposure to these materials. These exposures are commonly in the form of CNT/CNF-containing aerosols, resulting in a need for a reliable structure classification protocol to perform meaningful exposure assessments. However, airborne carbonaceous nanomaterials are very likely to form mixtures of individual nano-sized particles and micron-sized agglomerates with complex structures and irregular shapes, making structure identification and classification extremely difficult. While manual classification from transmission electron microscopy (TEM) images is widely used, it is time-consuming due to the lack of automation tools for structure identification. In the present study, we applied a convolutional neural network (CNN) based machine learning and computer vision method to recognize and classify airborne CNT/CNF particles from TEM images. We introduced a transfer learning approach to represent images by hypercolumn vectors, which were clustered -means and processed into a Vector of Locally Aggregated Descriptors (VLAD) representation to train a softmax classifier with the gradient boosting algorithm. This method achieved 90.9% accuracy on the classification of a 4-class dataset and 84.5% accuracy on a more complex 8-class dataset. The developed model established a framework to automatically detect and classify complex carbon nanostructures with potential applications that extend to the automated structural classification for other nanomaterials.This journal is © The Royal Society of Chemistry.

材料研发模式经历了经验主导的第一范式、理论模型主导的第二范式和计算模拟主导的第三范式，如今正处于数据驱动的第四范式。为加速新材料的设计与研发，发展基于材料数据库和人工智能算法的高通量自动集成计算和数据挖掘算法变得至关重要。本文介绍了作者团队自主开发的分布式高通量自动流程集成计算和数据管理智能平台ALKEMIE2.0 (Artificial Learning and Knowledge Enhanced Materials Informatics Engineering 2.0)，该平台基于AMDIV设计理念，包含了自动化、模块化、数据库、人工智能和可视化流程等5个适用于数据驱动的材料研发模式核心要素。概括来说，ALKEMIE2.0以模块化的方式集成了多个不同尺度的计算模拟软件；其高通量自动纠错流程可实现从建模、运行到数据分析，全程自动无人工干预；支持单用户不低于10⁴量级的并发高通量自动计算模拟。进一步而言，ALKEMIE2.0具有强大的可移植性和可扩展性，目前已部署在国家超算天津中心，基于多类型材料数据库结合超算强大的计算能力使得人工智能算法在新材料设计与研发中得以快速的应用和实践。更重要的是，ALKEMIE2.0设计了用户友好的可视化操作界面，使得结构建模、工作流计算逻辑、数据分析和机器学习模型具有更高的透明性和更强的可操作性，且适用于对材料计算模拟掌握程度从初级到专业的所有材料研究人员。最后，通过多平台部署和高通量筛选二元铝合金2个算例详细展示了ALKEMIE2.0的主要特色及功能。