인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
We propose that word embeddings of atoms derived from scientific literature are revisited as autonomous machine learning predictors in materials design. If static word embeddings encode comprehensive physicochemical information, joined embeddings of chemical elements constituting a chemical compound represent a viable source of physicochemical knowledge. Nevertheless, static word embeddings are susceptible to variability due to information heterogeneity within training material. We analysed whether variability occurs in embeddings affiliated with physicochemical entities, including explicit atoms, and whether it affects therein-encoded domain-specialized information or inhibits the information transfer. Results demonstrate the substantial variability in individual atomic embeddings, which is highly dependent on vocabulary terms selected for language modelling. Regardless, variability does not obstruct the mapping of materials' composite predictors into physicochemical properties when joined atomic embeddings are implemented within a regression model estimating the compound stability by predicting its formation energy. Moreover, the encoded information and the model's predictive performance maintained stability following compound vector calibration via dimensional reduction.Scientific contributionThe magnitude of variability in word embeddings of physicochemical entities, including chemical elements, occurring due to information heterogeneity in complementary training material of materials science, chemistry, and physics scientific literature was observed and quantified. The research shows that notable variability of vectorial representations of chemical elements does not obstruct the underlying statistical properties, nor does it inhibit the information transfer. Accordingly, regardless of their origin, conjoined atomic embeddings representing chemical compounds facilitate stable predictive performance when implemented within a regression model.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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